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  Subjects -> BUSINESS AND ECONOMICS (Total: 3107 journals)
    - ACCOUNTING (88 journals)
    - BANKING AND FINANCE (264 journals)
    - BUSINESS AND ECONOMICS (1150 journals)
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    - PRODUCTION OF GOODS AND SERVICES (138 journals)
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    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals)

BUSINESS AND ECONOMICS (1150 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: 12)
Accounting Forum     Hybrid Journal   (Followers: 23)
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   (Followers: 1)
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: 60)
African Development Review     Hybrid Journal   (Followers: 35)
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: 7)
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: 150)
American Journal of Business     Hybrid Journal   (Followers: 15)
American Journal of Business and Management     Open Access   (Followers: 52)
American Journal of Business Education     Open Access   (Followers: 10)
American Journal of Economics and Business Administration     Open Access   (Followers: 25)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 27)
American Journal of Evaluation     Hybrid Journal   (Followers: 13)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 19)
American Journal of Health Economics     Full-text available via subscription   (Followers: 11)
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: 25)
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: 28)
Annals of Operations Research     Hybrid Journal   (Followers: 8)
Annual Review of Economics     Full-text available via subscription   (Followers: 30)
Applied Developmental Science     Hybrid Journal   (Followers: 3)
Applied Economics     Hybrid Journal   (Followers: 46)
Applied Economics Letters     Hybrid Journal   (Followers: 29)
Applied Economics Quarterly     Full-text available via subscription   (Followers: 10)
Applied Financial Economics     Hybrid Journal   (Followers: 23)
Applied Mathematical Finance     Hybrid Journal   (Followers: 7)
Applied Stochastic Models in Business and Industry     Hybrid Journal   (Followers: 5)
Arab Economic and Business Journal     Open Access   (Followers: 3)
Archives of Business Research     Open Access   (Followers: 5)
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: 316)
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)
Asia-Pacific Management and Business Application     Open Access  
Asian Business Review     Open Access   (Followers: 2)
Asian Case Research Journal     Hybrid Journal   (Followers: 1)
Asian Development Review     Open Access   (Followers: 13)
Asian Economic Journal     Hybrid Journal   (Followers: 8)
Asian Economic Papers     Hybrid Journal   (Followers: 7)
Asian Economic Policy Review     Hybrid Journal   (Followers: 4)
Asian Journal of Accounting and Governance     Open Access   (Followers: 4)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 7)
Asian Journal of Social Sciences and Management Studies     Open Access   (Followers: 7)
Asian Journal of Sustainability and Social Responsibility     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Asian-pacific Economic Literature     Hybrid Journal   (Followers: 5)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5)
Atlantic Economic Journal     Hybrid Journal   (Followers: 15)
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: 25)
Australian Economic Review     Hybrid Journal   (Followers: 6)
Australian Journal of Maritime and Ocean Affairs     Hybrid Journal   (Followers: 10)
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: 4)
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: 10)
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: 33)
Brookings Papers on Economic Activity     Open Access   (Followers: 48)
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: 17)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 7)
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: 17)
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 Research     Open Access   (Followers: 6)
Business and Management Horizons     Open Access   (Followers: 12)
Business and Management Research     Open Access   (Followers: 17)
Business and Management Studies     Open Access   (Followers: 9)
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: 8)
Business Information Review     Hybrid Journal   (Followers: 13)
Business Management and Strategy     Open Access   (Followers: 41)
Business Research     Hybrid Journal   (Followers: 2)
Business Strategy and the Environment     Hybrid Journal   (Followers: 12)
Business Strategy Review     Hybrid Journal   (Followers: 7)
Business Strategy Series     Hybrid Journal   (Followers: 6)
Business Systems & Economics     Open Access   (Followers: 2)
Business Systems Research Journal     Open Access   (Followers: 5)
Business, Management and Education     Open Access   (Followers: 18)
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: 58)
Cambridge Journal of Regions, Economy and Society     Hybrid Journal   (Followers: 11)
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: 27)
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: 2)
CESifo Economic Studies     Hybrid Journal   (Followers: 16)
Chain Reaction     Full-text available via subscription  
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: 16)
China Economic Journal: The Official Journal of the China Center for Economic Research (CCER) at Peking University     Hybrid Journal   (Followers: 11)
China Economic Review     Hybrid Journal   (Followers: 9)
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: 2)
COEPTUM     Open Access  
Community Development Journal     Hybrid Journal   (Followers: 24)
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 Review     Hybrid Journal   (Followers: 16)
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: 6)
Corporate Philanthropy Report     Hybrid Journal   (Followers: 2)
Corporate Reputation Review     Hybrid Journal   (Followers: 4)
Creative and Knowledge Society     Open Access   (Followers: 10)
Creative Industries Journal     Hybrid Journal   (Followers: 9)
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  
Development     Full-text available via subscription   (Followers: 24)

        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  [3043 journals]
  • Metaheuristics for the tabu clustered traveling salesman problem
    • Abstract: Publication date: January 2018
      Source:Computers & Operations Research, Volume 89
      Author(s): Tianjiao Zhang, Liangjun Ke, Jing Li, Jisheng Li, Jingqi Huang, Zexi Li
      This paper considers a new variant of traveling salesman problem (TSP), called tabu clustered TSP (TCTSP). The nodes in TCTSP are partitioned into two kinds of subsets: clusters and tabu node sets, then the salesman has to visit exactly one node for each tabu node set and ensures that the nodes within a same cluster are visited consecutively, and the problem calls for a minimum cost cycle. The TCTSP can be used to model a class of telemetry tracking and command (TT&C) resources scheduling problem (TTCRSP), the goal of which is to efficiently schedule the TT&C resources in order to enable the satellites to be operated normally in their designed orbits. To solve it, two metaheuristics combined with path relinking are proposed. The one is Ant Colony Optimization (ACO) and the other is Greedy Randomized Adaptive Search Procedure (GRASP). The proposed algorithms are tested on the benchmark instances and real-life instances of the TTCRSP. The computational results show that the hybrid ACO with two path relinking strategies works the best among the studied metaheuristics in terms of solution quality within the same computational time.

      PubDate: 2017-08-03T06:22:10Z
       
  • Improved heuristic algorithms for the Job Sequencing and Tool Switching
           Problem
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Gustavo Silva Paiva, Marco Antonio M. Carvalho
      In flexible manufacturing systems, a single machine can be configured with different tools for processing different jobs, each requiring a specific set of tools. There is a limit to the maximum number of tools that can be loaded simultaneously in the machine; between the processing of two different jobs, it may be necessary to switch these tools, causing interruptions in the production line. The Job Sequencing and Tool Switching Problem (SSP) aims to determine a job sequence and the tool-loading order for a flexible machine, in order to minimize the number of tool switches. These two tasks can be separated into Sequencing, an NP -hard problem, and Tooling, which is a P problem if the job sequence is given. This paper proposes a methodology that uses graph representations, heuristic methods, and local search methods to solve the sequencing problem. These contributions are combined in an Iterated Local Search scheme, which is then combined with a classical tooling method, in order to solve the SSP. Comprehensive computational experiments show that the resulting method is competitive and can establish new best solutions for literature instances, while outperforming the current state-of-the-art method.

