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  Subjects -> BUSINESS AND ECONOMICS (Total: 3075 journals)
    - ACCOUNTING (89 journals)
    - BANKING AND FINANCE (261 journals)
    - BUSINESS AND ECONOMICS (1154 journals)
    - CONSUMER EDUCATION AND PROTECTION (24 journals)
    - COOPERATIVES (4 journals)
    - ECONOMIC SCIENCES: GENERAL (158 journals)
    - ECONOMIC SYSTEMS, THEORIES AND HISTORY (167 journals)
    - FASHION AND CONSUMER TRENDS (13 journals)
    - HUMAN RESOURCES (94 journals)
    - INSURANCE (23 journals)
    - INTERNATIONAL COMMERCE (127 journals)
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    - PRODUCTION OF GOODS AND SERVICES (138 journals)
    - PUBLIC FINANCE, TAXATION (32 journals)
    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals)

BUSINESS AND ECONOMICS (1154 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 1566 Journals sorted alphabetically
4OR: A Quarterly Journal of Operations Research     Hybrid Journal   (Followers: 9)
Abacus     Hybrid Journal   (Followers: 11)
Accounting Forum     Hybrid Journal   (Followers: 22)
Acta Amazonica     Open Access   (Followers: 3)
Acta Commercii     Open Access   (Followers: 2)
Acta Oeconomica     Full-text available via subscription   (Followers: 2)
Acta Scientiarum. Human and Social Sciences     Open Access   (Followers: 4)
Acta Universitatis Danubius. Œconomica     Open Access  
Acta Universitatis Nicolai Copernici Zarządzanie     Open Access   (Followers: 3)
AD-minister     Open Access   (Followers: 2)
ADR Bulletin     Open Access   (Followers: 5)
Advances in Developing Human Resources     Hybrid Journal   (Followers: 21)
Advances in Economics and Business     Open Access   (Followers: 12)
AfricaGrowth Agenda     Full-text available via subscription   (Followers: 1)
African Affairs     Hybrid Journal   (Followers: 57)
African Development Review     Hybrid Journal   (Followers: 34)
African Journal of Business and Economic Research     Full-text available via subscription   (Followers: 1)
African Journal of Business Ethics     Open Access   (Followers: 7)
African Review of Economics and Finance     Open Access   (Followers: 3)
Afro-Asian Journal of Finance and Accounting     Hybrid Journal   (Followers: 6)
Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi     Open Access   (Followers: 3)
Agronomy     Open Access   (Followers: 11)
Akademika : Journal of Southeast Asia Social Sciences and Humanities     Open Access   (Followers: 4)
Alphanumeric Journal : The Journal of Operations Research, Statistics, Econometrics and Management Information Systems     Open Access   (Followers: 4)
American Economic Journal : Applied Economics     Full-text available via subscription   (Followers: 125)
American Economic Journal : Economic Policy     Full-text available via subscription   (Followers: 94)
American Journal of Business     Hybrid Journal   (Followers: 14)
American Journal of Business and Management     Open Access   (Followers: 50)
American Journal of Business Education     Open Access   (Followers: 10)
American Journal of Economics and Business Administration     Open Access   (Followers: 22)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 27)
American Journal of Evaluation     Hybrid Journal   (Followers: 12)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 16)
American Journal of Health Economics     Full-text available via subscription   (Followers: 12)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
American Journal of Medical Quality     Hybrid Journal   (Followers: 7)
American Law and Economics Review     Hybrid Journal   (Followers: 26)
ANALES de la Universidad Central del Ecuador     Open Access   (Followers: 1)
Annales de l'Institut Henri Poincare (C) Non Linear Analysis     Full-text available via subscription   (Followers: 1)
Annals in Social Responsibility     Full-text available via subscription  
Annals of Finance     Hybrid Journal   (Followers: 26)
Annals of Operations Research     Hybrid Journal   (Followers: 8)
Annual Review of Economics     Full-text available via subscription   (Followers: 29)
Applied Developmental Science     Hybrid Journal   (Followers: 4)
Applied Economics     Hybrid Journal   (Followers: 44)
Applied Economics Letters     Hybrid Journal   (Followers: 28)
Applied Economics Quarterly     Full-text available via subscription   (Followers: 10)
Applied Financial Economics     Hybrid Journal   (Followers: 21)
Applied Mathematical Finance     Hybrid Journal   (Followers: 6)
Applied Stochastic Models in Business and Industry     Hybrid Journal   (Followers: 5)
Apuntes Universitarios     Open Access   (Followers: 1)
Arab Economic and Business Journal     Open Access   (Followers: 3)
Archives of Business Research     Open Access   (Followers: 4)
Arena Journal     Full-text available via subscription   (Followers: 1)
Argomenti. Rivista di economia, cultura e ricerca sociale     Open Access   (Followers: 2)
ASEAN Economic Bulletin     Full-text available via subscription   (Followers: 5)
Asia Pacific Business Review     Hybrid Journal   (Followers: 5)
Asia Pacific Journal of Human Resources     Hybrid Journal   (Followers: 310)
Asia Pacific Viewpoint     Hybrid Journal  
Asia-Pacific Journal of Business Administration     Hybrid Journal   (Followers: 3)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Business Review     Open Access   (Followers: 2)
Asian Case Research Journal     Hybrid Journal   (Followers: 1)
Asian Development Review     Open Access   (Followers: 14)
Asian Economic Journal     Hybrid Journal   (Followers: 6)
Asian Economic Papers     Hybrid Journal   (Followers: 7)
Asian Economic Policy Review     Hybrid Journal   (Followers: 3)
Asian Journal of Accounting and Governance     Open Access   (Followers: 3)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 7)
Asian Journal of Social Sciences and Management Studies     Open Access   (Followers: 6)
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 9)
Asian-pacific Economic Literature     Hybrid Journal   (Followers: 5)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5)
Atlantic Economic Journal     Hybrid Journal   (Followers: 14)
Australasian Journal of Regional Studies, The     Full-text available via subscription   (Followers: 2)
Australian Cottongrower, The     Full-text available via subscription   (Followers: 1)
Australian Economic Papers     Hybrid Journal   (Followers: 16)
Australian Economic Review     Hybrid Journal   (Followers: 7)
Australian Journal of Maritime and Ocean Affairs     Hybrid Journal   (Followers: 11)
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Baltic Journal of Real Estate Economics and Construction Management     Open Access   (Followers: 1)
Banks in Insurance Report     Hybrid Journal   (Followers: 1)
BBR - Brazilian Business Review     Open Access   (Followers: 5)
Benchmarking : An International Journal     Hybrid Journal   (Followers: 11)
BER : Consumer Confidence Survey     Full-text available via subscription   (Followers: 4)
BER : Economic Prospects : An Executive Summary     Full-text available via subscription  
BER : Economic Prospects : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Intermediate Goods Industries Survey     Full-text available via subscription   (Followers: 1)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Building and Construction : An Executive Summary     Full-text available via subscription   (Followers: 4)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Trends : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Wholesale Sector Survey     Full-text available via subscription   (Followers: 1)
Berkeley Business Law Journal     Free   (Followers: 11)
Bio-based and Applied Economics     Open Access   (Followers: 1)
Biodegradation     Hybrid Journal   (Followers: 1)
Biology Direct     Open Access   (Followers: 7)
Black Enterprise     Full-text available via subscription  
Board & Administrator for Administrators only     Hybrid Journal  
Border Crossing : Transnational Working Papers     Open Access   (Followers: 2)
Briefings in Real Estate Finance     Hybrid Journal   (Followers: 5)
British Journal of Industrial Relations     Hybrid Journal   (Followers: 30)
Brookings Papers on Economic Activity     Open Access   (Followers: 46)
Brookings Trade Forum     Full-text available via subscription   (Followers: 3)
BRQ Business Research Quarterly     Open Access   (Followers: 2)
Building Sustainable Legacies : The New Frontier Of Societal Value Co-Creation     Full-text available via subscription   (Followers: 1)
Bulletin of Economic Research     Hybrid Journal   (Followers: 16)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 6)
Bulletin of Indonesian Economic Studies     Hybrid Journal   (Followers: 3)
Bulletin of the Dnipropetrovsk University. Series : Management of Innovations     Open Access   (Followers: 1)
Business & Entrepreneurship Journal     Open Access   (Followers: 15)
Business & Information Systems Engineering     Hybrid Journal   (Followers: 5)
Business & Society     Hybrid Journal   (Followers: 9)
Business : Theory and Practice / Verslas : Teorija ir Praktika     Open Access   (Followers: 1)
Business and Economic Horizons     Open Access   (Followers: 2)
Business and Economic Research     Open Access   (Followers: 5)
Business and Management Horizons     Open Access   (Followers: 11)
Business and Management Research     Open Access   (Followers: 16)
Business and Management Studies     Open Access   (Followers: 7)
Business and Politics     Hybrid Journal   (Followers: 6)
Business and Professional Communication Quarterly     Hybrid Journal   (Followers: 7)
Business and Society Review     Hybrid Journal   (Followers: 5)
Business Economics     Hybrid Journal   (Followers: 6)
Business Ethics: A European Review     Hybrid Journal   (Followers: 16)
Business Horizons     Hybrid Journal   (Followers: 9)
Business Information Review     Hybrid Journal   (Followers: 13)
Business Management and Strategy     Open Access   (Followers: 39)
Business Research     Hybrid Journal   (Followers: 1)
Business Strategy and the Environment     Hybrid Journal   (Followers: 11)
Business Strategy Review     Hybrid Journal   (Followers: 6)
Business Strategy Series     Hybrid Journal   (Followers: 5)
Business Systems & Economics     Open Access   (Followers: 1)
Business Systems Research Journal     Open Access   (Followers: 4)
Business, Management and Education     Open Access   (Followers: 16)
Business, Peace and Sustainable Development     Full-text available via subscription   (Followers: 3)
Bustan     Hybrid Journal   (Followers: 1)
Cadernos EBAPE.BR     Open Access   (Followers: 1)
Cambridge Journal of Economics     Hybrid Journal   (Followers: 55)
Cambridge Journal of Regions, Economy and Society     Hybrid Journal   (Followers: 9)
Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration     Hybrid Journal   (Followers: 1)
Canadian Journal of Economics/Revue Canadienne d`Economique     Hybrid Journal   (Followers: 25)
Canadian journal of nonprofit and social economy research     Open Access   (Followers: 2)
Capitalism and Society     Hybrid Journal   (Followers: 2)
Capitalism Nature Socialism     Hybrid Journal   (Followers: 11)
Case Studies in Business and Management     Open Access   (Followers: 8)
CBU International Conference Proceedings     Open Access   (Followers: 1)
Central European Business Review     Open Access   (Followers: 1)
Central European Journal of Operations Research     Hybrid Journal   (Followers: 5)
Central European Journal of Public Policy     Open Access   (Followers: 1)
CESifo Economic Studies     Hybrid Journal   (Followers: 15)
Chain Reaction     Full-text available via subscription   (Followers: 1)
Challenge     Full-text available via subscription   (Followers: 4)
China & World Economy     Hybrid Journal   (Followers: 15)
China : An International Journal     Full-text available via subscription   (Followers: 17)
China Economic Journal: The Official Journal of the China Center for Economic Research (CCER) at Peking University     Hybrid Journal   (Followers: 10)
China Economic Review     Hybrid Journal   (Followers: 10)
China Finance Review International     Hybrid Journal   (Followers: 5)
China Nonprofit Review     Hybrid Journal   (Followers: 3)
China perspectives     Open Access   (Followers: 11)
Chinese Economy     Full-text available via subscription  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
CLIO América     Open Access   (Followers: 1)
Cliometrica     Hybrid Journal   (Followers: 1)
COEPTUM     Open Access  
Community Development Journal     Hybrid Journal   (Followers: 23)
Compensation & Benefits Review     Hybrid Journal   (Followers: 6)
Competition & Change     Hybrid Journal   (Followers: 10)
Competitive Intelligence Review     Hybrid Journal   (Followers: 2)
Competitiveness Review : An International Business Journal incorporating Journal of Global Competitiveness     Hybrid Journal   (Followers: 5)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computer Law & Security Report     Hybrid Journal   (Followers: 15)
Computers & Operations Research     Hybrid Journal   (Followers: 10)
Construction Innovation: Information, Process, Management     Hybrid Journal   (Followers: 14)
Contemporary Wales     Full-text available via subscription   (Followers: 3)
Contextus - Revista Contemporânea de Economia e Gestão     Open Access   (Followers: 1)
Contributions to Political Economy     Hybrid Journal   (Followers: 6)
Corporate Communications An International Journal     Hybrid Journal   (Followers: 4)
Corporate Philanthropy Report     Hybrid Journal   (Followers: 2)
Corporate Reputation Review     Hybrid Journal   (Followers: 5)
Creative and Knowledge Society     Open Access   (Followers: 10)
Creative Industries Journal     Hybrid Journal   (Followers: 8)
CRIS - Bulletin of the Centre for Research and Interdisciplinary Study     Open Access   (Followers: 1)
Crossing the Border : International Journal of Interdisciplinary Studies     Open Access   (Followers: 4)
Cuadernos de Administración (Universidad del Valle)     Open Access   (Followers: 1)
Cuadernos de Economía     Open Access   (Followers: 1)
Cuadernos de Economia - Latin American Journal of Economics     Open Access   (Followers: 1)
Cuadernos de Estudios Empresariales     Open Access   (Followers: 1)
Current Opinion in Creativity, Innovation and Entrepreneurship     Open Access   (Followers: 8)
De Economist     Hybrid Journal   (Followers: 12)
Decision Analysis     Full-text available via subscription   (Followers: 8)
Decision Sciences     Hybrid Journal   (Followers: 15)
Decision Support Systems     Hybrid Journal   (Followers: 15)
Defence and Peace Economics     Hybrid Journal   (Followers: 16)
der markt     Hybrid Journal   (Followers: 1)
Desenvolvimento em Questão     Open Access  

