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  Subjects -> BUSINESS AND ECONOMICS (Total: 3156 journals)
    - ACCOUNTING (94 journals)
    - BANKING AND FINANCE (270 journals)
    - BUSINESS AND ECONOMICS (1162 journals)
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    - PRODUCTION OF GOODS AND SERVICES (137 journals)
    - PUBLIC FINANCE, TAXATION (35 journals)
    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals)

BUSINESS AND ECONOMICS (1162 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: 10)
Abacus     Hybrid Journal   (Followers: 12)
Accounting Forum     Hybrid Journal   (Followers: 25)
Acta Amazonica     Open Access   (Followers: 5)
Acta Commercii     Open Access   (Followers: 4)
Acta Oeconomica     Full-text available via subscription   (Followers: 2)
Acta Scientiarum. Human and Social Sciences     Open Access   (Followers: 7)
Acta Universitatis Danubius. Œconomica     Open Access   (Followers: 3)
Acta Universitatis Nicolai Copernici Zarządzanie     Open Access   (Followers: 4)
AD-minister     Open Access   (Followers: 3)
ADR Bulletin     Open Access   (Followers: 6)
Advances in Developing Human Resources     Hybrid Journal   (Followers: 23)
Advances in Economics and Business     Open Access   (Followers: 11)
AfricaGrowth Agenda     Full-text available via subscription   (Followers: 1)
African Affairs     Hybrid Journal   (Followers: 60)
African Development Review     Hybrid Journal   (Followers: 33)
African Journal of Business and Economic Research     Full-text available via subscription   (Followers: 2)
African Journal of Business Ethics     Open Access   (Followers: 6)
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: 5)
Alphanumeric Journal : The Journal of Operations Research, Statistics, Econometrics and Management Information Systems     Open Access   (Followers: 5)
American Economic Journal : Applied Economics     Full-text available via subscription   (Followers: 170)
American Journal of Business     Hybrid Journal   (Followers: 16)
American Journal of Business and Management     Open Access   (Followers: 53)
American Journal of Business Education     Open Access   (Followers: 10)
American Journal of Economics and Business Administration     Open Access   (Followers: 26)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 29)
American Journal of Evaluation     Hybrid Journal   (Followers: 13)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 20)
American Journal of Health Economics     Full-text available via subscription   (Followers: 13)
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: 27)
ANALES de la Universidad Central del Ecuador     Open Access   (Followers: 2)
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: 10)
Annual Review of Economics     Full-text available via subscription   (Followers: 31)
Applied Developmental Science     Hybrid Journal   (Followers: 3)
Applied Economics     Hybrid Journal   (Followers: 47)
Applied Economics Letters     Hybrid Journal   (Followers: 30)
Applied Economics Quarterly     Full-text available via subscription   (Followers: 10)
Applied Financial Economics     Hybrid Journal   (Followers: 23)
Applied Mathematical Finance     Hybrid Journal   (Followers: 8)
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: 6)
Arena Journal     Full-text available via subscription   (Followers: 1)
Argomenti. Rivista di economia, cultura e ricerca sociale     Open Access   (Followers: 3)
ASEAN Economic Bulletin     Full-text available via subscription   (Followers: 5)
Asia Pacific Business Review     Hybrid Journal   (Followers: 6)
Asia Pacific Journal of Human Resources     Hybrid Journal   (Followers: 325)
Asia Pacific Viewpoint     Hybrid Journal   (Followers: 1)
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: 14)
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: 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 Sustainability and Social Responsibility     Open Access   (Followers: 1)
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: 10)
Australasian Journal of Regional Studies, The     Full-text available via subscription   (Followers: 1)
Australian Cottongrower, The     Full-text available via subscription   (Followers: 1)
Australian Economic Papers     Hybrid Journal   (Followers: 31)
Australian Economic Review     Hybrid Journal   (Followers: 6)
Australian Journal of Maritime and Ocean Affairs     Hybrid Journal   (Followers: 9)
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: 10)
Benefit : Jurnal Manajemen dan Bisnis     Open Access  
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: 4)
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: 34)
Brookings Papers on Economic Activity     Open Access   (Followers: 49)
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: 18)
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: 7)
Business Ethics: A European Review     Hybrid Journal   (Followers: 16)
Business Horizons     Hybrid Journal   (Followers: 6)
Business Information Review     Hybrid Journal   (Followers: 14)
Business Management and Strategy     Open Access   (Followers: 43)
Business Research     Hybrid Journal   (Followers: 2)
Business Strategy and the Environment     Hybrid Journal   (Followers: 14)
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: 59)
Cambridge Journal of Regions, Economy and Society     Hybrid Journal   (Followers: 10)
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: 29)
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: 15)
Case Studies in Business and Management     Open Access   (Followers: 9)
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: 17)
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: 18)
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: 8)
China Finance Review International     Hybrid Journal   (Followers: 5)
China Nonprofit Review     Hybrid Journal   (Followers: 3)
China perspectives     Open Access   (Followers: 12)
Chinese Economy     Full-text available via subscription  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
CLIO América     Open Access   (Followers: 1)
Cliometrica     Hybrid Journal   (Followers: 4)
COEPTUM     Open Access  
Community Development Journal     Hybrid Journal   (Followers: 24)
Compensation & Benefits Review     Hybrid Journal   (Followers: 7)
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: 6)
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: 12)
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: 5)
Corporate Communications An International Journal     Hybrid Journal   (Followers: 7)
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: 2)
Cuadernos de Economía     Open Access   (Followers: 2)
Cuadernos de Economia - Latin American Journal of Economics     Open Access   (Followers: 2)
Cuadernos de Estudios Empresariales     Open Access   (Followers: 2)
Current Opinion in Creativity, Innovation and Entrepreneurship     Open Access   (Followers: 10)
De Economist     Hybrid Journal   (Followers: 12)
Decision Analysis     Full-text available via subscription   (Followers: 10)
Decision Sciences     Hybrid Journal   (Followers: 18)
Decision Support Systems     Hybrid Journal   (Followers: 16)
Defence and Peace Economics     Hybrid Journal   (Followers: 17)
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]   [12 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0305-0548
   Published by Elsevier Homepage  [3118 journals]
  • An efficient quasi-physical quasi-human algorithm for packing equal
           circles in a circular container
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Kun He, Hui Ye, Zhengli Wang, Jingfa Liu
      We propose an efficient quasi-physical quasi-human (QPQH) algorithm for the equal circle packing problem. QPQH is based on our modified Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm, which we call the local BFGS, and a new basin-hopping strategy based on a Chinese proverb: alternate tension with relaxation. Starting from a random initial layout, we apply the local BFGS algorithm to reach a local minimum layout. The local BFGS algorithm fully utilizes the neighborhood information of each circle to considerably speed up the computation of the gradient descent process; this efficiency is very apparent for large-scale instances. When yielding a local minimum layout, the new basin-hopping strategy is used to shrink container sizes to different extents, to generate several new layouts. Experimental results indicate that the new basin-hopping strategy is very efficient, especially for layout types with comparatively dense packing in the center and comparatively sparse packing around the boundary of the container. We tested QPQH on instances in which n = 1 , 2 , ⋯ , 320 , and obtained 66 new layouts having smaller container sizes than the current best-known results reported in the literature.