      PubDate: 2017-08-03T06:22:10Z
       
  • Minimizing the fuel consumption and the risk in maritime transportation: A
           bi-objective weather routing approach
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Aphrodite Veneti, Angelos Makrygiorgos, Charalampos Konstantopoulos, Grammati Pantziou, Ioannis A. Vetsikas
      The paper presents an improved solution to the ship weather routing problem based on an exact time-dependent bi-objective shortest path algorithm. The two objectives of the problem are the minimization of the fuel consumption and the total risk of the ship route while taking into account the time-varying sea and weather conditions and an upper bound on the total passage time of the route. Safety is also considered by applying the guidelines of the International Maritime Organization (IMO). As a case study, the proposed algorithm is applied for finding ship routes in the area of the Aegean Sea, Greece. Enhancements of the proposed algorithm are also presented which improve the efficiency of our approach.

      PubDate: 2017-08-03T06:22:10Z
       
  • The 2-stage assembly flowshop scheduling problem with total completion
           time: Efficient constructive heuristic and metaheuristic
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Jose M. Framinan, Paz Perez-Gonzalez
      In this paper, we address the 2-stage assembly scheduling problem where there are m machines in the first stage to manufacture the components of a product and one assembly station (machine) in the second stage. The objective considered is the minimisation of the total completion time. Since the NP-hard nature of this problem is well-established, most previous research has focused on finding approximate solutions in reasonable computation time. In our paper, we first review and derive a number of problem properties and, based on these ideas, we develop a constructive heuristic that outperforms the existing constructive heuristics for the problem, providing solutions almost in real-time. Finally, for the cases where extremely high-quality solutions are required, a variable local search algorithm is proposed. The computational experience carried out shows that the algorithm outperforms the best existing metaheuristic for the problem. As a summary, the heuristics presented in the paper substantially modify the state-of-the-art of the approximate methods for the 2-stage assembly scheduling problem with total completion time objective.

      PubDate: 2017-08-03T06:22:10Z
       
  • A hybrid heuristic procedure for the Windy Rural Postman Problem with
           Zigzag Time Windows
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Oliver Lum, Rui Zhang, Bruce Golden, Edward Wasil
      In arc routing applications, some streets may require service along both sides of the street. For a subset of these streets, the vehicle operator may choose to service both sides during a single traversal. We refer to this as zigzag service. In contrast to servicing each side separately, the vehicle stops more frequently and incurs a traversal cost, two service costs, and a penalty cost associated with the slowed travel time required to perform the zigzag service. The tradeoff is that the vehicle only needs to service the street once on its route. However, for other streets zigzag service is only possible during the early part of a day when there is very little traffic. This scenario is modeled by the Windy Rural Postman Problem with Zigzag Time Windows (WRPPZTW). We develop a hybrid heuristic for the WRPPZTW that combines standard insertion and local search techniques with integer programming. We compare the computational performance of our heuristic to an exact procedure from Nossack et al. (2017) on a set of small instances with 28 edges and test the scalability of our heuristic on a set of larger grid instances with as many as 1200 edges.

      PubDate: 2017-08-03T06:22:10Z
       
  • Solving the multi-vehicle multi-covering tour problem
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Tuan Anh Pham, Minh Hoàng Hà, Xuan Hoai Nguyen
      The well-known multi-vehicle covering tour problem (m-CTP) involves finding a minimum-length set of vehicle routes passing through a subset of vertices, subject to constraints on the length of each route and the number of vertices that it contains, such that each vertex not included in any route is covered. Here, a vertex is considered as covered if it lies within a given distance of at least a vertex of a route. This article introduces a generalized variant of the m-CTP that we called the multi-vehicle multi-covering Tour Problem (mm-CTP). In the mm-CTP, a vertex must be covered at least not only once but several times. Three variants of the problem are considered. The binary mm-CTP where a vertex is visited at most once, the mm-CTP without overnight where revisiting a vertex is allowed only after passing through another vertex and the mm-CTP with overnight where revisiting a vertex is permitted without any restrictions. We first propose graph transformations to convert the last two variants into the binary one and focus mostly on solving this variant. A special case of the problem is then formulated as an integer linear program and a branch-and-cut algorithm is developed. We also develop a Genetic Algorithm (GA) that provides high-quality solutions for the problem. Extensive computational results on the new problem mm-CTP as well as its other special cases show the performance of our methods. In particular, our GA outperforms the current best metaheuristics proposed for a wide class of CTP problems.

      PubDate: 2017-08-03T06:22:10Z
       
  • Efficient order processing in an inverse order picking system
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): David Füßler, Nils Boysen
      An inverse order picking system inverts the basic logic of traditional picker-to-parts systems where pickers successively visit all shelves storing requested stock keeping units (SKUs). Instead, the picker successively moves bins each containing a particular SKU along a line of multiple order bins and puts items into all bins that require the current SKU. In this setting, we aim at a synchronization between the batches of picking orders concurrently assembled and the sequence of SKUs moved along the line, such that the number of line passings to be accomplished by the picker is minimized. We formalize the resulting optimization problem, prove computational complexity, and derive suited solution procedures. In our computational study, we also address important managerial aspects, such as the sizing of the picking area that restricts the number of picking orders concurrently processed.

      PubDate: 2017-07-23T04:08:11Z
       
  • Approximating class-departure variability in tandem queues with downtime
           events: Regression-based variability function
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Ruth Sagron, Gad Rabinowitz, Israel Tirkel
      Predicting queue performance by approximating class-departure variability in tandem queues with downtime events via existing decomposition methods is neither accurate enough nor efficient enough. Analytic approximations, if conducted alone, lack accuracy but attempting to increase accuracy by incorporating simulation to analytic approximation has proved to require significant computation efforts. The aim of this paper is to reduce the latter inefficiency by modeling the Regression-Based Variability Function (RBVF) designed to approximate the between-class effect by exploiting the departure process from a single queue. The new approach predicts performance of n-tandem queues by reducing the focus to two-tandem queues for each traffic intensity level, as well as by modeling different policies of downtimes (e.g. first-come-first-served or priority). Numerical experiments demonstrate that the proposed RBVF delivers both accuracy and efficiency improvements: the relative errors associated with RBVF are about three times smaller than the best existing analytic procedures and the computation efforts associated with RBVF are about five times smaller than existing analytic procedure combined with simulation.