        1 2 3 4 5 6 | Last

Journal Cover Computers & Operations Research
  [SJR: 2.237]   [H-I: 104]   [10 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0305-0548
   Published by Elsevier Homepage  [3041 journals]
  • A strongly polynomial Contraction-Expansion algorithm for network flow
           problems
    • Abstract: Publication date: August 2017
      Source:Computers & Operations Research, Volume 84
      Author(s): Jean Bertrand Gauthier, Jacques Desrosiers, Marco E. Lübbecke
      This paper addresses the solution of the capacitated minimum cost flow problem on a network containing n nodes and m arcs. Satisfying necessary and sufficient optimality conditions can be done on the residual network although it can be quite time consuming as testified by the minimum mean cycle-canceling algorithm (MMCC). We introduce a contracted network which exploits these conditions on a much smaller network. Since the construction of this contracted network is very flexible, we study its properties depending on the construction choice. A generic contraction algorithm is then produced around the contracted network. Interestingly enough, it turns out it encapsulates both the MMCC and primal network simplex algorithms as extreme cases. By guiding the solution using a particular expansion scheme, we are able to recuperate theoretical results from MMCC. As such, we obtain a strongly polynomial Contraction-Expansion algorithm which runs in O(m 3 n 2) time. There is thus no improvement of the runtime complexity, yet the expansion scheme sticks to very practical observations of MMCC’s behavior, namely that of phases and jumps on the optimality parameter. The solution time is ultimately significantly reduced, even more so as the size of the instance increases.

      PubDate: 2017-03-20T12:25:55Z
       
  • Resource-constrained project scheduling with flexible resource profiles in
           continuous time
    • Abstract: Publication date: August 2017
      Source:Computers & Operations Research, Volume 84
      Author(s): Anulark Naber
      This paper addresses the resource-constrained project scheduling problem with flexible resource profiles (FRCPSP) in continuous time. In contrast to the discrete-time system, each task may start, end, or change its resource allocation at any point in time. The additional decisions for the continuous times of these events greatly amplify the problem complexity. We propose a mixed-integer linear programming model together with problem-specific inequalities and heuristic time limits, both of which are applied in the branch-and-cut procedure. In addition, the fractional period-width preprocessing and heuristic as well as the event estimation method are proposed to estimate the time and event parameters. Through the computational results, we investigate the pros and cons of the continuous-time model against the discrete-time counterpart both in terms of solution quality and runtimes, as well as the effectiveness of the preprocessing and different solution procedures.