      PubDate: 2017-12-26T16:27:29Z
       
  • A memetic algorithm to pack unequal circles into a square
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Zhi-Zhong Zeng, Xin-Guo Yu, Mao Chen, Yuan-Yuan Liu
      A circle packing problem involves packing given circles into a container. The objective is to minimize the size of the container without causing any overlap. This paper focuses on a representative circle packing problem where the circles are unequal and the container is a square, called Packing Unequal Circles into a Square (PUCS). It proposes a memetic algorithm to solve the problem. The memetic algorithm can be regarded as a combination of a genetic algorithm (GA) and an iterated local search (ILS). It is composed of a local search phase and a global transformation phase. The global transformation phase evolves a population; the local search phase optimizes the offsprings generated by the global transformation phase. The proposed approach exhibits several novel features in its global transformation phase, such as the z-crossover operator based on the symmetry of the container and the complementarity of the configuration, a perturbation operator inspired by gene-fragment-insertion, a reproduction selector based on the genetic relationships of the individuals, and a hybrid population updating strategy based on the diversity of the reproduction operators. Experimental results show that the memetic algorithm is effective for the problem.

      PubDate: 2017-12-26T16:27:29Z
       
  • The driver and vehicle routing problem
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Bencomo Domínguez-Martín, Inmaculada Rodríguez-Martín, Juan-José Salazar-González
      In the vehicle routing literature it is generally assumed that each vehicle is driven by a single driver from the beginning to the end of its route. We introduce a new vehicle routing problem without this assumption. We consider a problem with two depots in which vehicles must departure from one depot and arrive to the other, while drivers should leave and return to the same depot and their routes can not exceed a given duration. Under these conditions, changes of vehicle are mandatory for the drivers in order to go back to their base depots. These changes can only take place at some particular nodes. Moreover, vehicles and drivers must be synchronized. We model the problem as a vehicle routing problem with two depots and two types of routes, one for drivers and the other for vehicles. We propose a mixed integer programming formulation for the problem and design a branch-and-cut algorithm to solve it. Computational results show that the proposed approach can find optimal solutions for instances with up to 30 nodes.

      PubDate: 2017-12-26T16:27:29Z
       
  • Optimization and approximation methods for dynamic appointment scheduling
           with patient choices
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Jin Wang, Youhua (Frank) Chen, Minghui Xu
      A well-designed appointment scheduling system in healthcare should take into account patient choices in order to improve patient satisfaction. A dynamic programming model is proposed to decide which slots should be offered for patients to choose from. We characterize optimal offer sets with a simple form by using the notion of “complete set”, in which all slots with revenues higher than a certain value are offered. An approximate method making use of the complete-set policy is proposed for estimating the value associated with the system state. Experiments show that the complete-set policy is effective and efficient. The model is extended to handle a general appointment system, in which the reward depends on both patients and healthcare service providers. The complete-set policy continues to exhibit excellent performances.

      PubDate: 2017-12-26T16:27:29Z
       
  • Robust routing in deterministic delay-tolerant networks
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Ronan Bocquillon, Antoine Jouglet
      A system of systems is a set of heterogeneous independent systems that share data in pursuit of a common goal. These systems form a delay-/disruption-tolerant network (DTN), where routing is based on the store-carry-and-forward paradigm. Systems can communicate whenever they are close enough to each other, in what are called contacts. We assume that the movements of these systems may be predicted in advance and we consider that a sequence of contacts is given at the outset. During a contact, a given emitting system can transfer to a given receiving system a fixed amount of data (termed datum unit) that it has in its possession. The dissemination problem is to find a transfer plan such that all the data can be transferred from a given subset of source systems to a given subset of recipient systems. In this paper we study the problem where communications may fail. We propose an algorithm for finding a robust transfer plan that minimizes the dissemination length.

      PubDate: 2017-12-26T16:27:29Z
       
  • Large neighborhood search with constraint programming for a vehicle
           routing problem with synchronization constraints
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Hossein Hojabri, Michel Gendreau, Jean-Yves Potvin, Louis-Martin Rousseau
      This paper considers an extension of the vehicle routing problem with time windows, where the arrival of two vehicles at different customer locations must be synchronized. That is, one vehicle has to deliver some product to a customer, like a home theater system, while the crew on another vehicle must install it. This type of problem is often encountered in practice and is very challenging due to the interdependency among the vehicle routes, but has received little attention in the literature. A constraint programming-based adaptive large neighborhood search is proposed to solve this problem. The search abilities of the large neighborhood search and the constraint propagation abilities of constraint programming are combined to determine the feasibility of any proposed modification to the current solution. Numerical results are reported on instances derived from benchmark instances for the vehicle routing problem with time windows with up to 200 customers.

      PubDate: 2017-12-26T16:27:29Z
       
  • A two-step gradient estimation approach for setting supply chain operating
           parameters
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Philip M. Kaminsky, Stewart Liu
      In earlier work, we found retrospective optimization to be effective for setting policy parameters in supply chains with relatively simple structures. This method finds these parameters by solving an integer program over a single randomly generated sample path. Initial efforts to extend this methodology to more complex settings were in many cases too slow to be effective. In response to this, in this research we combine retrospective optimization over a relatively short time horizon with stochastic approximation gradient search algorithms, an approach that proves to be fast and effective. We compare this approach to retrospective optimization without gradient search on simple serial supply chains where the solution is known, and then use it for effective inventory positioning in more complex biopharmaceutical supply chains.

      PubDate: 2017-12-26T16:27:29Z
       
  • A matheuristic approach for the Quickest Multicommodity k-Splittable Flow
           Problem
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Anna Melchiori, Antonino Sgalambro
      The literature on k-splittable flows, see Baier et al. (2002), provides evidence on how controlling the number of used paths enables practical applications of flows optimization in many real-world contexts. Such a modeling feature has never been integrated so far in Quickest Flows, a class of optimization problems suitable to cope with situations such as emergency evacuations, transportation planning and telecommunication systems, where one aims to minimize the makespan, i.e. the overall time needed to complete all the operations, see Pascoal et al. (2006). In order to bridge this gap, a novel optimization problem, the Quickest Multicommodity k-Splittable Flow Problem (QMCkSFP) is introduced in this paper. The problem seeks to minimize the makespan of transshipment operations for given demands of multiple commodities, while imposing restrictions on the maximum number of paths for each single commodity. The computational complexity of this problem is analyzed, showing its NP-hardness in the strong sense, and an original Mixed-Integer Programming formulation is detailed. We propose a matheuristic algorithm based on a hybridized Very Large-Scale Neighborhood Search that, utilizing the presented mathematical formulation, explores multiple search spaces to solve efficiently large instances of the QMCkSFP. High quality computational results obtained on benchmark test sets are presented and discussed, showing how the proposed matheuristic largely outperforms a state-of-the-art heuristic scheme frequently adopted in path-restricted flow problems.