      PubDate: 2017-07-23T04:08:11Z
       
  • Efficient pairwise preference elicitation allowing for indifference
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Juergen Branke, Salvatore Corrente, Salvatore Greco, Walter Gutjahr
      Many methods in Multi-Criteria Decision Analysis for choice problems rely on eliciting pairwise preference information in their attempt to efficiently identify the most preferred solution out of a larger set of solutions. That is, they repeatedly ask the decision maker which of two solutions is preferred, and then use this information to reduce the number of possibly preferred solutions until only one remains. However, if the solutions have a very similar value to the decision maker, he/she may not be able to accurately decide which solution is preferred. This paper makes two main contributions. First, it extends Robust Ordinal Regression to allow a user to declare indifference in case the values of the two solutions do not differ by more than some personal threshold. Second, we propose and compare several heuristics to pick pairs of solutions to be shown to the decision maker in order to minimize the number of interactions necessary.

      PubDate: 2017-07-23T04:08:11Z
       
  • A problem evolution algorithm with linear programming for the dynamic
           facility layout problem—A general layout formulation
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Yiyong Xiao, Yue Xie, Sadan Kulturel-Konak, Abdullah Konak
      Facility layout problems (FLPs) are quite common and important in many industries. This paper presents a mixed integer linear programming (MILP) model for the dynamic facility layout problem, which is a generalization of several special cases of FLPs studied in recent years. A new evolutionary meta-heuristic framework, named as the problem evolution algorithm (PEA), is developed as a general solution approach for FLPs. Computational experiments show that the PEA combined with the linear programming (LP), called PEA-LP in short, performs well in various types of FLPs. In addition, a new polyhedral inner-approximation method is proposed based on secant lines for the linearization of the non-linear constraint for department area requirements. This new method guarantees that the actual department area is always greater than or equal to the required area within a given maximum deviation error. Furthermore, two new symmetry-breaking constraints which help to improve the computational efficiency of the MILP model are also introduced. Computational experiments on several well-known problem instances from the literature are carried out to test the DFLP-FZ and the PEA-LP with promising results.

      PubDate: 2017-07-23T04:08:11Z
       
  • Computational comparison of several greedy algorithms for the minimum cost
           perfect matching problem on large graphs
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Sanne Wøhlk, Gilbert Laporte
      The aim of this paper is to computationally compare several algorithms for the Minimum Cost Perfect Matching Problem on an undirected complete graph. Our work is motivated by the need to solve large instances of the Capacitated Arc Routing Problem (CARP) arising in the optimization of garbage collection in Denmark. Common heuristics for the CARP involve the optimal matching of the odd-degree nodes of a graph. The algorithms used in the comparison include the CPLEX solution of an exact formulation, the LEDA matching algorithm, a recent implementation of the Blossom algorithm, as well as six constructive heuristics. Our results show that two of the constructive heuristics consistently exhibit the best behavior compared with the other four.

      PubDate: 2017-07-23T04:08:11Z
       
  • Competitive Liner Shipping Network Design
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Christian Vad Karsten, Berit Dangaard Brouer, David Pisinger
      We present a solution method for the liner shipping network design problem which is a core strategic planning problem faced by container carriers. We propose the first practical algorithm which explicitly handles transshipment time limits for all demands. Individual sailing speeds at each service leg are used to balance sailing speed against operational costs, hence ensuring that the found network is competitive on both transit time and cost. We present a matheuristic for the problem where a MIP is used to select which ports should be inserted or removed on a route. Computational results are presented showing very promising results for realistic global liner shipping networks. Due to a number of algorithmic enhancements, the obtained solutions can be found within the same time frame as used by previous algorithms not handling time constraints. Furthermore, we present a sensitivity analysis on fluctuations in bunker price which confirms the applicability of the algorithm.

      PubDate: 2017-07-23T04:08:11Z
       
  • Modeling and Computational Optimization for Complex System Management
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Le Thi Hoai An, Pham Dinh Tao


      PubDate: 2017-07-23T04:08:11Z
       
  • DC programming and DCA for solving Brugnano–Casulli piecewise linear
           systems
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Tao Pham Dinh, Vinh Thanh Ho, Hoai An Le Thi
      Piecewise linear optimization is one of the most frequently used optimization models in practice, such as transportation, finance and supply-chain management. In this paper, we investigate a particular piecewise linear optimization that is optimizing the norm of piecewise linear functions (NPLF). Specifically, we are interested in solving a class of Brugnano–Casulli piecewise linear systems (PLS), which can be reformulated as an NPLF problem. Speaking generally, the NPLF is considered as an optimization problem with a nonsmooth, nonconvex objective function. A new and efficient optimization approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) is developed. With a suitable DC formulation, we design a DCA scheme, named ℓ1-DCA, for the problem of optimizing the ℓ1-norm of NPLF. Thanks to particular properties of the problem, we prove that under some conditions, our proposed algorithm converges to an exact solution after a finite number of iterations. In addition, when a nonglobal solution is found, a numerical procedure is introduced to find a feasible point having a smaller objective value and to restart ℓ1-DCA at this point. Several numerical experiments illustrate these interesting convergence properties. Moreover, we also present an application to the free-surface hydrodynamic problem, where the correct numerical modeling often requires to have the solution of special PLS, with the aim of showing the efficiency of the proposed method.

      PubDate: 2017-07-23T04:08:11Z
       
  • Algorithms for unconstrained global optimization of nonlinear (polynomial)
           programming problems: The single and multi-segment polynomial B-spline
           approach
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): D.D. Gawali, Ahmed Zidna, P.S.V. Nataraj
      We investigate the use of the polynomial B-spline form for unconstrained global optimization of multivariate polynomial nonlinear programming problems. We use the B-spline form for higher order approximation of multivariate polynomials. We first propose a basic algorithm for global optimization that uses several accelerating algorithms such as cut-off test and monotonicity test. We then propose an improved algorithm consisting of several additional ingredients, such as a new subdivision point selection rule and a modified subdivision direction selection rule. The performances of the proposed basic and improved algorithms are tested and compared on a set of 14 test problems under two test conditions. The results of the tests show the superiority of the improved algorithm with multi-segment B-spline over that of the single segment B-spline, in terms of the chosen performance metrics. We also compare the quality of the set of all global minimizers found using the proposed algorithms (basic & improved) with those using well-known solvers BARON and Gloptipoly, on a smaller set of four test problems. The problems in the latter set have multiple global minimizers. The results show the superiority of the proposed algorithms, in that they are able to capture all the global minimizers, whereas Gloptipoly and BARON fail to do so in some of the test problems.

      PubDate: 2017-07-23T04:08:11Z
       
  • A clique covering MIP model for the irregular strip packing problem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Marcos Okamura Rodrigues, Franklina M.B. Toledo
      The irregular strip packing problem consists in the cutting of a set of two-dimensional pieces from an object of fixed width using the minimum possible length. Despite its economic importance for many industries, few exact studies have addressed this problem. Recently, a mixed integer programming model in which pieces are placed on a grid has been proposed. Although the model has proved the optimality for some large instances, it has a large number of non-overlap constraints, which grows quickly according to the discretization resolution and number of distinct pieces. This paper proposes a clique covering model to reduce the number of constraints and improve the linear relaxation. The model has outperformed the previous model in most evaluated instances and obtained an optimal solution for instances with up to 25 pieces (22 distinct pieces) subject to grid discretization.