      PubDate: 2017-03-20T12:25:55Z
       
  • Min-degree constrained minimum spanning tree problem with fixed centrals
           and terminals: Complexity, properties and formulations
    • Abstract: Publication date: August 2017
      Source:Computers & Operations Research, Volume 84
      Author(s): Fabio C.S. Dias, Manoel Campêlo, Críston Souza, Rafael Andrade
      We consider a variant of the Min-Degree Constrained Minimum Spanning Tree Problem where the central and terminal nodes are fixed a priori. We prove that the optimization problem is NP-Hard even for complete graphs and the feasibility problem is NP-Complete even if there is an edge between each central and each terminal in the input graph. Actually, this complexity result still holds when the minimum degree of each central node is restricted to be a same value d ≥ 2. We derive necessary and sufficient conditions for feasibility. We present several integer linear programming formulations – based on known formulations for the minimum spanning tree problem – along with a theoretical comparison among the lower bounds provided by their linear relaxations. We propose three Lagrangian heuristics. Computational experiments compare the performances of the heuristics and the formulations.

      PubDate: 2017-03-20T12:25:55Z
       
  • A low-space algorithm for the subset-sum problem on GPU
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): V.V. Curtis, C.A.A. Sanches
      We present a highly scalable parallel solution for the Subset-Sum Problem on Graphics Processing Units (GPUs) that substantially reduces the memory access by the device and, consequently, decreases the total runtime. We test this algorithm only on hard instances, which require the exhaustion of the entire search space, instead of simple random benchmarks. On a GPU with only 1.2 GB of global memory, we address hard instances with 100,000 items limited to 106 and 200 items limited to 108. Our algorithm achieves excellent runtimes outperforming the best-known practical and parallel algorithms, reaching speed-ups higher than 1000 in the best case compared to its sequential version.

      PubDate: 2017-03-20T12:25:55Z
       
  • Production planning in additive manufacturing and 3D printing
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Qiang Li, Ibrahim Kucukkoc, David Z. Zhang
      Additive manufacturing is a new and emerging technology and has been shown to be the future of manufacturing systems. Because of the high purchasing and processing costs of additive manufacturing machines, the planning and scheduling of parts to be processed on these machines play a vital role in reducing operational costs, providing service to customers with less price and increasing the profitability of companies which provide such services. However, this topic has not yet been studied in the literature, although cost functions have been developed to calculate the average production cost per volume of material for additive manufacturing machines. In an environment where there are machines with different specifications (i.e. production time and cost per volume of material, processing time per unit height, set-up time, maximum supported area and height, etc.) and parts in different heights, areas and volumes, allocation of parts to machines in different sets or groups to minimize the average production cost per volume of material constitutes an interesting and challenging research problem. This paper defines the problem for the first time in the literature and proposes a mathematical model to formulate it. The mathematical model is coded in CPLEX and two different heuristic procedures, namely ‘best-fit’ and ‘adapted best-fit’ rules, are developed in JavaScript. Solution-building mechanisms of the proposed heuristics are explained stepwise through examples. A numerical example is also given, for which an optimum solution and heuristic solutions are provided in detail, for illustration. Test problems are created and a comprehensive experimental study is conducted to test the performance of the heuristics. Experimental tests indicate that both heuristics provide promising results. The necessity of planning additive manufacturing machines in reducing processing costs is also verified.
      Graphical abstract image

      PubDate: 2017-03-15T14:03:24Z
       
  • On minimization of the number of branches in branch-and-bound algorithms
           for the maximum clique problem
    • Abstract: Publication date: August 2017
      Source:Computers & Operations Research, Volume 84
      Author(s): Chu-Min Li, Hua Jiang, Felip Manyà
      When searching for a maximum clique in a graph G, branch-and-bound algorithms in the literature usually focus on the minimization of the number of branches generated at each search tree node. We call dynamic strategy this minimization without any constraint, because it induces a dynamic vertex ordering in G during the search. In this paper, we introduce a static strategy that minimizes the number of branches subject to the constraint that a static vertex ordering in G must be kept during the search. We analyze the two strategies and show that they are complementary. From this complementarity, we propose a new algorithm, called MoMC, that combines the strengths of the two strategies into a single algorithm. The reported experimental results show that MoMC is generally better than the algorithms implementing a single strategy.

      PubDate: 2017-03-08T13:52:25Z
       
  • Multiobjective programming for sizing and locating biogas plants: A model
           and an application in a region of Portugal
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Sandra Silva, Luís Alçada-Almeida, Luís C. Dias
      The location of undesirable facilities involves economic, environmental and social impacts. The costs associated and the rejection of facilities by nearby population are crucial concerns. This paper introduces a Multiobjective Mixed-Integer Linear Programming (MMILP) approach to identify locations and capacities of biogas plants to treat animal waste from dairy farms, and assign each farm to a subset of the opened biogas plants. Three objectives were considered in the mathematical model: minimizing initial investment, operation and maintenance costs; minimizing transportation cost; and minimizing social rejection. The proposed model was applied to the Entre-Douro-e-Minho Region in Portugal. The approach provided as output a set of Pareto optimal solutions, represented by maps using a Geographic Information System, each one achieving a unique combination of economic and social performance.

      PubDate: 2017-03-08T13:52:25Z
       
  • Heuristic decision rules for short-term trading of renewable energy with
           co-located energy storage
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Michael Hassler
      In the last decade, the share of renewable energy sources in the energy mix has risen significantly in many countries, and the large-scale integration of these intermittent energy sources constitutes a major challenge to the power grid. A crucial building block of a successful transformation of today's energy systems is the use of energy storage, either co-located with renewable energy sources or on a grid-level. To this end, we present a model on the basis of a Markov Decision Process for the short-term trading of intermittent energy production co-located with energy storage. The model explicitly considers the time lag between trade and delivery of energy, which is characteristic for energy markets. Our storage representation includes asymmetrical conversion losses, asymmetrical power, and self-discharge. Stochastic production and market prices are represented by ARIMA processes, and the producer may also undertake price arbitrage by purchasing energy on the market when prices are comparatively low. Regarding the solution of our model, we develop several intuitive and easily interpretable decision rules that can be readily applied in practice. An extensive numerical study, based on real-world data, confirms the excellent performance of these rules in comparison to a sophisticated Approximate Dynamic Programming algorithm adapted from literature.

      PubDate: 2017-03-08T13:52:25Z
       
  • Quality assurance laboratory planning system to maximize worker preference
           subject to certification and preference balance constraints
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Alex J. Ruiz-Torres, Farzad Mahmoodi, Markku Kuula
      This research addresses the assignment of technicians to quality control tests in a pharmaceutical manufacturing environment. The problem is complex as it includes constraints related to the capabilities of the quality assurance technicians, as well as various criteria related to efficiency, customer service, and worker satisfaction. We consider several factors that are particular to labor scheduling in the pharmaceutical industry: preference to certain types of work and certification related to training in specific tests. We propose and utilize a technician satisfaction metric and develop a heuristic to maximize this measure. Experiments are performed in order to evaluate the performance of the proposed heuristic, and gain insights regarding the relationship among key experimental factors. The results demonstrate that, in general, the proposed heuristic quickly generates scheduling assignments that provide a very good approximation of the optimal solution.

      PubDate: 2017-03-02T07:28:25Z
       
  • Minmax scheduling with acceptable lead-times: Extensions to
           position-dependent processing times, due-window and job rejection
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Enrique Gerstl, Baruch Mor, Gur Mosheiov
      We focus on a due-date assignment model where due-dates are determined by penalties for jobs exceeding pre-specified (job-dependent, different) deadlines. The underlying assumption of this model, denoted by DIF, is that there are "lead times that customers consider to be reasonable and expected". In a minmax DIF model, the value of the objective function is that of the largest job/due-date cost. The goal is to find both the job sequence and the due-dates, such that this value is minimized. In this paper we study several extensions of the minmax DIF model. First, we consider general position-dependent job processing times. Then we extend the model to a setting of a due-window for acceptable lead-times. Here, the assumption is that a time interval exists, such that due-dates assigned to be within this interval are not penalized. The last extension of the DIF model is to a setting allowing job-rejection. This option reflects many real-life situations, where the scheduler may decide to process only a subset of the jobs, and the rejected jobs are penalized. The first two extensions are shown to be polynomially solvable: we introduce solution algorithms requiring O(n 3) and O(n 4) time, respectively, where n is the number of jobs. The last extension (assuming job-rejection) is proved to be NP-hard in the ordinary sense, and an efficient pseudo-polynomial dynamic programming algorithm is introduced.