      PubDate: 2017-12-26T16:27:29Z
       
  • A semivectorial bilevel programming approach to optimize electricity
           dynamic time-of-use retail pricing
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Maria João Alves, Carlos Henggeler Antunes
      Presently, residential electricity consumers are, in general, charged at flat or dual time-of-use tariffs along the day, which are defined by the retailer for long periods (e.g., one year). These pricing schemes do not convey price signals reflecting generation costs and grid conditions. Hence, consumers lack the incentives to engage in different consumption patterns using the flexibility they generally have in the operation of some end-use loads. Dynamic tariffs, i.e. energy prices varying possibly with significant magnitude in short periods of time, are expected to become an applicable pricing scheme in smart grids. In this setting, home energy management systems can play an important role to help end-users optimizing the usage of appliances to minimize energy costs without compromising comfort. This can be advantageous also from the perspective of grid management. A semivectorial bilevel programming approach is developed to model the interaction between electricity retailers and consumers in order to optimize electricity time-of-use retail pricing. The aim is to support the retailer in finding good decisions for the prices. The retailer (upper level decision maker) establishes dynamic time-of-use electricity prices to maximize profits. The consumer (lower level decision maker) responds by selecting, under that price setting, a load scheduling decision leading to a nondominated solution balancing his objectives of minimizing the electricity bill (cost dimension) and minimizing the dissatisfaction in face of his preferences and requirements (comfort dimension). The lower level optimization problem is formulated as a bi-objective mixed-integer linear programming problem. A hybrid approach is proposed, which consists of a genetic algorithm for the upper level problem and an exact solver to solve surrogate scalar problems at the lower level. A case study is presented and discussed.

      PubDate: 2017-12-26T16:27:29Z
       
  • Partial objective inequalities for the multi-item capacitated lot-sizing
           problem
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): İ. Esra Büyüktahtakın, J. Cole Smith, Joseph C. Hartman
      In this paper, we study a mixed-integer programming model of the single-level multi-item capacitated lot-sizing problem (MCLSP), which incorporates shared capacity on the production of items for each period throughout a planning horizon. We derive valid bounds on the partial objective function of the MCLSP formulation by solving the first t periods of the problem over a subset of all items, using dynamic programming and integer programming techniques. We also develop algorithms for strengthening these valid inequalities by back-lifting techniques. These inequalities can be utilized within a cutting-plane algorithm, in which we perturb the partial objective function coefficients to identify violated inequalities to the MCLSP polytope. Our computational results show that the envelope inequalities are very effective for the MCLSP instances with different capacity and cost characteristics, when compared to the (l, S) inequalities.

      PubDate: 2017-12-26T16:27:29Z
       
  • Benders’ decomposition for curriculum-based course timetabling
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Niels-Christian F. Bagger, Matias Sørensen, Thomas R. Stidsen
      In this paper we applied Benders’ decomposition to the Curriculum-Based Course Timetabling (CBCT) problem. The objective of the CBCT problem is to assign a set of lectures to time slots and rooms. Our approach was based on segmenting the problem into time scheduling and room allocation problems. The Benders’ algorithm was then employed to generate cuts that connected the time schedule and room allocation. We generated only feasibility cuts, meaning that most of the solutions we obtained from a mixed integer programming solver were infeasible, therefore, we also provided a heuristic in order to regain feasibility. We compared our algorithm with other approaches from the literature for a total of 32 data instances. We obtained a lower bound on 23 of the instances, which were at least as good as the lower bounds obtained by the state-of-the-art, and on eight of these, our lower bounds were higher. On two of the instances, our lower bound was an improvement of the currently best-known. Lastly, we compared our decomposition to the model without the decomposition on an additional six instances, which are much larger than the other 32. To our knowledge, this was the first time that lower bounds were calculated for these six instances.

      PubDate: 2017-12-26T16:27:29Z
       
  • Branch-and-cut methods for the Network Design Problem with Vulnerability
           Constraints
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Luís Gouveia, Martim Joyce-Moniz, Markus Leitner
      The aim of Network Design Problem with Vulnerability Constraints (NDPVC), is to design survivable telecommunications networks that impose length bounds on the communication paths of each commodity pair, before and after the failure of any k links. This problem was proposed as an alternative to the Hop-Constrained Survivable Network Design Problem (kHSNDP), which addresses similar issues, but imposes very conservative constraints, possibly leading to unnecessarily expensive solution or even rendering instances infeasible. In fact, it is known that the cost of the optimal solutions of the NDPVC never exceeds that of the related kHSNDP. However, previous results using the standard methods of a general-purpose integer linear (ILP) solver, combined with several ILP formulations, show that such methods fail to solve most instances in the benchmarking test set, within a time limit of two hours. In this paper, we propose three branch-and-cut algorithms, which are significantly more efficient in solving the NDPVC. The first algorithm is a cutting-plane method devised in the context of a new layered graph ILP formulation, whereas the other two are based on Benders decomposition methods of previously known formulations. With the proposed new methods, we are able to solve substantially more instances of the NDPVC and therefore able to provide a more complete comparison of its solutions to those of the kHSNDP.

      PubDate: 2017-12-26T16:27:29Z
       
  • Online algorithms for the maximum k-colorable subgraph problem
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Alain Hertz, Romain Montagné, François Gagnon
      The maximum k-colorable subgraph problem (k-MCSP) is to color as many vertices as possible with at most k colors, such that no two adjacent vertices share the same color. We consider online algorithms for this NP -hard problem, and give bounds on their competitive ratio. We then consider a large family A of online sequential coloring algorithms and determine the smallest graphs for which no algorithm in A can produce an optimal solution to the k-MCSP. We then compare the performance of several online sequential coloring algorithms, using DIMACS benchmark instances. We finally consider the case where vertices colored at an early stage can receive a new color later on, as long as they remain colored.