      PubDate: 2017-07-23T04:08:11Z
       
  • DC programming and DCA for enhancing physical layer security via
           cooperative jamming
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Thi Thuy Tran, Hoai An Le Thi, Tao Pham Dinh
      The explosive development of computational tools these days is threatening security of cryptographic algorithms, which are regarded as primary traditional methods for ensuring information security. The physical layer security approach is introduced as a method for both improving confidentiality of the secret key distribution in cryptography and enabling the data transmission without relaying on higher-layer encryption. In this paper, the cooperative jamming paradigm - one of the techniques used in the physical layer is studied and the resulting power allocation problem with the aim of maximizing the sum of secrecy rates subject to power constraints is formulated as a nonconvex optimization problem. The objective function is a so-called DC (Difference of Convex functions) function, and some constraints are coupling. We propose a new DC formulation and develop an efficient DCA (DC Algorithm) to deal with this nonconvex program. The DCA introduces the elegant concept of approximating the original nonconvex program by a sequence of convex ones: at each iteration of DCA requires solution of a convex subproblem. The main advantage of the proposed approach is that it leads to strongly convex quadratic subproblems with separate variables in the objective function, which can be tackled by both distributed and centralized methods. One of the major contributions of the paper is to develop a highly efficient distributed algorithm to solve the convex subproblem. We adopt the dual decomposition method that results in computing iteratively the projection of points onto a very simple structural set which can be determined by an inexpensive procedure. The numerical results show the efficiency and the superiority of the new DCA based algorithm compared with existing approaches.

      PubDate: 2017-07-23T04:08:11Z
       
  • An exact decomposition method to save trips in cooperative pickup and
           delivery based on scheduled trips and profit distribution
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Yang Yu, Qi Lou, Jiafu Tang, Junwei Wang, XiaoHang Yue
      Compared to the non-cooperative mode, the cooperative mode is a powerful way to reduce operational cost in pickup and delivery service. In order to protect business sensitive information, sometimes participants are unwilling to open the customer's detailed information. Thus, we utilize the publishable trip scheduled results to compute the saved trips brought by cooperation. A mathematical model minimizing trips of cooperation is proposed. To obtain the exact solution, we define the cooperative trip set. We prove that only when cooperative trip set exists it is possible to save trips by cooperation. For a two-trip cooperative trip set, we exactly obtain the saved trips by enumerating all feasible cooperative cases. For a K-trip cooperative trip set, we propose an exact method to obtain the saved trips by decomposing it to at most K-1 two-trip cooperative trip sets. Computational complexity of the based-on-decomposition exact algorithm is O(N), where N is the total number of trips. Using the based-on-decomposition algorithm, we calculate the exact Shapley value to distribute profit. To empirically verify the exact method, we perform the extensive experiment cases of the real cooperative pickup and delivery service, i.e., “picking up and delivering customers to airport service” (PDCA).
      Graphical abstract image

      PubDate: 2017-07-23T04:08:11Z
       
  • Mathematical models and solution approach for cross-training staff
           scheduling at call centers
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Gamze Kilincli Taskiran, Xinhui Zhang
      Call centers face demand variations over time across multiple service categories and typically employ a cross-trained workforce with flexible schedules to hedge against these fluctuations. In practice, it is often impossible to cross-train agents in each category, thus partial and limited cross-training are the norm. This adds another layer of complexity to determine the optimal mix of cross-trained workforce (on top of the shift and tour schedules) and has created a challenging problem in the optimization of staff schedules. To solve this problem to its fullest extent, an integer program that addresses cross-training, shift schedule, days off and break assignments across multiple service categories is proposed. The model is hard to solve and a two-phase sequential approach is developed. The first phase is to find the optimal mix of the workforce, i.e., the categories to be cross-trained and the time periods in which they are to be deployed; the second phase is a smaller staff scheduling model to find the composition of the workforce and to construct their weekly tours. For all the test cases, which are of practical sizes, the two-phase sequential approach provided better solutions than the solution of the original model with a state-of-the-art commercial solver subject to imposed time limits. Experimental results with data from a call center with nine categories clearly demonstrate the significance of cross-training. In fact, partial limited cross-training, where 30% of staff is cross-trained with two skills or 10% of staff is cross-trained with three skills, could result in considerable cost savings; however, these savings could diminish quickly with the increase of efficiency loss in secondary skills. Experiments also suggest that cross-training could be a more effective approach than part-time shifts to hedge against fluctuations across service categories.

      PubDate: 2017-07-23T04:08:11Z
       
  • A hybrid genetic approach for solving an integrated multi-objective
           operating room planning and scheduling problem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Rosita Guido, Domenico Conforti
      In this paper, we propose a multi-objective integer linear programming model aiming at efficiently planning and managing hospital operating room suites. By effectively exploiting a novel hybrid genetic solution approach, the devised optimization model is able to determine, in an integrated way, (i) the operating room time assigned to each surgical specialty, (ii) the operating room time assigned to each surgical team, (iii) the surgery admission planning and (iv) the surgery scheduling. The resulting Pareto frontiers provide a set of “optimal” solutions able to support hospital managers in efficiently orchestrating the involved resources and planning surgeons and surgeries. On this basis, the proposed solution framework could represent a suitable engine for the development of advanced and effective health care management decision support systems.

      PubDate: 2017-07-23T04:08:11Z
       
  • A Variable Neighborhood Search heuristic for the maximum ratio clique
           problem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Dominik Goeke, Mahdi Moeini, David Poganiuch
      Consider a graph in which every vertex has two non-negative weights. In this graph, the maximum ratio clique problem (MRCP) searches for a maximal clique that maximizes a fractional function defined by the ratio of the sums of vertex weights. It has been proved that MRCP is NP-hard and, consequently, it is difficult to solve MRCP by exact methods. Due to this fact, we present the first heuristic approach, i.e., a multi-start Variable Neighborhood Search (MS-VNS) algorithm. In order to verify the performance of our MS-VNS, we use standard instances and according to our observations, our MS-VNS approach provides high-quality solutions in a short computation time. Furthermore, on most of the instances, our algorithm outperforms the classical methods that have already been used for solving MRCP.