      PubDate: 2017-03-02T07:28:25Z
       
  • A multi-tier linking approach to analyze performance of autonomous
           vehicle-based storage and retrieval systems
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Debjit Roy, Ananth Krishnamurthy, Sunderesh S. Heragu, Charles Malmborg
      To improve operational flexibility, throughput capacity, and responsiveness in order fulfillment operations, several distribution centers are implementing autonomous vehicle-based storage and retrieval system (AVS/RS) in their high-density storage areas. In such systems, vehicles are self-powered to travel in horizontal directions (x- and y- axes), and use lifts or conveyors for vertical motion (z-axis). In this research, we propose a multi-tier queuing modeling framework for the performance analysis of such vehicle-based warehouse systems. We develop an embedded Markov chain based analysis approach to estimate the first and second moment of inter-departure times from the load-dependent station within a semi-open queuing network. The linking solution approach uses traffic process approximations to analyze the performance of sub-models corresponding to individual tiers (semi-open queues) and the vertical transfer units (open queues). These sub-models are linked to form an integrated queuing network model, which is solved using an iterative algorithm. Performance estimates such as expected transaction cycle times and resource (vehicle and vertical transfer unit) utilization are determined using this algorithm, and can be used to evaluate a variety of design configurations during the conceptualization phase.

      PubDate: 2017-03-02T07:28:25Z
       
  • An exact hybrid method for the vehicle routing problem with time windows
           and multiple deliverymen
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Aldair Alvarez, Pedro Munari
      The vehicle routing problem with time windows and multiple deliverymen (VRPTWMD) is a variant of the vehicle routing problem with time windows in which service times at customers depend on the number of deliverymen assigned to the route that serves them. In particular, a larger number of deliverymen in a route leads to shorter service times. Hence, in addition to the usual routing and scheduling decisions, the crew size for each route is also an endogenous decision. This problem is commonly faced by companies that deliver goods to customers located in busy urban areas, a situation that requires nearby customers to be grouped in advance so that the deliverymen can serve all customers in a group during one vehicle stop. Consequently, service times can be relatively long compared to travel times, which complicates serving all scheduled customers during regular work hours. In this paper, we propose a hybrid method for the VRPTWMD, combining a branch-price-and-cut (BPC) algorithm with two metaheuristic approaches. We present a wide variety of computational results showing that the proposed hybrid approach outperforms the BPC algorithm used as standalone method in terms of both solution quality and running time, in some classes of problem instances from the literature. These results indicate the advantages of using specific algorithms to generate good feasible solutions within an exact method.

      PubDate: 2017-02-22T22:33:54Z
       
  • Lagrangian and branch-and-cut approaches for upgrading spanning tree
           problems
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Eduardo Álvarez-Miranda, Markus Sinnl
      Problems aiming at finding budget constrained optimal upgrading schemes to improve network performance have received attention over the last two decades. In their general setting, these problems consist of designing a network and, simultaneously, allocating (limited) upgrading resources in order to enhance the performance of the designed network. In this paper we address two particular optimal upgrading network design problems; in both cases, the sought-after layout corresponds to a spanning tree of the input network and upgrading decisions are to be taken on nodes. We design Mixed Integer Programming-based algorithmic schemes to solve the considered problems: Lagrangian relaxation approaches and branch-and-cut algorithms. Along with the designed algorithms, different enhancements, including valid inequalities, primal heuristic and variable fixing procedures, are proposed. Using two set of instances, we experimentally compare the designed algorithms and explore the benefits of the devised enhancements. The results show that the proposed approaches are effective for solving to optimality most of the instances in the testbed, or manage to obtain solutions and bounds giving very small optimality gaps. Besides, the proposed enhancements turn out to be beneficial for improving the performance of the algorithms.

      PubDate: 2017-02-22T22:33:54Z
       
  • A multi-start iterated local search algorithm for the generalized
           quadratic multiple knapsack problem
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Mustafa Avci, Seyda Topaloglu
      The quadratic multiple knapsack problem (QMKP) is a variant of the classical knapsack problem where multiple knapsacks are considered and the objective is to maximize a quadratic objective function subject to capacity constraints. The generalized quadratic multiple knapsack problem (G-QMKP) extends the QMKP by considering the setups, assignment conditions and the knapsack preferences of the items. In this study, a multi-start iterated local search algorithm (MS-ILS) in w the variable neighborhood descent (VND) algorithm is integrated with an adaptive perturbation mechanism is proposed to solve the G-QMKP. The multi-start implementation together with the adaptive perturbation mechanism enables the search process to explore different search regions in the search space while VND is applied to obtain high-quality solutions from the examined regions. Another remarkable feature of MS-ILS is its simplicity and adaptiveness that ease its implementation. The proposed MS-ILS is tested on G-QMKP benchmark instances derived from the literature. The results indicate that the developed MS-ILS can produce high-quality solutions for the G-QMKP. Particularly, it has been observed that the developed MS-ILS has improved the best known solutions for 35 out of 48 large-size G-QMKP instances.

      PubDate: 2017-02-22T22:33:54Z
       
  • Redistributing stock in library systems with a depot
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): G. Van der Heide, K.J. Roodbergen, N.D. Van Foreest
      Public library organizations often utilize depots for carrying out shipments to libraries in case of stock-outs and for storing low demand rental items at low cost. Similar systems may be employed by rental companies for other rental products such as tools, DVDs, and jewelry. Since shipments deplete the depot’s inventory, stock must be taken back from the libraries in order to deal with future shipment requests. These shipment and take-back operations are carried out periodically, e.g. daily or weekly. This work focuses on optimizing the decisions for shipments and take-backs. We model the system by means of a Markov decision process and investigate its optimal policy for various problem instances. For the take-back decision, we distinguish between so-called threshold, reactive, and preventive take-backs. We use the insights from the MDP to develop a three-phase take-back heuristic. In experiments, our heuristic performs within 1% on average from the optimal solution. For settings with a large number of libraries, it is shown that an acceptable performance can be achieved by setting a base-stock level at the depot and taking back sufficient stock from the libraries to achieve this level.

      PubDate: 2017-02-22T22:33:54Z
       
  • A fast two-level variable neighborhood search for the clustered vehicle
           routing problem
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Christof Defryn, Kenneth Sörensen
      In this paper, we present an improved two-level heuristic to solve the clustered vehicle routing problem (CluVRP). The CluVRP is a generalization of the classical capacitated vehicle routing problem (CVRP) in which customers are grouped into predefined clusters, and all customers in a cluster must be served consecutively by the same vehicle. This paper contributes to the literature in the following ways: (i) new upper bounds are presented for multiple benchmark instances, (ii) good heuristic solutions are provided in much smaller computing times than existing approaches, (iii) the CluVRP is reduced to its cluster level without assuming Euclidean coordinates or distances, and (iv) a new variant of the CluVRP, the CluVRP with weak cluster constraints, is introduced. In this variant, clusters are allocated to vehicles in their entirety, but all corresponding customers can be visited by the vehicle in any order. The proposed heuristic solves the CluVRP by combining two variable neighborhood search algorithms, that explore the solution space at the cluster level and the individual customer level respectively. The algorithm is tested on different benchmark instances from the literature with up to 484 nodes, obtaining high quality solutions while requiring only a limited calculation time.

      PubDate: 2017-02-22T22:33:54Z
       
  • Real-time management of transportation disruptions in forestry
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Amine Amrouss, Nizar El Hachemi, Michel Gendreau, Bernard Gendron
      In this paper, we present a mathematical programming model based on a time-space network representation for solving real-time transportation problems in forestry. We cover a wide range of unforeseen events that may disrupt the planned transportation operations (e.g., delays, changes in the demand and changes in the topology of the transportation network). Although each of these events has different impacts on the initial transportation plan, one key characteristic of the proposed model is that it remains valid for dealing with all the unforeseen events, regardless of their nature. Indeed, the impacts of such events are reflected in a time-space network and in the input parameters rather than in the model itself. The empirical evaluation of the proposed approach is based on data provided by Canadian forestry companies and tested under generated disruption scenarios. The test sets have been successfully solved to optimality in short computational times and demonstrate the potential improvement of transportation operations incurred by this approach.