      PubDate: 2017-12-26T16:27:29Z
       
  • Aircraft parking stand allocation problem with safety consideration for
           independent hangar maintenance service providers
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Yichen Qin, Felix T.S. Chan, S.H. Chung, T. Qu, B. Niu
      An aircraft parking stand allocation problem for aircraft hangar maintenance in the context of an independent aircraft maintenance, repair and overhaul (MRO) service provider is studied. This problem arises from the increasing outsourcing maintenance requests initiated by clients that can cause congestion on certain days. Given a set of maintenance requests on a peak day that exceed the capacity of the maintenance hangar, the service provider has to select and first serve the particular subset of aircraft that maximizes their overall profits and then rearrange the remaining requests later. The objective of the proposed problem is to determine a subset of maintenance orders with maximal overall profits and a feasible parking plan on a peak day. In particular, there is to be no overlap between aircraft, and the risk of collision measured by the shortest distance between each pair of aircraft is to be minimized. To solve this problem, No-Fit Polygon (NFP) construction is adopted to prevent overlap between pairs of aircraft. A two-stage MIP approach is proposed, in which the first model is used to find the subset of maintenance orders with the maximal overall profits, while the second model maximizes the overall safety margins based on the revised NFPs. A heuristic algorithm is introduced in order to improve the efficiency of the branch-and-bound algorithm in the second stage problem. Testing instances are generated based on the real situation in an aircraft maintenance company, and the effectiveness of the proposed approaches are evaluated through computational experiments.

      PubDate: 2017-12-26T16:27:29Z
       
  • A preference-based, multi-unit auction for pricing and capacity allocation
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Javad Lessan, Selçuk Karabatı
      We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers’ preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers’ “fairness” expectations. The auction’s termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system’s profit, indicating its effectiveness for a seller who needs to operate under the “fairness” constraint.

      PubDate: 2017-12-26T16:27:29Z
       
  • Scheduling pumpoff operations in onshore oilfields with electric-power
           constraints and variable cycle time
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Eduardo Camponogara, Luiz Alberto Guardini, Leonardo Salsano de Assis
      An oilfield is a complex enterprise that requires hefty capital investments and substantial energy resources for its operation. In mature onshore oilfields, sucker-rod pumps are deployed to enable oil production when the reservoir pressure is low. Albeit robust, such an artificial-lifting technique relies on electric-power supply to keep the rotary machines running. Managing a limited source of electric power while, at the same time, maximizing oil production and reducing equipment wear is of paramount importance, particularly so with today’s low prices for the oil barrel. To this end, this paper proposes mixed-integer linear formulations for scheduling the operations of sucker-rod pumps, which work according to a control policy that alternates between on and off pumping periods, the so called pumpoff policy. Formulations for scheduling the initial operations and reconfiguring the control policies are developed, implemented, and tested with computational experiments.

      PubDate: 2017-12-26T16:27:29Z
       
  • Online scheduling problems with flexible release dates: Applications to
           infrastructure restoration
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Sarah G. Nurre, Thomas C. Sharkey
      We consider scheduling problems with the new concept of flexible release dates under an online optimization framework. A flexible release date is one where the traditional release date of a specific operation can be moved earlier in time, specifically to the completion time of an associated supplementary operation. In this context, we examine two classes of parallel identical machines: those that perform supplementary operations to alter release dates and those that perform installation operations to change the network characteristics. We further consider multi-function machines that can perform both supplementary and installation operations. The release date of an operation is often determined by events outside the knowledge of the decision-maker. Therefore, we consider scheduling problems in an online setting to model the lack of- and evolution of information about the release dates of tasks. Motivated by infrastructure restoration after an extreme event, we consider flexible release dates for an integrated network design and scheduling problem that seeks to improve the performance of a network over time by selecting and scheduling operations that will change the network characteristics. To solve these problems, we propose heuristic dispatching rules whose solutions are benchmarked against the solutions of a mixed integer programming formulation. Using a realistic infrastructure network, we perform computational tests; the results of these tests demonstrate the ability of the dispatching rule to find high-quality solutions in real time and quickly adapt to the arrival of new information. From the analysis of these results, we deduce policy insights regarding the role of flexible release dates and the machine fleet configuration.

      PubDate: 2017-12-12T18:56:47Z
       
  • Two-state optimal maintenance planning of repairable systems with
           covariate effects
    • Abstract: Publication date: April 2018
      Source:Computers & Operations Research, Volume 92
      Author(s): Wujun Si, Ernie Love, Qingyu Yang
      Optimal maintenance planning for repairable systems plays a critical role in ensuring an appropriate level of system reliability and availability. An important class of optimal maintenance planning decisions are those in which one must determine whether to implement a repair or a renewal (replacement) upon system failure. Most existing models within this class are based on a single-state framework, wherein the system age is utilized as the unique measure to determine whether to repair or renew. Extended models have also appeared in the literature which utilize both the system age and the number of failures/repairs since last replacement to provide a two-dimensional characterization of the system state thereby providing more flexibility and improving the quality of maintenance planning. The existing two-state optimal maintenance planning models, however, only work for a single system. They cannot handle situations where multiple systems are involved, especially when multiple systems operate in different environments (treated as covariate effects) leading to heterogeneity in failure processes of those systems. Ignoring the covariate effects can result in a non-optimal (i.e., more costly) maintenance planning. In this article, we propose a two-state covariate-dependent optimal maintenance planning model for multiple systems. Specifically, we develop a covariate-dependent trend renewal process model to formulate the heterogeneous failure processes of multiple systems. A maximum likelihood estimation method is developed for model parameter estimation. Based on the proposed model, we develop a two-state covariate-dependent optimal maintenance planning by utilizing a discrete semi-Markov decision process. The optimal covariate-dependent control-limit maintenance policy is derived based on a numerical search algorithm. A simulation study and a real-world case study are conducted to illustrate the proposed approach.

      PubDate: 2017-12-12T18:56:47Z
       
  • An efficient exact model for the cell formation problem with a variable
           number of production cells
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Ilya Bychkov, Mikhail Batsyn
      The Cell Formation Problem has been studied as an optimization problem in manufacturing for more than 90 years. It consists of grouping machines and parts into manufacturing cells in order to maximize loading of cells and minimize movement of parts from one cell to another. Many heuristic algorithms have been proposed which are doing well even for large-sized instances. However, only a few authors have aimed to develop exact methods and most of these methods have some major restrictions such as a fixed number of production cells for example. In this paper we suggest a new mixed-integer linear programming model for solving the cell formation problem with a variable number of manufacturing cells. The popular grouping efficacy measure is used as an objective function. To deal with its fractional nature we apply the Dinkelbach approach. Our computational experiments are performed on two testsets: the first consists of 35 well-known instances from the literature and the second contains 32 instances less popular. We solve these instances using CPLEX software. Optimal solutions have been found for 63 of the 67 considered problem instances and several new solutions unknown before have been obtained. The computational times are greatly decreased comparing to the state-of-art approaches.

      PubDate: 2017-12-12T18:56:47Z
       
  • The Weighted Fair Sequences Problem
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Bruno Jefferson de S. Pessoa, Daniel Aloise, Lucidio A.F. Cabral
      Scheduling problems on which constraints are imposed with regard to the temporal distances between successive executions of the same task have numerous applications, ranging from task scheduling in real-time systems to automobile production on a mixed-model assembly line. This paper introduces a new NP-hard optimization problem belonging to this class of problems, namely the Weighted Fair Sequences Problem (WFSP). We present a mathematical formulation for the WFSP based on mixed-integer linear programming (MILP) as well as a series of cuts to improve its resolution via exact methods. Finally, we propose a heuristic solution method that works with much less variables of the WFSP formulation. The reported computational experiments show that, for a given time horizon, the proposed MILP-based heuristic increases the size of WFSP instances that can be tackled in practice. Moreover, its results should be considered as optimal whether a presented conjecture on the WFSP problem is proved true in the future.