      PubDate: 2017-07-23T04:08:11Z
       
  • Network design in scarce data environment using moment-based
           distributionally robust optimization
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Hideaki Nakao, Siqian Shen, Zhihao Chen
      We consider a network design problem (NDP) under random demand with unknown distribution for which only a small number of observations are known. We design arc capacities in the first stage and optimize single-commodity network flows after realizing the demand in the second stage. The objective is to minimize the total cost of allocating arc capacities, flowing commodities, and penalty for unmet demand. We formulate a distributionally robust NDP (DR-NDP) by constructing an ambiguity set of the unknown demand distribution based on marginal moment information, to minimize the worst-case total cost over all possible distributions. Approximating polynomials with piecewise-linear functions, we reformulate DR-NDP as a mixed-integer linear program optimized via a cutting-plane algorithm. We test diverse network instances to compare DR-NDP with a stochastic programming approach, a deterministic benchmark model, and a robust NDP formulation. Our results demonstrate adequate robustness of optimal DR-NDP solutions and how they perform under varying demand, modeling parameter, network, and cost settings. The results highlight potential niche uses of DR-NDP in data-scarce contexts.

      PubDate: 2017-07-09T09:05:39Z
       
  • A capacitated hub location problem under hose demand uncertainty
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Merve Meraklı, Hande Yaman
      In this study, we consider a capacitated multiple allocation hub location problem with hose demand uncertainty. Since the routing cost is a function of demand and capacity constraints are imposed on hubs, demand uncertainty has an impact on both the total cost and the feasibility of the solutions. We present a mathematical formulation of the problem and devise two different Benders decomposition algorithms. We develop an algorithm to solve the dual subproblem using complementary slackness. In our computational experiments, we test the efficiency of our approaches and we analyze the effects of uncertainty. The results show that we obtain robust solutions with significant cost savings by incorporating uncertainty into our problem.

      PubDate: 2017-07-09T09:05:39Z
       
  • Solving a bi-objective unrelated parallel batch processing machines
           scheduling problem: A comparison study
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): B. Shahidi-Zadeh, R. Tavakkoli-Moghaddam, A. Taheri-Moghadam, I. Rastgar
      Nowadays in competitive markets, production organizations are looking to increase their efficiency and optimize manufacturing operations. In addition, batch processor machines (BPMs) are faster and cheaper to carry out operations; thus the performance of manufacturing systems is increased. This paper studies a production scheduling problem on unrelated parallel BPMs with considering the release time and ready time for jobs as well as batch capacity constraints. In unrelated parallel BPMs, modern machines are used in a production line side by side with older machines that have different purchasing costs; so this factor is introduced as a novel objective to calculate the optimum cost for purchasing various machines due to the budget. Thus, a new bi-objective mathematical model is presented to minimize the makespan (i.e., Cmax ), tardiness/earliness penalties and the purchasing cost of machines simultaneously. The presented model is first coded and solved by the ε-constraint‌ method. Because of the complexity of the NP-hard problem, exact methods are not able to optimally solve large-sized problems in a reasonable time. Therefore, we propose a multi-objective harmony search (MOHS) algorithm. the results are compared with the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective ant colony optimization algorithm (MOACO). To tune their parameters, the Taguchi method is used. The results are compared by five metrics that show the effectiveness of the proposed MOHS algorithm compared with the MOPSO, NSGA-II and MOACO. At last, the sensitivity of the model is analyzed on new parameters and impacts of each parameter are illustrated on bi- objective functions.
      Graphical abstract image

      PubDate: 2017-07-09T09:05:39Z
       
  • The Ring Spur Assignment Problem: New formulation, valid inequalities and
           a branch-and-cut approach
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Rahimeh Neamatian Monemi, Shahin Gelareh
      A new mathematical model is proposed for the Ring Spur Assignment Problem (RSAP) that arises in the design of next-generation telecommunication networks. In this problem, every node of the network lies either on a ring among a set of bounded disjoint local rings or is spurred by a single arc to another node on a local ring. A special ring, called a tertiary ring, interconnects the local rings. Our new integer programming model employs only O ( n 2 ) variables and has a stronger LP relaxation. Several classes of valid inequalities and corresponding separation procedures are presented giving rise to an efficient branch-and-cut solution algorithm. We report optimal solutions for all SNDLib instances including those that have not previously been solved to optimality.

      PubDate: 2017-07-09T09:05:39Z
       
  • Empirical analysis for the VRPTW with a multigraph representation for the
           road network
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Hamza Ben Ticha, Nabil Absi, Dominique Feillet, Alain Quilliot
      Vehicle routing problems have drawn researchers’ attention for more than fifty years. Most approaches found in the literature are based on the key assumption that for each pair of points of interest (e.g., customers, depot...), the best origin-destination path can be computed. Thus, the problem can be addressed via a simple graph representation, where nodes represent points of interest and arcs represent the best paths. Yet, in practice, it is common that several attributes are defined on road segments. Consequently, alternative paths presenting different trade-offs exist between points of interest. In this study, we investigate in depth a special representation of the road network proposed in the literature and called a multigraph. This representation enables one to maintain all these alternative paths in the solution space. We present an empirical analyses based on the Vehicle Routing Problem with Time Windows, as a test bed problem, solved with branch-and-price algorithms developed for the different types of graphs. Computational experiments on modified benchmarks from the literature and on instances derived from real data evaluate the impact of the modeling on solution quality.

      PubDate: 2017-07-09T09:05:39Z
       
  • An integrated replenishment and production control policy under inventory
           inaccuracy and time-delay
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Ming Li, Zheng Wang
      Inventory inaccuracy has a significant negative impact on the performance of raw materials replenishment and production control. It usually leads to high inventory holding cost or large backlog penalty. To hedge against it, we investigate a replenishment and production control problems for a multiple machines and multiple product-types production/inventory system with inventory inaccuracy. The objective is to minimize the average production cost, including the inventory holding cost and the backlog penalty. In addition to inventory inaccuracy, the lead-time of raw materials replenishment and the unreliability of machines are also taken into consideration. In light of the principle of dynamic programming, a simplified optimal replenishment and production control policy is constructed based on the assumption that the records of inventories are completely accurate. To overcome the shortcomings of the simplified policy in hedging against inventory inaccuracy, a conditional expectation-based replenishment and production control policy is developed based on the fundamental structure of the simplified optimal replenishment and production control policy, and the conditional probability of the physical inventory level. Numerical experiments are conducted to examine the performance of the proposed policy for hedging against inventory inaccuracy, and sensitivity analysis is carried out to study how the parameters of the production/inventory system and the proposed policy affect the average production cost.

      PubDate: 2017-07-09T09:05:39Z
       
  • An adaptive large neighborhood search for the full truckload pickup and
           delivery problem with resource synchronization
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Axel Grimault, Nathalie Bostel, Fabien Lehuédé
      This paper presents the Full Truckload Pickup and Delivery Problem with Resource Synchronization (FT-PDP-RS). It consists of optimizing the transport of materials between sites, using a heterogeneous fleet of trucks, in the context of public works. Full truckload pickup and delivery requests have to be served within time windows. Trucks are synchronized on pickup or delivery locations based on unitary loading and unloading resources. We propose an Adaptive Large Neighborhood Search (ALNS) to solve this problem. Custom destroy and repair operators and an efficient feasibility insertion procedure have been designed to solve it. The method has been evaluated on real instances from the literature and on real case instances from a public works company. Computational experiments confirm the efficiency of the method.