      PubDate: 2017-02-22T22:33:54Z
       
  • MIP neighborhood synthesis through semantic feature extraction and
           automatic algorithm configuration
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Tommaso Adamo, Gianpaolo Ghiani, Antonio Grieco, Emanuela Guerriero, Emanuele Manni
      The definition of a “good” neighborhood structure on the solution space is a key step when designing several types of heuristics for Mixed Integer Programming (MIP). Typically, in order to achieve efficiency in the search, the neighborhood structures need to be tailored not only to the specific problem but also to the peculiar distribution of the instances to be solved (reference instance population). Nowadays, this is done by human experts through a time-consuming process comprising: (a) problem analysis, (b) literature scouting and (c) experimentation. In this paper, we illustrate an Automatic Neighborhood Design algorithm that mimics steps (a) and (c). Firstly, the procedure extracts some semantic features from a MIP compact model. Secondly, these features are used to derive automatically some neighborhood design mechanisms. Finally, the “proper mix” of such mechanisms is sought through an automatic configuration phase performed on a training set representative of the reference instance population. When assessed on four well-known combinatorial optimization problems, our automatically-generated neighborhoods outperform state-of-the-art model-based neighborhoods with respect to both scalability and solution quality.

      PubDate: 2017-02-22T22:33:54Z
       
  • Column generation strategies and decomposition approaches for the
           two-stage stochastic multiple knapsack problem
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): D.D. Tönissen, J.M. van den Akker, J.A. Hoogeveen
      Many problems can be formulated by variants of knapsack problems. However, such models are deterministic, while many real-life problems include some kind of uncertainty. Therefore, it is worthwhile to develop and test knapsack models that can deal with disturbances. In this paper, we consider a two-stage stochastic multiple knapsack problem. Here, we have a multiple knapsack problem together with a set of possible disturbances. For each disturbance, or scenario, we know its probability of occurrence and the resulting reduction in the sizes of the knapsacks. For each knapsack we decide in the first stage which items we take with us, and when a disturbance occurs we are allowed to remove items from the corresponding knapsack. Our goal is to find a solution where the expected revenue is maximized. We use branch-and-price to solve this problem. We present and compare two solution approaches: the separate recovery decomposition (SRD) and the combined recovery decomposition (CRD). We prove that the LP-relaxation of the CRD is stronger than the LP-relaxation of the SRD. Furthermore, we investigate numerous column generation strategies and methods to create additional columns outside the pricing problem. These strategies reduce the solution time significantly. To the best of our knowledge, there is no other paper that investigates such strategies so thoroughly.

      PubDate: 2017-02-22T22:33:54Z
       
  • Branch-and-price and adaptive large neighborhood search for the truck and
           trailer routing problem with time windows
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Sophie N. Parragh, Jean-François Cordeau
      Motivated by a situation faced by infrastructure service providers operating in urban areas with accessibility restrictions, we study the truck and trailer routing problem with time windows (TTRPTW). In this problem the vehicle fleet consists of trucks and trailers which may be decoupled. A set of customers has to be served and some of the customers can only be accessed by the truck without the trailer. This gives rise to the planning of truck-and-trailer routes containing truck-only subroutes, in addition to truck-only routes and truck-and-trailer routes without subroutes. We propose a branch-and-price algorithm for the TTRPTW, using problem specific enhancements in the pricing scheme and alternative lower bound computations. We also tailor an adaptive large neighborhood search algorithm to the TTRPTW in order to obtain good initial columns. When compared to existing metaheuristic algorithms we obtain highly competitive results. Some instances with up to 100 customers are solved to optimality with the proposed branch-and-price algorithm.

      PubDate: 2017-02-15T22:30:51Z
       
  • A new approach to cooperative competition in facility location problems:
           Mathematical formulations and an approximation algorithm
    • Abstract: Publication date: July 2017
      Source:Computers & Operations Research, Volume 83
      Author(s): Mohammad Rohaninejad, Hamidreza Navidi, Behdin Vahedi Nouri, Reza Kamranrad
      This paper deals with cooperative competition in facility location problems in which potential players (investors) are in competition (or conflict) over acquiring suitable sites and clients. In order to formulate the problem, a game-theoretical multi-objective model with the objective of maximizing investor utility is presented. In the proposed method, an acceptance threshold constraint is applied to facility allocation that is based on a combination of distance between a facility and clients, and investors’ product prices. Since the common solution methods for multi-objective optimization, such as weighted sums, ε-constraints, multi-objective meta-heuristic algorithms, etc. are not efficient enough, and cannot guarantee achieving Nash equilibrium points, a new approach is developed to solve the presented problem. Moreover, according to the computational complexity of the problem, an approximation algorithm is introduced for large-sized problems. Finally, the computational results demonstrate that the proposed algorithm performs efficiently in obtaining Nash equilibrium points.

      PubDate: 2017-02-15T22:30:51Z
       
  • Balancing stochastic two-sided assembly line with multiple constraints
           using hybrid teaching-learning-based optimization algorithm
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Qiuhua Tang, Zixiang Li, LiPing Zhang, Chaoyong Zhang
      Two-sided assembly lines are usually found in the factories which produce large-sized products. In most literatures, the task times are assumed to be deterministic while these tasks may have varying operation times in real application, causing the reduction of performance or even the infeasibility of the schedule. Moreover, the ignorance of some specific constraints including positional constraints, zoning constraints and synchronism constraints will result in the invalidation of the schedule. To solve this stochastic two-sided assembly line balancing problem with multiple constraints, we propose a hybrid teaching-learning-based optimization (HTLBO) approach which combines both a novel teaching-learning-based optimization algorithm for global search and a variable neighborhood search with seven neighborhood operators for local search. Especially, a new priority-based decoding approach is developed to ensure that the selected tasks satisfy most of the constraints identified by multiple thresholds of the priority value and to reduce the idle times related to sequence-dependence among tasks. Experimental results on benchmark problems demonstrate both remarkable efficiency and universality of the developed decoding approach, and the comparison among 11 algorithms shows the effectiveness of the proposed HTLBO.

      PubDate: 2017-02-09T22:28:47Z
       
  • The multi-objective assembly line worker integration and balancing problem
           of type-2
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Mayron César O. Moreira, Rafael Pastor, Alysson M. Costa, Cristóbal Miralles
      The consideration of worker heterogeneity in assembly lines has received a fair amount of attention in the literature in the past decade. Most of this exploration uses as motivation the example of assembly lines in sheltered work centers for the disabled. Only recently has the community started looking at the situation faced in assembly lines in the general industrial park, when in the presence of worker heterogeneity. This step raises a number of questions around the best way to incorporate heterogeneous workers in the line, maximizing their integration while maintaining productivity levels. In this paper we propose the use of Miltenburg’s regularity criterion and cycle time as metrics for integration of workers and productivity, respectively. We then define, model and develop heuristics for a line balancing problem with these two goals. Results obtained through an extensive set of computational experiments indicate that a good planning can obtain trade-off solutions that perform well in both objectives.

      PubDate: 2017-02-09T22:28:47Z
       
  • A differential evolution algorithm for finding the median ranking under
           the Kemeny axiomatic approach
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Antonio D’Ambrosio, Giulio Mazzeo, Carmela Iorio, Roberta Siciliano
      In recent years the analysis of preference rankings has become an increasingly important topic. One of the most important tasks in dealing with preference rankings is the identification of the median ranking, namely that ranking that best represents the preferences of a population of judges. This task is known with several alternative names, such as rank aggregation problem, consensus ranking problem, social choice problem. In this paper we propose a Differential Evolution algorithm for the Consensus Ranking detection (DECoR) within the Kemeny’s axiomatic framework. The algorithm works with full, partial and incomplete rankings. A simulation study shows that our proposal is particularly feasible when working with a very large number of objects to be ranked, because it is accurate and also faster than other proposals. Some applications on real data sets show the practical utility of our proposal in helping the users in taking decisions.