      PubDate: 2017-12-12T18:56:47Z
       
  • Production planning with order acceptance and demand uncertainty
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Tarik Aouam, Kobe Geryl, Kunal Kumar, Nadjib Brahimi
      Traditional production planning models assume that all orders must be satisfied when capacity is available. In this paper, we analyze the value of providing decision makers with the flexibility to accept or reject orders, when order quantity is uncertain. We introduce this demand flexibility in two production planning problems. The first problem integrates order acceptance in the capacitated lot sizing problem, providing the option to reject an order if it requires a high setup cost and cannot be aggregated with additional orders to take advantage of economies of scale. The second problem integrates order acceptance in the order release planning problem with load-dependent lead times (LDLTs). This problem provides the option to reject an order if it increases the workload causing the delay of other orders due to congestion effects. Robust counterparts of both integrated problems are formulated as linear mixed integer programs (MIPs). The deterministic integrated problems and their robust counterparts are shown to be NP-hard and a two-stage MIP heuristic is proposed as a solution procedure. A relax and fix (RF) heuristic is adapted to efficiently construct feasible solutions to the robust problems, which are then improved by a fix and optimize (FO) heuristic. Numerical results show that the proposed heuristics give promising results in terms of solution quality and computation time. Simulation experiments are conducted to assess the value of demand flexibility and to study the effects of various parameters on economical performance.

      PubDate: 2017-12-12T18:56:47Z
       
  • Three-dimensional protein structure prediction based on memetic algorithms
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Leonardo de Lima Corrêa, Bruno Borguesan, Mathias J. Krause, Márcio Dorn
      Tertiary protein structure prediction is a challenging problem in Structural Bioinformatics and is classified according to the computational complexity theory as a NP-hard problem. In this paper, we proposed a first-principle method that makes use of a priori information about known protein structures to tackle the three-dimensional protein structure prediction problem. We do so by designing a multimodal memetic algorithm that uses an evolutionary approach with a ternary tree-structured population allied to a local search strategy. The method has been developed based on an incremental approach using the combination of promising evolutionary components to address the concerned multimodal problem. Three memetic algorithms focused on the problem are proposed. The first one modifies a basic version of a memetic algorithm by introducing modified global search operators. The second uses a different population structure for the memetic algorithm. And finally, the last algorithm consists of the integration of global operators and multimodal strategies to deal with the inherent multimodality of the protein structure prediction problem. The implementations take advantage of structural knowledge stored in the Protein Data Bank to guide the exploiting and restrict the protein conformational search space. Predicted three-dimensional protein structures were analyzed regarding root mean square deviation and the global distance total score test. Obtained results for the three versions outperformed the basic version of the memetic algorithm. The third algorithm overcomes the results of the previous two, demonstrating the importance of adapting the method to deal with the complexities of the problem. In addition, the achieved results are topologically compatible with the experimental correspondent, confirming the promising performance of our approach.
      Graphical abstract image

      PubDate: 2017-12-12T18:56:47Z
       
  • Modeling customer bounded rationality in operations management: A review
           and research opportunities
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Hang Ren, Tingliang Huang
      Many studies in operations management started to explicitly model customer behavior. However, it is typically assumed that customers are fully rational decision-makers and maximize their utility perfectly. Recently, modeling customer bounded rationality has been gaining increasing attention and interest. This paper summarizes various approaches of modeling customer bounded rationality, surveys how they are applied to relevant operations management settings, and presents the new insights obtained. We also suggest future research opportunities in this important area.

      PubDate: 2017-11-16T03:06:42Z
       
  • Vehicle routing with backhauls: Review and research perspectives
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Çağrı Koç, Gilbert Laporte
      In the Vehicle Routing Problem with Backhauls (VRPB), the customer set is partitioned into linehaul customers who require deliveries, and backhaul customers who require pickups. Both the linehaul customers and the backhaul customers must be visited contiguously, and all routes must contain at least one linehaul customer. All deliveries have to be loaded at the depot, and all pickups up have to be transported to the depot. This survey paper aims to comprehensively review the existing literature on VRPBs, including models, exact and heuristic algorithms, variants, industrial applications and case studies, with an emphasis on the recent literature. The paper contains several synthetic tables and proposes a number of promising research directions.

      PubDate: 2017-11-16T03:06:42Z
       
  • A bi-objective aggregate production planning problem with learning effect
           and machine deterioration: Modeling and solution
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Esmaeil Mehdizadeh, Seyed Taghi Akhavan Niaki, Mojtaba Hemati
      The learning effects of the workers and machine deterioration in an aggregate production planning (APP) problem have not been taken into account in the literature yet. These factors affect the performance of any real-world production system and require attention. In this paper, a bi-objective optimization model is developed for an APP problem with labor learning effect and machine deterioration. The first objective of this model maximizes the profit by improving learning and reducing the failure cost of the system. The second objective function minimizes the costs associated with repairs and deterioration, which depend on the failure rate of the machines in the production periods. The aim of this article is to obtain appropriate levels of production rates in regular and overtimes, inventory and shortage levels, workers' hiring and firing levels, and the quantities of the products that are subcontracted. To demonstrate the validity of the proposed mathematical formulation, the multi-objective model is converted into a single-objective model using the fuzzy goal programming method, based on which computational experiments are performed on a set of random small-sized instances solved by the LINGO software. As the problem is shown NP-hard, a subpopulation genetic algorithm (SPGA) is proposed to solve large-size problems. In addition, two other meta-heuristics called weighted sum multi-objective genetic algorithm (WMOGA) and non-dominated sorting genetic algorithm II (NSGA-II) are utilized to solve a set of benchmark problems, in order to validate the results obtained and to assess the performance of the SPGA. For tuning the parameters, the Taguchi method is proposed in order to obtain high-quality solutions. Finally, the performances of the proposed algorithms are statistically compared together. The computational results show that SPGA compared to the other algorithms has a better performance in terms of some multi-objective optimization criteria.
      Graphical abstract image

      PubDate: 2017-11-16T03:06:42Z
       
  • A two-phase heuristic for an in-port ship routing problem with tank
           allocation
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Xin Wang, Mari Jevne Arnesen, Kjetil Fagerholt, Magnhild Gjestvang, Kristian Thun
      This paper addresses an in-port ship routing problem with tank allocation that arises in the chemical shipping industry. The aim is to optimize a tanker’s port call operation that integrates sequencing decisions for visiting terminals and allocating cargo loads to available tanks while taking into account cargo time windows, terminal draft limits and various tank allocation restrictions. We model the problem as a Traveling Salesman Problem with Pickups and Deliveries, Time Windows, Draft Limits and Tank Allocation (TSPPD–TWDLTA), and propose a two-phase heuristic to solve it. Computational studies show that the heuristic is able to provide good solutions to real-sized in-port routing problems with tank allocation in chemical shipping in a reasonable amount of time.