      PubDate: 2017-06-28T22:14:08Z
       
  • A distance-limited continuous location-allocation problem for spatial
           planning of decentralized systems
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Kagan Gokbayrak, Ayse Selin Kocaman
      We introduce a new continuous location-allocation problem where the facilities have both a fixed opening cost and a coverage distance limitation. The problem has wide applications especially in the spatial planning of water and/or energy access networks where the coverage distance might be associated with the physical loss constraints. We formulate a mixed integer quadratically constrained problem (MIQCP) under the Euclidean distance setting and present a three-stage heuristic algorithm for its solution: In the first stage, we solve a planar set covering problem (PSCP) under the distance limitation. In the second stage, we solve a discrete version of the proposed problem where the set of candidate locations for the facilities is formed by the union of the set of demand points and the set of locations in the PSCP solution. Finally, in the third stage, we apply a modified Weiszfeld’s algorithm with projections that we propose to incorporate the coverage distance component of our problem for fine-tuning the discrete space solutions in the continuous space. We perform numerical experiments on three example data sets from the literature to demonstrate the performance of the suggested heuristic method.

      PubDate: 2017-06-28T22:14:08Z
       
  • Integrating dock-door assignment and vehicle routing with cross-docking
    • Abstract: Publication date: December 2017
      Source:Computers & Operations Research, Volume 88
      Author(s): Furkan Enderer, Claudio Contardo, Ivan Contreras
      This paper presents an integrated cross-dock door assignment and vehicle routing problem arising in the operation of cross-dock terminals. It consists of assigning origins to inbound doors, transferring commodities between doors, and routing vehicles from outbound doors to destinations. The objective is to jointly minimize the total material handling and transportation costs. Two formulations of the problem are presented and computationally compared. In addition, we develop a column generation algorithm based on the most promising formulation and a heuristic to obtain lower and upper bounds for the optimal solution of the problem, respectively. Numerical results on a set of benchmark instances with up to 20 origins and 50 destinations confirm the efficiency of the proposed solution algorithms.

      PubDate: 2017-06-28T22:14:08Z
       
  • An exact algorithm for Min-Max hyperstructure equipartition with a
           connected constraint
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Tunzi Tan, Suixiang Gao, Juan A. Mesa
      Hyperstructure is a topological concept that shares characteristics with both graphs and hypergraphs. The Min-Max hyperstructure equipartition with a connected constraint problem consists in partitioning a hyperstructure into K equal-sized connected parts that minimizes the maximum load in each part (the number of hyperedges assigned to each part). This problem, proved to be NP-hard, is an integer nonlinear programming problem. The linearized version of this problem has been introduced. Shrink and Cut algorithm is designed to simplify original complex hyperstructure without changing the optimal solution of the Min-Max hyperstructure equipartition with a connected constraint problem. By the use of this algorithm, Min-Max hyper-tree equipartition with a connected constraint can be solved in polynomial time. An exact algorithm: Min-Max hyperstructure partitioning algorithm, based on a Shrink and Cut algorithm and algorithm S for finding all the spanning trees, is designed to solve ordinary cases, which shows well on experimental results.

      PubDate: 2017-06-28T22:14:08Z
       
  • A multi-phase heuristic for the production routing problem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Oğuz Solyalı, Haldun Süral
      This study considers the production routing problem where a plant produces and distributes a single item to multiple retailers over a multi-period time horizon. The problem is to decide on when and how much to produce and stock at the plant, when and how much to serve and stock at each retailer, and vehicle routes for shipments such that the sum of fixed production setup cost, variable production cost, distribution cost, and inventory carrying cost at the plant and retailers is minimized. A multi-phase heuristic is proposed for the problem. The proposed heuristic is a mathematical programming-based heuristic that relies on formulating and solving restricted versions of the problem as mixed integer programs. The computational experiments on benchmark instances show favorable results with regard to the quality of the solutions found at the expense of higher computing times on large instances. In particular, the heuristic managed to find new best solutions for the 65% of benchmark instances.

      PubDate: 2017-06-20T21:50:53Z
       
  • Lagrangian relaxation for SVM feature selection
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): M. Gaudioso, E. Gorgone, M. Labbé, A.M. Rodríguez-Chía
      We discuss a Lagrangian-relaxation-based heuristics for dealing with feature selection in the Support Vector Machine (SVM) framework for binary classification. In particular we embed into our objective function a weighted combination of the L 1 and L 0 norm of the normal to the separating hyperplane. We come out with a Mixed Binary Linear Programming problem which is suitable for a Lagrangian relaxation approach. Based on a property of the optimal multiplier setting, we apply a consolidated nonsmooth optimization ascent algorithm to solve the resulting Lagrangian dual. In the proposed approach we get, at every ascent step, both a lower bound on the optimal solution as well as a feasible solution at low computational cost. We present the results of our numerical experiments on some benchmark datasets.

      PubDate: 2017-06-20T21:50:53Z
       
  • Expressiveness and robustness measures for the evaluation of an additive
           value function in multiple criteria preference disaggregation methods: An
           experimental analysis
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Miłosz Kadziński, Mohammad Ghaderi, Jakub Wąsikowski, Núria Agell
      An additive value function is one of the prevailing preference models in Multiple Criteria Decision Aiding (MCDA). Its indirect elicitation through pairwise questions is often applied due to lowering the cognitive effort on the part of a Decision Maker (DM). A practical usefulness of this approach is influenced by both expressiveness of the assumed model and robustness of the recommendation computed with its use. We experimentally evaluate the above characteristics in view of using an additive value function in the preference disaggregation context. The simulation results are quantified with the following four measures: (1) the share of decision scenarios for which a set of compatible value functions is non-empty, (2) the minimal difference between comprehensive values of reference alternatives compared pairwise by the DM, (3) the number of pairs of alternatives for which the necessary preference relation confirmed by all compatible functions holds, and (4) the number of non-trivial certain inferences which cannot be derived directly from the preference information. We discuss how these measures are influenced by the settings with different numbers of alternatives, criteria, pairwise comparisons, and performance distributions. We also study how the results change when applying various procedures for selection of the characteristic points which define the shape of per-criterion marginal value functions. In this regard, we compare four existing discretization algorithms with a new supervised technique proposed in this paper. Overall, we indicate that expressiveness and robustness are contradictory objectives, and a compromise between them needs to be reached to increase the usefulness of an additive value model in the preference disaggregation methods.