      PubDate: 2017-02-09T22:28:47Z
       
  • Solving a large multicontainer loading problem in the car manufacturing
           industry
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): J.F. Correcher, M.T. Alonso, F. Parreño, R. Alvarez-Valdes
      Renault, a large car manufacturer with factories all over the world, has a production system in which not every factory produces all the parts required to assemble a vehicle. Every day, large quantities of car parts are sent from one factory to another, defining very large truck/container transportation problems. The main challenge faced by the Renault logistics platforms is to load the items into trucks and containers as efficiently as possible so as to minimize the number of vehicles sent. Therefore, the problem to be solved is a multicontainer loading problem in which, besides the usual geometric constraints preventing items from overlapping and exceeding the dimensions of the container, there are many other constraints, concerning the way in which items are put into layers, layers into stacks and stacks into containers, limiting the total weight and the weight supported by the items. In this paper we propose a GRASP algorithm, including constructive procedures to build solutions satisfying all the constraints, randomization strategies to produce diversity of solutions, and improvement moves to obtain high-quality solutions in short computing times. The algorithm has been tested on a set of real instances provided by the company and the results are competitive with the best results known, including some new improved solutions.

      PubDate: 2017-02-09T22:28:47Z
       
  • Finding extreme supported solutions of biobjective network flow problems:
           An enhanced parametric programming approach
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Andrea Raith, Antonio Sedeño-Noda
      We address the problem of determining a complete set of extreme supported efficient solutions of biobjective minimum cost flow (BMCF) problems. A novel method improving the classical parametric method for this biobjective problem is proposed. The algorithm runs in O(Nn(m + nlogn)) time determining all extreme supported non-dominated points in the outcome space and one extreme supported efficient solution associated with each one of them. Here n is the number of nodes, m is the number of arcs and N is the number of extreme supported non-dominated points in outcome space for the BMCF problem. The memory space required by the algorithm is O(n + m) when the extreme supported efficient solutions are not required to be stored in RAM. Otherwise, the algorithm requires O(N + m) space. Extensive computational experiments comparing the performance of the proposed method and a standard parametric network simplex method are presented.

      PubDate: 2017-02-09T22:28:47Z
       
  • An integer programming approach to scheduling the transshipment of
           products at cross-docks in less-than-truckload industries
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): M.Y. Maknoon, F. Soumis, P. Baptiste
      This paper introduces an exact method to schedule the internal transshipment process at cross-docks in less-than-truckload industries. An integer programming formulation is presented to minimize the cost of double handling by synchronizing two types of decisions: (1) products’ internal transferring route, and (2) the order of processing trucks at the terminal doors. Several valid inequalities are introduced to strengthen the formulation and to increase the efficiency of the proposed algorithm. A tailored branch and bound algorithm is developed. Several structural properties and a heuristic method are implemented to enhance the algorithm. Computational experiments of up to 40 trucks demonstrate the efficiency of the proposed approach.

      PubDate: 2017-02-09T22:28:47Z
       
  • Improving polynomial estimation of the Shapley value by stratified random
           sampling with optimum allocation
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Javier Castro, Daniel Gómez, Elisenda Molina, Juan Tejada
      In this paper, we propose a refinement of the polynomial method based on sampling theory proposed by Castro et al. (2009) to estimate the Shapley value for cooperative games. In addition to analyzing the variance of the previously proposed estimation method, we employ stratified random sampling with optimum allocation in order to reduce the variance. We examine some desirable statistical features of the stratified approach and provide some computational results by analyzing the gains due to stratification, which are around 30% on average and more than 80% in the best case.

      PubDate: 2017-02-09T22:28:47Z
       
  • Bi-objective orienteering for personal activity scheduling
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Piotr Matl, Pamela C. Nolz, Ulrike Ritzinger, Mario Ruthmair, Fabien Tricoire
      We propose and solve a rich, bi-objective extension of the orienteering problem with time windows (OPTW) to model a combined routing and scheduling problem. Our research is motivated by the problem faced by mobile freelancers who have to integrate irregular appointments and tasks into their daily routines. Those people have a number of tasks which they need to perform at various locations (e.g. meetings with different clients), subject to varying time constraints (e.g. opening hours), and with different levels of importance or urgency (e.g. submitting a deliverable versus cleaning the home office). Furthermore, sets of related tasks may be subject to precedence relations and time dependencies. We explicitly consider the trade-off between planning more tasks and enjoying more free time by means of a bi-objective model. The extension of the OPTW and the bi-objective formulation result in the Personal Planning Problem (PPP). We present a mathematical formulation of the PPP and a metaheuristic based on Large Neighborhood Search (LNS) is developed to generate a set of non-dominated solutions to the problem. Solution quality is analyzed on real-world-inspired test instances. Exact reference sets based on a linear single-commodity flow model are used as benchmarks. Extensive computational experiments show that the proposed metaheuristic generates near-optimal solution sets and scales well to larger instances.

      PubDate: 2017-02-03T21:56:10Z
       
  • A hybrid integer and constraint programming approach to solve nurse
           rostering problems
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Erfan Rahimian, Kerem Akartunalı, John Levine
      The Nurse Rostering Problem can be defined as assigning a series of shift sequences (schedules) to several nurses over a planning horizon according to some limitations and preferences. The inherent benefits of generating higher-quality schedules are a reduction in outsourcing costs and an increase in job satisfaction of employees. In this paper, we present a hybrid algorithm, which combines Integer Programming and Constraint Programming to efficiently solve the highly-constrained Nurse Rostering Problem. We exploit the strength of IP in obtaining lower-bounds and finding an optimal solution with the capability of CP in finding feasible solutions in a co-operative manner. To improve the performance of the algorithm, and therefore, to obtain high-quality solutions as well as strong lower-bounds for a relatively short time, we apply some innovative ways to extract useful information such as the computational difficulty of instances and constraints to adaptively set the search parameters. We test our algorithm using two different datasets consisting of various problem instances, and report competitive results benchmarked with the state-of-the-art algorithms from the recent literature as well as standard IP and CP solvers, showing that the proposed algorithm is able to solve a wide variety of instances effectively.

      PubDate: 2017-02-03T21:56:10Z
       
  • A continuous DC programming approach for resource allocation in OFDMA/TDD
           wireless networks
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Nguyen Canh Nam, Pham Thi Hoai
      The next generation broadband wireless networks deploys OFDM/OFDMA as the enabling technologies for broadband data transmission with QoS capabilities. Many optimization problems have arisen in the conception of such a network. This article studies an optimization problem in resource allocation. By using mathematical modeling technique we formulate the considered problem as a pure integer linear program. This problem is reformulated as a DC (Difference of Convex functions) program via an exact penalty technique. We then propose a continuous approach for its resolution. Our approach is based on DC programming and DCA (DC Algorithm). It works in a continuous domain, but provides integer solutions. To check globality of computed solutions, a global method combining DCA with a well adapted Branch-and-Bound (B&B) algorithm is investigated. Preliminary numerical results are reported to show the efficiency of the proposed method with respect to the standard Branch-and-Bound algorithm.

      PubDate: 2017-02-03T21:56:10Z
       
  • Multi-vehicle prize collecting arc routing for connectivity problem
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Vahid Akbari, F. Sibel Salman
      For effective disaster response, roads should be cleared or repaired to provide accessibility and relief services to the affected people in shortest time. We study an arc routing problem that aims to regain the connectivity of the road network components by clearing a subset of the blocked roads. In this problem, we maximize the total prize gained by reconnecting disconnected network components within a specified time limit. The solution should determine the coordinated routes of each work troop starting at a depot node such that none of the closed roads can be traversed unless their unblocking/clearing procedure is finished. We develop an exact Mixed Integer Program (MIP) and a matheuristic method. The matheuristic solves single vehicle problems sequentially with updated prizes. To obtain an upper bound, we first relax the timing elements in the exact formulation and then solve its relaxed MIP, which decomposes into single vehicle problems, by Lagrangian Relaxation. We show the effectiveness of the proposed methods computationally on both random Euclidean and Istanbul road network data generated with respect to predicted earthquake scenarios.