      PubDate: 2017-11-16T03:06:42Z
       
  • A new two-stage heuristic for the recreational vehicle scheduling problem
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Sarang Kulkarni, Rahul Patil, Mohan Krishnamoorthy, Andreas Ernst, Abhiram Ranade
      In this paper, we address the problem of vehicle scheduling in a recreational vehicle rental operation. Two mathematical formulations have been employed in the literature to model the recreational vehicle scheduling problem (RVSP): an assignment-problem-based formulation and a network-flow-based formulation. We propose a new formulation motivated by inventory planning to solve the RVSP. The inventory formulation uses the assignment arcs in a network structure, which is improved by aggregating nodes and arcs. Modifications that are based on node aggregation are also suggested to the existing assignment formulation to reduce the size of the formulation. We find that the inventory formulation outperforms the assignment formulations with and without the aggregation of nodes. We also propose a two-stage heuristic that is based on the inventory formulation and compare its performance with an existing heuristic from the literature. Computational results on real-life RVSP problem instances show that our new heuristic performs significantly better, in terms of the solution time, without compromising too much on the solution quality, for most of the instances.

      PubDate: 2017-11-16T03:06:42Z
       
  • Large Neighborhood Search applied to the Software Module Clustering
           problem
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Marlon C. Monçores, Adriana C.F. Alvim, Márcio O. Barros
      The Software Module Clustering problem seeks to distribute the modules comprising a software system into clusters to maximize cluster cohesion and minimize coupling between clusters. Metaheuristics based on local search have been successfully applied to find good solutions for this problem, outperforming more-complex, population-based heuristics. In this paper, we present a heuristic based on Large Neighborhood Search to address the Software Module Clustering problem. We also perform what is to our knowledge the largest experimental study addressing this problem to date, involving 124 instances of varying size and complexity and comparing our proposed algorithm to the heuristic that has found the best results for the problem so far. Our proposed algorithm outperformed the state-of-the-art heuristic on 93 out of 124 instances with 95% confidence level. We also report new upper bounds on the MQ value for 44 instances and evaluated the relative goodness of the solutions obtained by our proposed algorithm for 89 instances. Considering the 77 instances for which optimal solutions are proven, the proposed algorithm found the optimal solution for 30 instances (39%). Additionally, thirteen developers participate in a study focused on the distribution of the modules comprising a software project into clusters. We used JodaMoney as our study object and we compared the characteristics of the solutions generated by its authors, the best solution generated by LNS_SMC and the solution generated by each of the 13 subjects. LNS_SMC solution performs better than inexperienced subjects ones in issue resolution prediction but performs worse than the solutions proposed by experienced subjects and also the author’s solution. For the prediction of concomitant changes, the LNS_SMC solution was outperformed by both subjects and authors solutions.

      PubDate: 2017-11-16T03:06:42Z
       
  • Tabu search for the dynamic Bipartite Drawing Problem
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Rafael Martí, Anna Martínez-Gavara, Jesús Sánchez-Oro, Abraham Duarte
      Drawings of graphs have many applications and they are nowadays well-established tools in computer science in general, and optimization in particular. Project scheduling is one of the many areas in which representation of graphs constitutes an important instrument. The experience shows that the main quality desired for drawings of graphs is readability, and crossing reduction is a fundamental aesthetic criterion to achieve it. Incremental or dynamic graph drawing is an emerging topic in this context, where we seek to preserve the layout of a graph over successive drawings. In this paper, we target the edge crossing reduction in the context of incremental graph drawing. Specifically, we apply a mathematical programming formulation and several heuristic methods based on the tabu search methodology to solve it. In line with the previous paper on this topic, we consider bipartite graphs in our experimentation. The extensive computational experiments with more than 1000 instances show the superiority of our proposals in both, quality and computing time.

      PubDate: 2017-11-09T02:38:13Z
       
  • Robust scheduling to minimize the weighted number of late jobs with
           interval due-date uncertainty
    • Abstract: Publication date: March 2018
      Source:Computers & Operations Research, Volume 91
      Author(s): Maciej Drwal
      We consider the class of single machine scheduling problems with the objective to minimize the weighted number of late jobs, under the assumption that completion due-dates are not known precisely at the time when decision-maker must provide a schedule. It is assumed that only the intervals to which the due-dates belong are known. The concept of maximum regret is used to define robust solution. A polynomial time algorithm is given for the case when weights of jobs are all equal. A mixed-integer linear programming formulation is provided for the general case, and computational experiments are reported.

      PubDate: 2017-11-09T02:38:13Z
       
  • A guided local search with iterative ejections of bottleneck operations
           for the job shop scheduling problem
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Yuichi Nagata, Isao Ono
      This paper presents a local search-based method that works in partial solution space for solving the job shop scheduling problem (JSP). The proposed method iteratively solves a series of constraint satisfaction problems (CSPs), where the current CSP is defined as the original JSP with an additional constraint that the makespan is smaller than that of the schedule obtained by solving the previous CSP. To obtain a solution to the current CSP, a local search-based procedure is performed in a partial solution space where the current solution is represented as a partial schedule. The neighborhood consists of a set of partial schedules whose makespan is less than that of the best-so-far complete schedule obtained by solving the previous CSP. The existence of the additional constraint on the makespan restricts possible local moves to those that satisfy necessary conditions to improve the best-so-far complete schedule. These moves are efficiently enumerated by using a dynamic programming-based algorithm we present in this paper. We also present an effective strategy of selecting next partial solution from the neighborhood, perturbation procedure, and tabu-search procedure, all of which are embedded into the basic framework to enhance the performance.

      PubDate: 2017-11-02T00:32:00Z
       
  • Exact and heuristic algorithms for order acceptance and scheduling with
           sequence-dependent setup times
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Yuri Laio T.V. Silva, Anand Subramanian, Artur Alves Pessoa
      The Order Acceptance and Scheduling (OAS) problem consists of simultaneously deciding which orders (jobs) are going to be accepted for processing as well as their associated schedule. This problem typically arises when a company does not have the capacity to meet the demand, thus being forced to reject some orders. We consider a OAS variant where each job has a processing time, due date, release date, deadline, revenue and penalty weight. In addition, for each pair of jobs i and j, there is a setup time required before starting to process j if this job is scheduled immediately after job i. The objective is to select and schedule a subset of jobs that maximizes the total profit, which is given by the total revenue minus the total weighted tardiness. To solve this NP -hard problem, we propose a new arc-time-indexed mathematical formulation that is capable of solving instances with up to 50 jobs. However, since this formulation relies on a pseudo-polynomial number of variables, larger instances cannot be solved in practice. To overcome this limitation, we developed two exact algorithms over this formulation where the first is based on Lagrangian relaxation and the second is based on column generation. We report tight upper bounds for instances with up to 100 jobs. Moreover, we also implemented a local search based metaheuristic algorithm for obtaining high quality lower bounds. Extensive computational experiments were carried out in 1500 benchmark instances ranging from 10 to 100 jobs and the results obtained suggest that the proposed exact and heuristic methods are capable of finding extremely competitive results when compared to those available in the literature.