      PubDate: 2017-06-20T21:50:53Z
       
  • A decomposition based hybrid heuristic algorithm for the joint passenger
           and freight train scheduling problem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Liang Liu, Maged Dessouky
      We study the joint problem of scheduling passenger and freight trains for complex railway networks, where the objective is to minimize the tardiness of passenger trains at station stops and the delay of freight trains. We model the problem as a mixed integer program and propose a two-step decomposition heuristic to solve the problem. The heuristic first vertically decomposes the train schedules into a passenger train scheduling phase and then a freight train scheduling phase. In the freight train scheduling phase, we use a train-based decomposition to iteratively schedule each freight train. Experimental results show the efficiency and quality of the proposed heuristic algorithm on real world size problems.

      PubDate: 2017-06-20T21:50:53Z
       
  • A variable neighborhood search and simulated annealing hybrid for the
           profile minimization problem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Gintaras Palubeckis
      Given an undirected simple graph G, the profile minimization problem (PMP) is to find an ordering of the vertices of the graph G such that the sum of the profiles of all its vertices is minimized. The profile of the vertex v in position i is defined as max { 0 , i − h v } , where hv is the position of the leftmost vertex among all vertices adjacent to v in G. We propose an approach for the PMP, which combines a variable neighborhood search (VNS) scheme with the multi-start simulated annealing (MSA) technique. The solution delivered by MSA is submitted as input to the VNS component of the method. The VNS algorithm heavily relies on a fast insertion neighborhood exploration procedure. We show that the time complexity of this procedure is O(n 2), where n is the order of G. We have found empirically that it is advantageous to give between 50 and 75% of the computation time to MSA and the rest to VNS. The results of the computational experiments demonstrate the superiority of our MSA-VNS algorithm over the current state-of-the-art metaheuristic approaches for the PMP. Using MSA-VNS, we improved the best known solutions for 50 well-recognized benchmark PMP instances in the literature. The source code implementing MSA-VNS is made publicly available as a benchmark for future comparisons.

      PubDate: 2017-06-11T13:05:47Z
       
  • M/G/c/c state dependent queuing model for a road traffic system of two
           sections in tandem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Nacira Guerouahane, Djamil Aissani, Nadir Farhi, Louiza Bouallouche-Medjkoune
      We propose in this article a M/G/c/c state dependent queuing model for road traffic flow. The model is based on finite capacity queuing theory which captures the stationary density-flow relationships. It is also inspired from the deterministic Godunov scheme for the road traffic simulation. We first present a reformulation of the existing linear case of M/G/c/c state dependent model, in order to use flow rather than speed variables. We then extend this model in order to consider upstream traffic demand and downstream traffic supply. After that, we propose the model for two road sections in tandem where both sections influence each other. In order to deal with this mutual dependence, we solve an implicit system given by an algebraic equation. Finally, we derive some performance measures (throughput and expected travel time). A comparison with results predicted by the M/G/c/c state dependent queuing networks shows that the model we propose here captures really the dynamics of the road traffic.

      PubDate: 2017-06-11T13:05:47Z
       
  • Airline flight schedule planning under competition
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Ahmed Abdelghany, Khaled Abdelghany, Farshid Azadian
      This paper presents a modeling framework for airline flight schedule planning under competition. The framework generates an operational flight timetable that maximizes the airline's revenue, while ensuring efficient utilization of the airline's resources (e.g. aircraft and crew). It explicitly considers passenger demand shift due to the network-level competition with other airlines. It also considers minimizing the needless ground time of the resources. The problem is formulated in the form of a bi-level mathematical program where the upper level represents the airline scheduling decisions, while the lower level captures passenger responses in terms of itinerary choices. A solution methodology is developed which integrates a meta- heuristic search algorithm, a network competition analysis model, and a resource (e.g. aircraft and crew) tracking model. The performance of the framework is evaluated through several experiments to develop the schedule for a major U.S. airline. The results demonstrate the success of the framework to develop a competitive schedule with efficient resources.

      PubDate: 2017-06-01T15:54:32Z
       
  • A stochastic model for the throughput analysis of passing dual yard cranes
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Suruchika Saini, Debjit Roy, René de Koster
      New container terminal technologies such as passing dual yard cranes per stack promise increase in stacking throughput capacity. However, dual yard cranes can interfere, which reduces the cranes’ throughput capacity. Using crane operational protocols, we develop a stochastic model for two passing dual yard cranes and obtain closed-form expressions for the crane throughput capacity with interference delays. We then develop an approximate model to estimate the expected throughput times for both balanced and unbalanced stack configuration. A detailed discrete-event simulation is built to validate the analytical model. We show that interference effects between the cranes can reduce the crane throughput capacity by an average of 35% and interference delays increase with an increase in the number of bays in the stack. We use the model to develop operational insights.

      PubDate: 2017-06-01T15:54:32Z
       
  • Large-scale vehicle routing problems: Quantum Annealing, tunings and
           results
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): A. Syrichas, A. Crispin
      Quantum Annealing was previously applied to the vehicle routing problem and the results were promising. For all benchmark instances in the study, optimal results were obtained. However, 100% success rate was not achieved in every case, and tuning the control parameters for larger instances proved cumbersome. This work addresses these remaining difficulties. An empirical approach is taken wherein measurements of run-time behaviour are exploited to transform existing good values of control parameters so that they can be used successfully for other problem instances. The course of this work shows a method which simplifies hand-tuning so that the heuristic performs successfully when applied to larger instances, and also demonstrates a tuning method which establishes control parameter values for instances which belong in broadly defined groupings. In addition, new best known solutions for large-scale instances, and initial results for the distance-constrained variant of the vehicle routing problem are presented.

      PubDate: 2017-06-01T15:54:32Z
       
  • A Relax-and-Cut framework for large-scale maximum weight connected
           subgraph problems
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Eduardo Álvarez-Miranda, Markus Sinnl
      Finding maximum weight connected subgraphs within networks is a fundamental combinatorial optimization problem both from theoretical and practical standpoints. One of the most prominent applications of this problem appears in Systems Biology and it corresponds to the detection of active subnetworks within gene interaction networks. Due to its importance, several modeling and algorithmic strategies have been proposed for tackling the maximum weight connected subgraph problem (MWCS) over the last years; the most effective strategies typically depend on the use of integer linear programming (ILP). Nonetheless, this implies that large-scale networks (such as those appearing in Systems Biology) can become burdensome; moreover, not all practitioners may have access to an ILP solver. In this paper, a unified modeling and algorithmic scheme is designed to solve the MWCS and some of its application-oriented variants with cardinality-constraints or budget-constraints. The proposed framework is based on a general node-based model which is tackled by a Relax-and-Cut scheme, i.e., Lagrangian relaxation combined with constraint generation; this yields a heuristic procedure capable of providing both dual and primal bounds. The approach is enhanced by additional valid inequalities, lifted valid inequalities, primal heuristics and variable-fixing procedures. Computational results on instances from the literature, as well as on additional large-scale instances, show that the proposed framework is competitive with respect to the existing approaches and it allows to find improved solutions for some unsolved instances from literature. The effect of initializing a Branch-and-Cut approach with information from the Relax-and-Cut is also investigated. The implemented approach is made available online.