      PubDate: 2017-01-28T21:52:03Z
       
  • A cutting plane algorithm for the site layout planning problem with travel
           barriers
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Ahmed W.A. Hammad, David Rey, Ali Akbarnezhad
      Site layout planning is an imperative procedure that may significantly impact the productivity and the efficiency of logistical operations undertaken on a construction site. This paper considers the site layout planning problem (SLPP) which entails the allocation of temporary facilities on a construction site in the presence of travel barriers such that the total transportation cost between facilities is minimised. In order to account for travel barriers, the SLPP is typically solved under the assumption that the available region for facility layout can be discretised. In this paper, we propose a general Mixed Integer Programming (MIP) model to represent the SLPP, accounting for the presence of barriers, and we show how space-discretised formulations can be derived from this model. In particular, we propose a novel MIP model, which permits facilities to cover multiple locations. This is then benchmarked against a commonly adopted MIP model in the literature. We also highlight a systematic procedure to convert the continuous feasible space in SLPP to a set of discretised locations based on the concept of d-visibility, enabling us to approximate the barrier distance function embedded in the objective function. In particular, we focus on presenting a simple space discretisation approach for converting the continuous SLP into a discrete problem for which the discrete SLP models would be applicable. Space-discretised MIP formulations are highly combinatorial and we introduce a cutting plane algorithm to improve their tractability. Specifically, we propose a novel exact location-decomposition algorithm which works from a relaxed MIP formulation and iteratively generates feasibility cuts to converge to an optimal solution. Both space-discretised MIP models and the decomposition algorithm are tested on a large group of instances to analyse their effectiveness in solving the SLPP. Computational results indicate that the proposed location-decomposition algorithm improves on the pure MIP approach and provides a competitive framework to solve realistic SLPP instances.

      PubDate: 2017-01-21T21:20:18Z
       
  • Multi-period technician scheduling with experience-based service times and
           stochastic customers
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Xi Chen, Barrett W. Thomas, Mike Hewitt
      This paper introduces the multi-period technician scheduling problem with experience-based service times and stochastic customers. In the problem, a manager must assign tasks of different types that are revealed at the start of each day to technicians who must complete the tasks that same day. As a technician gains experience with a type of task, the time that it takes to serve future tasks of that type is reduced (often referred to as experiential learning). As such, while the problem could be modeled as a single-period problem (i.e. focusing solely on the current day’s tasks), we instead choose to model it as a multi-period problem and thus capture that daily decisions should recognize the long-term effects of learning. Specifically, we model the problem as a Markov decision process and introduce an approximate dynamic programming-based solution approach. The model can be adapted to handle cases of worker attrition and new task types. The solution approach relies on an approximation of the cost-to-go that uses forecasts of the next day’s assignments for each technician and the resulting estimated time it will take to service those assignments given current period decisions. Using an extensive computational study, we demonstrate the value of our approach versus a myopic solution approach that views the problem as a single-period problem.

      PubDate: 2017-01-14T16:06:22Z
       
  • The Traveling Purchaser Problem with time-dependent quantities
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): E. Angelelli, M. Gendreau, R. Mansini, M. Vindigni
      The deterministic Traveling Purchaser Problem looks for a tour visiting a subset of markets in order to satisfy a positive discrete demand for some products at minimum traveling and purchasing costs. In this paper, we assume that the quantities available in the markets for all the products are time-varying decreasing at a constant rate. We propose a compact mixed integer formulation for the problem, and strengthen it with the introduction of connectivity constraints. A new branching strategy and a primal heuristic enforcing the bounding operations have been embedded into a branch-and-cut framework. The branching rule exploits a simple valid inequality and the potential presence of necessary markets. The resulting method outperforms CPLEX 12.6 when used to solve the proposed model. The algorithms have been tested on standard TSPLIB instances, modified to include products and quantities that decrease at different rates of consumption.

      PubDate: 2017-01-14T16:06:22Z
       
  • VCS: A new heuristic function for selecting boxes in the single container
           loading problem
    • Abstract: Publication date: June 2017
      Source:Computers & Operations Research, Volume 82
      Author(s): Ignacio Araya, Keitel Guerrero, Eduardo Nuñez
      The single container loading problem consists of a container that has to be filled with a set of boxes. The objective of the problem is to maximize the total volume of the loaded boxes. For solving the problem, constructive approaches are the most successful. A key element of these approaches is related to the selection of the box to load next. In this work, we propose a new evaluation function for ranking boxes. Our function rewards boxes that fit well in the container, taking into account the previously placed ones. To construct a more robust function, we consider some other well-known evaluation criteria such as the volume of the block and the estimated wasted volume in the free space of the container. Our approach shows promising results when compared with other state-of-the-art algorithms on a set of 1600 well-known benchmark instances.

      PubDate: 2017-01-14T16:06:22Z
       
  • Achieving full connectivity of sites in the multiperiod reserve network
           design problem
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Nahid Jafari, Bryan L. Nuse, Clinton T. Moore, Bistra Dilkina, Jeffrey Hepinstall-Cymerman
      The conservation reserve design problem is a challenge to solve because of the spatial and temporal nature of the problem, uncertainties in the decision process, and the possibility of alternative conservation actions for any given land parcel. Conservation agencies tasked with reserve design may benefit from a dynamic decision system that provides tactical guidance for short-term decision opportunities while maintaining focus on a long-term objective of assembling the best set of protected areas possible. To plan cost-effective conservation over time under time-varying action costs and budget, we propose a multi-period mixed integer programming model for the budget-constrained selection of fully connected sites. The objective is to maximize a summed conservation value over all network parcels at the end of the planning horizon. The originality of this work is in achieving full spatial connectivity of the selected sites during the schedule of conservation actions.

      PubDate: 2017-01-06T13:20:34Z
       
  • A Benders decomposition based framework for solving cable trench problems
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Hatice Calik, Markus Leitner, Martin Luipersbeck
      In this work, we present an algorithmic framework based on Benders decomposition for the Capacitated p-Cable Trench Problem with Covering. We show that our approach can be applied to most variants of the Cable Trench Problem (CTP) that have been considered in the literature. The proposed algorithm is augmented with a stabilization procedure to accelerate the convergence of the cut loop and with a primal heuristic to derive high-quality primal solutions. Three different variants of the CTP are considered in a computational study which compares the Benders approach with two compact integer linear programming formulations that are solved with CPLEX. The obtained results show that the proposed algorithm significantly outperforms the two compact models and that it can be used to tackle significantly larger instances than previously considered algorithms based on Lagrangean relaxation.

      PubDate: 2017-01-06T13:20:34Z
       
  • Mixed integer linear programming models for optimal crop selection
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Carlo Filippi, Renata Mansini, Elisa Stevanato
      In this paper, we propose the modeling of a real-case problem where a farmer has to optimize the use of his/her land by selecting the best mix of crops to cultivate. Complexity of the problem is due to the several factors that have to be considered simultaneously. These include the market prices variability of harvested products, the specific resource requests for each crop, the restrictions caused by limited machines availability, and the timing of operations required to complete each crop cultivation. We provide two different mathematical formulations for the analyzed problem. The first one represents a natural integer programming formulation looking for the crop-mix that maximizes the farmer’s expected profit measured as the difference between revenues obtained by selling the harvested products and the production costs. Since the revenue of each crop depends on the price as quoted at the exchange market and the yield per hectare of harvested product, we define it as a random variable. Then, the second model uses the maximization of the Conditional Value-at-Risk (CVaR) as objective function and looks for the crop-mix that allows to maximize the average expected profit under a predefined quantile of worst realizations. To test and compare the proposed models with the cultivation choice made by the farmer, we use Italian historical data represented by monthly returns of different crops over a time period of 16 years. Computational results emphasize the advantage of using the CVaR model for a risk-averse farmer and provide interesting insights for farmers involved in similar problems.

      PubDate: 2016-12-28T03:45:49Z
       
  • Delay-constrained routing problems: Accurate scheduling models and
           admission control
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Antonio Frangioni, Laura Galli, Giovanni Stea
      As shown in [1], the problem of routing a flow subject to a worst-case end-to-end delay constraint in a packed-based network can be formulated as a Mixed-Integer Second-Order Cone Program, and solved with general-p‘urpose tools in real time on realistic instances. However, that result only holds for one particular class of packet schedulers, Strictly Rate-Proportional ones, and implicitly considering each link to be fully loaded, so that the reserved rate of a flow coincides with its guaranteed rate. These assumptions make latency expressions simpler, and enforce perfect isolation between flows, i.e., admitting a new flow cannot increase the delay of existing ones. Other commonplace schedulers both yield more complex latency formulæ and do not enforce flow isolation. Furthermore, the delay actually depends on the guaranteed rate of the flow, which can be significantly larger than the reserved rate if the network is unloaded. In this paper we extend the result to other classes of schedulers and to a more accurate representation of the latency, showing that, even when admission control needs to be factored in, the problem is still efficiently solvable for realistic instances, provided that the right modeling choices are made.