      PubDate: 2017-11-02T00:32:00Z
       
  • Efficient simulated annealing based solution approaches to the competitive
           single and multiple allocation hub location problems
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Nader Ghaffarinasab, Alireza Motallebzadeh, Younis Jabarzadeh, Bahar Y. Kara
      Hub location problems (HLPs) constitute an important class of problems in logistics with numerous applications in passenger/cargo transportation, postal services, telecommunications, etc. This paper addresses the competitive single and multiple allocation HLPs where the market is assumed to be a duopoly. Two firms (decision makers) sequentially decide on the configuration of their hub networks trying to maximize their own market shares. The customers choose one firm based on the cost of service provided by these firms. Mathematical formulations are presented for the problems of the first and second firms (the leader and the follower, respectively) and Simulated Annealing (SA) based solution algorithms are proposed for solving these problems both in single and multiple allocation settings. Extensive computational experiments show the capability of the proposed solution algorithms to obtain the optimal solutions in short computational times. Some managerial insights are also derived based on the obtained results.

      PubDate: 2017-11-02T00:32:00Z
       
  • A computational study of the general lot-sizing and scheduling model under
           demand uncertainty via robust and stochastic approaches
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Douglas Alem, Eduardo Curcio, Pedro Amorim, Bernardo Almada-Lobo
      This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

      PubDate: 2017-10-04T06:42:09Z
       
  • Cyclic scheduling of parts and robot moves in m-machine robotic cells
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Hakan Gultekin, Betul Coban, Vahid Eghbal Akhlaghi
      We consider a flow shop type manufacturing cell consisting of m machines and a material handling robot producing multiple parts. The robot transfers the parts between the machines and loads/unloads the machines. We consider the cyclic scheduling of the parts and the robot moves with the objective of maximizing the throughput rate. We develop a mixed integer linear programming formulation of the problem. The formulation is improved with several valid inequalities and reformulations of the constraints. We also develop a hybrid metaheuristic algorithm for this strongly NP-Hard problem. The algorithm is modified to handle both 1-unit and multi-unit robot cycles. Multi-threading is used to parallelize the algorithm in order to improve its efficiency. After calibrating the parameters of the heuristic algorithm, an extensive computational study is performed to evaluate its performance. The results of this study revealed that the developed heuristic provides near-optimal solutions in reasonable solution times. The effects of parallelization and the benefits of considering multi-unit cycles instead of 1-unit cycles are also quantified. Our computational tests show that multi-unit cycles improve the throughput rate by 9% on the average. The improvement can reach to 20% depending on the problem parameters.

      PubDate: 2017-10-04T06:42:09Z
       
  • A hybrid dynamic programming and memetic algorithm to the Traveling
           Salesman Problem with Hotel Selection
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Yongliang Lu, Una Benlic, Qinghua Wu
      The Traveling Salesman Problem with Hotel Selection (TSPHS) is a variant of the classic Traveling Salesman Problem. It arises from a number of real-life applications where the maximum travel time for each “day trip” is limited. In this paper, we present a highly effective hybrid between dynamic programming and memetic algorithm for TSPHS. The main features of the proposed method include a dynamic programming approach to find an optimal hotel sequence for a given tour, three dedicated crossover operators for solution recombination, an adaptive rule for crossover selection, and a two-phase local refinement procedure that alternates between feasible and infeasible searches. Experiments on four sets of 131 benchmark instances from the literature show a remarkable performance of the proposed approach. In particular, it finds improved best solutions for 22 instances and matches the best known results for 103 instances. Additional analyses highlight the contribution of the dynamic programming approach, the joint use of crossovers and the two local search phases to the performance of the proposed algorithm.

      PubDate: 2017-10-04T06:42:09Z
       
  • Exact approaches for the knapsack problem with setups
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Fabio Furini, Michele Monaci, Emiliano Traversi
      We consider a generalization of the knapsack problem in which items are partitioned into classes, each characterized by a fixed cost and capacity. We study three alternative Integer Linear Programming formulations. For each formulation, we design an efficient algorithm to compute the linear programming relaxation (one of which is based on Column Generation techniques). We theoretically compare the strength of the relaxations and derive specific results for a relevant case arising in benchmark instances from the literature. Finally, we embed the algorithms above into a unified implicit enumeration scheme which is run in parallel with an improved Dynamic Programming algorithm to effectively solve the problem to proven optimality. An extensive computational analysis shows that our new exact algorithm is capable of efficiently solving all the instances of the literature and turns out to be the best algorithm for instances with a low number of classes.

      PubDate: 2017-10-04T06:42:09Z
       
  • Improved quick hypervolume algorithm
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Andrzej Jaszkiewicz
      In this paper, we present a significant improvement of the Quick Hypervolume algorithm, one of the state-of-the-art algorithms for calculating the exact hypervolume of the space dominated by a set of d-dimensional points. This value is often used as the quality indicator in the multiobjective evolutionary algorithms and other multiobjective metaheuristics and the efficiency of calculating this indicator is of crucial importance especially in the case of large sets or many dimensional objective spaces. We use a similar divide and conquer scheme as in the original Quick Hypervolume algorithm, but in our algorithm we split the problem into smaller sub-problems in a different way. Through both theoretical analysis and a computational study we show that our approach improves the computational complexity of the algorithm and practical running times.

      PubDate: 2017-09-26T08:04:32Z
       
  • Random partial neighborhood search for the post-enrollment course
           timetabling problem
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Yuichi Nagata
      In this study, we present a local search-based algorithm for the post-enrollment-based course timetabling problem, which incorporates a mechanism for adapting the neighborhood size during the course of the search. At each iteration, the neighborhood size is changed simply by constructing a random partial neighborhood, which is defined as a random subset of the entire neighborhood. The main reason for using a random partial neighborhood is to control the trade-off between exploration and exploitation during search, and two updating strategies are considered for changing the neighborhood size. The proposed algorithms were tested using well-known benchmark sets and the results obtained were highly competitive with those produced by the leading solvers developed for these benchmark sets.