      PubDate: 2017-06-01T15:54:32Z
       
  • GLNS: An effective large neighborhood search heuristic for the Generalized
           Traveling Salesman Problem
    • Abstract: Publication date: November 2017
      Source:Computers & Operations Research, Volume 87
      Author(s): Stephen L. Smith, Frank Imeson
      This paper presents a new solver for the exactly one-in-a-set Generalized Traveling Salesman Problem (GTSP). In the GTSP, we are given as input a complete directed graph with edge weights, along with a partition of the vertices into disjoint sets. The objective is to find a cycle (or tour) in the graph that visits each set exactly once and has minimum length. In this paper we present an effective algorithm for the GTSP based on adaptive large neighborhood search. The algorithm operates by repeatedly removing from, and inserting vertices in, the tour. We propose a general insertion mechanism that contains as special cases the well-known nearest, farthest and random insertion mechanisms. We provide extensive benchmarking results for our solver in comparison to the state-of-the-art on a wide range of existing and new problem libraries. We show that on the GTSP-LIB library, the proposed algorithm is competitive with the best known algorithms. On several other libraries, we show that given the same amount of time, the proposed solver finds higher quality solutions than existing approaches, particularly on harder instances that are non-metric and/or whose sets are not clustered.

      PubDate: 2017-05-27T07:30:00Z
       
  • Completion time variance minimisation on two identical parallel processors
    • Abstract: Publication date: October 2017
      Source:Computers & Operations Research, Volume 86
      Author(s): B. Srirangacharyulu
      The problem of scheduling jobs to minimise completion time variance (CTV) is a well-known problem in scheduling research. CTV is categorized as a non-regular performance measure and its value may decrease by increasing the job completion times. This objective is relevant in situations where providing uniform service to customers is important, and is in-line with just-in-time philosophy. The problem concerned in this paper is to schedule n jobs on two identical parallel machines to minimise CTV. We consider the unrestricted version of the problem. The problem is said to be restricted when a machine is not allowed to remain idle when jobs are available for processing. It may be necessary to delay the start of job processing on a machine in order to reduce the completion time deviations. This gives rise to the unrestricted version of the problem. We discuss several properties of an optimal schedule to the problem. In this paper, we develop a lower bound on CTV for a known partial schedule and propose a branch and bound algorithm to solve the problem. Optimal solutions are obtained and results are reported.

      PubDate: 2017-05-17T07:16:43Z
       
  • An adaptive large neighborhood search metaheuristic for agile satellite
           scheduling with time-dependent transition time
    • Abstract: Publication date: October 2017
      Source:Computers & Operations Research, Volume 86
      Author(s): Xiaolu Liu, Gilbert Laporte, Yingwu Chen, Renjie He
      Agile satellites belong to the new generation of satellites with three degrees of freedom for acquiring images on the Earth. As a result, they have longer visible time windows for the requested targets. An image shot can be conducted at any time in the window if and only if the time left is sufficient for the fulfillment of the imaging process. For an agile satellite, a different observation time means a different image angle, thus defining a different transition time from its neighboring tasks. Therefore, the setup time for each imaging process depends on the selection of its observation start time, making the problem a time-dependent scheduling problem. To solve it, we develop a metaheuristic, called adaptive large neighborhood search (ALNS), thus creating a conflict-free observational timeline. ALNS is a local search framework in which a number of simple operators compete to modify the current solution. In our ALNS implementation, we define six removal operators and three insertion operators. At each iteration, a pair of operators is selected to destroy the current solution and generate a new solution with a large collection of variables modified. Time slacks are introduced to confine the propagation of the time-dependent constraint of transition time. Computational experiments show that the ALNS metaheuristic performs effectively, fulfilling more tasks with a good robustness.

      PubDate: 2017-05-12T08:55:49Z
       
  • An improved Ant Colony System for the Sequential Ordering Problem
    • Abstract: Publication date: October 2017
      Source:Computers & Operations Research, Volume 86
      Author(s): Rafał Skinderowicz
      It is not rare that the performance of one metaheuristic algorithm can be improved by incorporating ideas taken from another. In this article we present how Simulated Annealing (SA) can be used to improve the efficiency of the Ant Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering Problem (SOP). Moreover, we show how the very same ideas can be applied to improve the convergence of a dedicated local search, i.e. the SOP-3-exchange algorithm. A statistical analysis of the proposed algorithms both in terms of finding suitable parameter values and the quality of the generated solutions is presented based on a series of computational experiments conducted on SOP instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed ACS-SA and EACS-SA algorithms often generate solutions of better quality than the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with the proposed SOP-3-exchange-SA local search was able to find 10 new best solutions for the SOP instances from the SOPLIB2006 repository, thus improving the state-of-the-art results as known from the literature. Overall, the best known or improved solutions were found in 41 out of 48 cases.

      PubDate: 2017-05-07T01:02:00Z
       
  • Exact computational solution of Modularity Density Maximization by
           effective column generation
    • Abstract: Publication date: October 2017
      Source:Computers & Operations Research, Volume 86
      Author(s): Rafael de Santiago, Luís C. Lamb
      Modularity Density Maximization (MDM) is associated with clustering problems in networks. MDM is an alternative to the resolution limit issue of the Modularity Maximization problem. Several reports in the literature have described mathematical programming models for MDM which solve instances of at most 40 nodes. In this context, this paper presents six new column generation methods that find exact solutions for the MDM problem. These methods use exact and auxiliary heuristic problems and an initial variable generator. Our methods show clear improvements over current results. Comparisons between our proposed methods and state-of-the-art algorithms are also carried out. Our results show that (i) two of our methods surpass the exact state-of-the-art algorithms in terms of time, and (ii) our methods provide optimal values for larger instances than current approaches can tackle.

      PubDate: 2017-05-07T01:02:00Z
       
  • Improving electromagnetism algorithm for solving resource allocation
           problem in stochastic networks
    • Abstract: Publication date: October 2017
      Source:Computers & Operations Research, Volume 86
      Author(s): Weng-Ming Chu, Koan-Yuh Chang
      This study investigates optimal resource allocation for minimizing total cost in stochastic networks (SN) where the duration of all activities involved is not only a random variable, but also a function of the resources allocated. The total cost of the network comprises resource usage cost and penalty cost. An Electromagnetism Algorithm (EA) is used as a decision tool for optimization and a Label-Correcting Tracing Algorithm (LCTA) for approximation of completion time in SN is suggested. Furthermore, the Critical Path Cluster Algorithm (CPCA) and Cluster Local Search Algorithm (CLSA) are developed to enhance EA's search ability for resource allocation. Results from numerical experiments show that the proposed EA yields good solution quality.

      PubDate: 2017-05-07T01:02:00Z
       
 
 
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