      PubDate: 2016-12-28T03:45:49Z
       
  • An effective iterated tabu search for the maximum bisection problem
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Fuda Ma, Jin-Kao Hao, Yang Wang
      Given an edge weighted graph G = ( V , E ) , the maximum bisection problem involves partitioning the vertices of V into two disjoint subsets of equal cardinality such that the weight sum of the edges crossing the two subsets is maximized. In this study, we present an Iterated Tabu Search (ITS) algorithm to solve the problem. ITS employs two distinct search operators organized into three search phases to effectively explore the search space. Bucket sorting is used to ensure a high computational efficiency of the ITS algorithm. Experiments based on 71 well-known benchmark instances of the literature demonstrate that ITS is highly competitive compared to state-of-the-art approaches and discovers improved best-known results (new lower bounds) for 8 benchmark instances. The key ingredients of the algorithm are also investigated.

      PubDate: 2016-12-28T03:45:49Z
       
  • Scheduling on parallel processors with varying processing times
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Radosław Rudek
      In this paper, we construct the pseudopolynomial dynamic programming algorithm that optimally solves the parallel identical processor scheduling problem to minimize the maximum job completion times (makespan) under varying processing times. They can be described by an arbitrary monotonic function dependent on the number of previously processed jobs, which can model learning or aging effects. Beside the canonical dynamic programming algorithm, we provide its efficient parallel fast version, which solves moderate problem instances of the problem within reasonable time and memory usage. Additionally, on the basis of the constructed algorithm, a fully polynomial time approximation scheme for the considered problem is provided.

      PubDate: 2016-12-28T03:45:49Z
       
  • Exact and heuristic approaches based on noninterfering transmissions for
           joint gateway selection, time slot allocation, routing and power control
           for wireless mesh networks
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Kagan Gokbayrak, E. Alper Yıldırım
      Wireless mesh networks (WMNs) provide cost-effective alternatives for extending wireless communication over larger geographical areas. In this paper, given a WMN with its nodes and possible wireless links, we consider the problem of gateway node selection for connecting the network to the Internet along with operational problems such as routing, wireless transmission capacity allocation, and transmission power control for efficient use of wired and wireless resources. Under the assumption that each node of the WMN has a fixed traffic rate, our goal is to allocate capacities to the nodes in proportion to their traffic rates so as to maximize the minimum capacity-to-demand ratio, referred to as the service level. We adopt a time division multiple access (TDMA) scheme, in which a time frame on the same frequency channel is divided into several time slots and each node can transmit in one or more time slots. We propose two mixed integer linear programming formulations. The first formulation, which is based on individual transmissions in each time slot, is a straightforward extension of a previous formulation developed by the authors for a related problem under a different set of assumptions. The alternative formulation, on the other hand, is based on sets of noninterfering wireless transmissions. In contrast with the first formulation, the size of the alternative formulation is independent of the number of time slots in a frame. We identify simple necessary and sufficient conditions for simultaneous transmissions on different links of the network in the same time slot without any significant interference. Our characterization, as a byproduct, prescribes a power level for each of the transmitting nodes. Motivated by this characterization, we propose a simple scheme to enumerate all sets of noninterfering transmissions, which is used as an input for the alternative formulation. We also introduce a set of valid inequalities for both formulations. For large instances, we propose a three-stage heuristic approach. In the first stage, we solve a partial relaxation of our alternative optimization model and determine the gateway locations. This stage also provides an upper bound on the optimal service level. In the second stage, a routing tree is constructed for each gateway node computed in the first stage. Finally, in the third stage, the alternative optimization model is solved by fixing the resulting gateway locations and the routing trees from the previous two stages. For even larger networks, we propose a heuristic approach for solving the partial relaxation in the first stage using a neighborhood search on gateway locations. Our computational results demonstrate the promising performance of our exact and heuristic approaches and the valid inequalities

      PubDate: 2016-12-28T03:45:49Z
       
  • A hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Mohamed Amine Masmoudi, Kris Braekers, Malek Masmoudi, Abdelaziz Dammak
      This paper investigates the Heterogeneous Dial-A-Ride Problem (H-DARP) that consists of determining a vehicle route planning for heterogeneous users’ transportation with a heterogeneous fleet of vehicles. A hybrid Genetic Algorithm (GA) is proposed to solve the problem. Efficient construction heuristics, crossover operators and local search techniques, specifically tailored to the characteristics of the H-DARP, are provided. The proposed algorithm is tested on 92 benchmarks instances and 40 newly introduced larger instances. Computational experiments show the effectiveness of our approach compared to the current state-of-the-art algorithms for the DARP and H-DARP. When tested on the existing instances, we achieved average gaps of only 0.47% to the best-known solutions for the DARP, and 0.05% to the optimal solutions for the H-DARP, compared to 0.85% and 0.10%, respectively, obtained by the current state-of-the-art algorithms. For the 40 newly generated instances, average gaps of the hybrid GA are 0.35% smaller compared to the current state-of-the-art method. Besides, our method provides best results for 31 of these instances and ties with the existing method on 8 other instances.

      PubDate: 2016-12-21T03:43:30Z
       
  • A Combined column generation and heuristics for railway short-term rolling
           stock planning with regular inspection constraints
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Tatsushi Nishi, Akiyoshi Ohno, Masahiro Inuiguchi, Satoru Takahashi, Kenji Ueda
      The aim of railway rolling stock planning problem is to find an optimal allocation of train-sets for a given set of trips in the train timetable in order to minimize the total cost. We propose a column generation and Lagrangian relaxation heuristics for short-term rolling stock planning problems with regular inspection constraints. The problem is formulated as a subtour traveling salesman problem to find a set of elementary shortest cycles that cover all trips in the timetable. In the proposed method, a tight lower bound is obtained from the continuous relaxation of Dantzig–Wolfe reformulation by column generation. The pricing problem can be formulated as an elementary shortest cycle problem with resource constraints. A labeling algorithm is applied to solve the pricing problem. In order to reduce the computational effort, we apply a general state space augmenting algorithm to solve the pricing problems. Computational results show that the proposed column generation and Lagrangian relaxation heuristics can find good lower and upper bounds for 300 trips within reasonable computing time.

      PubDate: 2016-12-21T03:43:30Z
       
  • Minimizing CO2 emissions in a practical daily carpooling problem
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Bruno P. Bruck, Valerio Incerti, Manuel Iori, Matteo Vignoli
      Governments, as well as companies and individuals, are increasingly aware of the damages to the environment caused by human activities. In this sense, the reduction of CO2 emissions is an important topic that is pursued through a range of practices. A relevant example is carpooling, which is defined as the act of individuals sharing a single car. In this paper we approach a practical case found in an Italian service company. Our objective is to develop an integrated web application to be used by the employees of this company to organize carpooling crews on a daily basis, so as to reach a common destination. We look for possible crews by the use of mathematical formulations and heuristic algorithms. The heuristic algorithms are then embedded into the web application to provide users with carpooling solutions. Experimental results attest for a great potential in CO2 savings by the use of carpooling in the real-world scenario as well as in newly generated instances.

      PubDate: 2016-12-21T03:43:30Z
       
  • A linear programming based heuristic framework for min-max regret
           combinatorial optimization problems with interval costs
    • Abstract: Publication date: May 2017
      Source:Computers & Operations Research, Volume 81
      Author(s): Lucas Assunção, Thiago F. Noronha, Andréa Cynthia Santos, Rafael Andrade
      This work deals with a class of problems under interval data uncertainty, namely interval robust-hard problems, composed of interval data min-max regret generalizations of classical NP-hard combinatorial problems modeled as 0-1 integer linear programming problems. These problems are more challenging than other interval data min-max regret problems, as solely computing the cost of any feasible solution requires solving an instance of an NP-hard problem. The state-of-the-art exact algorithms in the literature are based on the generation of a possibly exponential number of cuts. As each cut separation involves the resolution of an NP-hard classical optimization problem, the size of the instances that can be solved efficiently is relatively small. To smooth this issue, we present a modeling technique for interval robust-hard problems in the context of a heuristic framework. The heuristic obtains feasible solutions by exploring dual information of a linearly relaxed model associated with the classical optimization problem counterpart. Computational experiments for interval data min-max regret versions of the restricted shortest path problem and the set covering problem show that our heuristic is able to find optimal or near-optimal solutions and also improves the primal bounds obtained by a state-of-the-art exact algorithm and a 2-approximation procedure for interval data min-max regret problems.

      PubDate: 2016-12-21T03:43:30Z
       
 
 
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