      PubDate: 2017-09-26T08:04:32Z
       
  • Stochastic maximum flow interdiction problems under heterogeneous risk
           preferences
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Xiao Lei, Siqian Shen, Yongjia Song
      We consider a generic maximum flow interdiction problem that involves a leader and a follower who take actions in sequence. Given an interdiction budget, the leader destroys a subset of arcs to minimize the follower’s maximum flows from a source to a sink node. The effect from an interdiction action taken on each arc is random, following a given success rate of decreasing the arc’s capacity to zero. The follower can add additional arc capacities for mitigating flow losses, after knowing the leader’s interdiction plan but before realizing the uncertainty. We consider risk-neutral and risk-averse behaviors of the two players and investigate five bi-level/tri-level programming models for different risk-preference combinations. The models incorporate the expectation, left-tail, and right-tail Conditional Value-at-Risk (CVaR) as commonly used convex risk measures for evaluating random maximum flows in the leader’s and follower’s objectives. We reformulate each model as an equivalent mixed-integer linear program and test them on real-world network instances to demonstrate interactions between the leader and the follower under various risk-preference settings.

      PubDate: 2017-09-26T08:04:32Z
       
  • Total completion time minimization for machine scheduling problem under
           time windows constraints with jobs’ linear processing rate function
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Nhan-Quy Nguyen, Farouk Yalaoui, Lionel Amodeo, Hicham Chehade, Pascal Toggenburger
      In this paper, we consider an identical parallel machine scheduling problem with a single additional resource. The processing rate of a job is defined by a linear resource consumption function. The addressed problem takes into consideration two new constraints. The first is the time-varying total available resource. The second new constraint limits the resource consumption incrementation of each job on two consecutive periods of time. Moreover, jobs have bounded resource consumption, arrival times and deadlines. Many practical applications, such as the electrical charging scheduling, can find interests in our works. Our contributions are two-folds. First, we introduce a Mixed-Integer-Linear-Program (MILP) to formulate the problem. Then, we present a heuristic consisting of two phases: a feasible solution construction phase using geometrical strip packing and a solution improvement phase. The heuristic is proven to be very efficiency for dealing with the problem throughout the numerical experiments.

      PubDate: 2017-09-26T08:04:32Z
       
  • An efficient evolutionary algorithm for the orienteering problem
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Gorka Kobeaga, María Merino, Jose A. Lozano
      This paper deals with the Orienteering Problem, which is a routing problem. In the Orienteering Problem each node has a profit assigned and the goal is to find the route that maximizes the total collected profit subject to a limitation on the total route distance. To solve this problem, we propose an evolutionary algorithm, whose key characteristic is to maintain unfeasible solutions during the search. Furthermore, it includes a novel solution codification for the Orienteering Problem, a novel heuristic for node inclusion in the route, an adaptation of the Edge Recombination crossover developed for the Travelling Salesperson Problem, specific operators to recover the feasibility of solutions when required, and the use of the Lin-Kernighan heuristic to improve the route lengths. We compare our algorithm with three state-of-the-art algorithms for the problem on 344 benchmark instances, with up to 7397 nodes. The results show a competitive behavior of our approach in instances of low-medium dimensionality, and outstanding results in the large dimensionality instances reaching new best known solutions with lower computational time than the state-of-the-art algorithms.

      PubDate: 2017-09-20T07:50:06Z
       
  • Preemptive rerouting of airline passengers under uncertain delays
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Lindsey A. McCarty, Amy E.M. Cohn
      An airline’s operational disruptions can lead to flight delays that in turn impact passengers, not only through the delays themselves but also through possible missed connections. Since the length of a delay is often not known in advance, we consider preemptive rerouting of airline passengers before the length of the delay is realized. Our goal is to reaccommodate passengers proactively as soon as it is known that a flight will be delayed instead of waiting until passengers have missed connections. We consider the simplified version of the real-world problem in which only a single flight is delayed. We model this problem as a two-stage stochastic programming problem, with first-stage decisions that may preemptively assign passengers to new itineraries in anticipation of the delay’s impact, and second-stage decisions that may further modify itineraries for any passengers who still miss connections after the delay has been realized. We present a Benders Decomposition approach to solving this problem and give computational results to demonstrate the reasonable run time in solving our model. This research lays the groundwork for the more-realistic case in which multiple flights in the network may experience concurrent delays.

      PubDate: 2017-09-14T07:48:29Z
       
  • Enhancing urban mobility: Integrating ride-sharing and public transit
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Mitja Stiglic, Niels Agatz, Martin Savelsbergh, Mirko Gradisar
      Seamless integration of ride-sharing and public transit may offer fast, reliable, and affordable transfer to and from transit stations in suburban areas thereby enhancing mobility of residents. We investigate the potential benefits of such a system, as well as the ride-matching technology required to support it, by means of an extensive computational study. Our study shows that the integration of a ride-sharing system and a public transit system can significantly enhance mobility and increase the use of public transport.

      PubDate: 2017-09-14T07:48:29Z
       
  • A bi-criteria hybrid Genetic Algorithm with robustness objective for the
           course timetabling problem
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Can Akkan, Ayla Gülcü
      Traditional methods of generating timetables may yield high-quality solutions, but they may not yield robust solutions that may easily be adapted to changing inputs. Incorporating late changes by making minimum modifications on the final timetable is an important need in many practical applications of timetabling. In this study, we focus on a subset of course timetabling problems, the curriculum-based timetabling problem. We first define a robustness measure for the problem, and then try to find a set of good solutions in terms of both penalty and robustness values. We model the problem as a bi-criteria optimization problem and solve it by a hybrid Multi-objective Genetic Algorithm, which makes use of Hill Climbing and Simulated Annealing algorithms in addition to the standard Genetic Algorithm approach. The algorithm is tested on the well known ITC-2007 instances and shown to identify high quality Pareto fronts.

      PubDate: 2017-09-14T07:48:29Z
       
  • Minimizing the tracking error of cardinality constrained portfolios
    • Abstract: Publication date: February 2018
      Source:Computers & Operations Research, Volume 90
      Author(s): Purity Mutunge, Dag Haugland
      We study the problem of selecting a restricted number of shares included in a stock market index, such that the portfolio resembles the index as closely as possible. To measure the difference between the portfolio and the index, referred to as the tracking error, we use a quadratic function with the covariance matrix of the index returns as coefficient matrix. The problem is proved to be strongly NP-hard, and we give theoretical evidence that continuous relaxations of mixed integer quadratic programming (MIQP) formulations are likely to produce poor lower bounds on the tracking error. For fast computation of near-optimal portfolios, we demonstrate how the best-extension-by-one construction heuristic can be designed to run in time bounded by a fourth order polynomial. We also show that the running time of one iteration of the best-exchange-by one improvement heuristic is of the same order. Computational experiments applied to real-life stock market indices show that in instances where an index of less than 500 assets is to be tracked by a portfolio of 10 assets, a commercially available MIQP solver fails to reduce the integrality gap below 94% in 30 CPU-minutes. In contrast, the construction heuristic under study needs less than 30 CPU-seconds to produce a portfolio of 100 assets tracking an index of nearly 2000 assets.

      PubDate: 2017-09-14T07:48:29Z
       
 
 
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