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An Approach to Linear Programming with 0-1 Variables

Author

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  • Shizuo Senju

    (Keio University, Tokyo)

  • Yoshiaki Toyoda

    (Dainippon Ink and Chemicals, Inc., Tokyo)

Abstract

There are many decision making problems in which one seeks to choose the optimal package from a large number of indivisible independent proposals. For instance, jobbing firms have often to choose the most profitable package of orders from hundreds of potential ones under a great many restrictions on available resources, such as working time of different facilities, number of specialists, materials, etc. This article is intended to present a simple approach to obtaining approximate solutions for such problems. The fundamental concept is to make some ordinal scales among proposals. Steps of calculation are illustrated by examples of choosing the optimal mix of orders, one of which involves 60 candidate proposals with 30 restricting conditions. This method may be of great help when (1) the number of candidates and restricting conditions are large; (2) the estimated or raw data on required resources for proposals and their incremental profits contain some errors; and (3) the distribution of incremental profits and required resources of candidates differs greatly, say, week by week, and the limits on resources can be extended in a practical manner by carrying over an inventory of profitable backlog orders, reducing, in effect, the remaining capacity in the future week.

Suggested Citation

  • Shizuo Senju & Yoshiaki Toyoda, 1968. "An Approach to Linear Programming with 0-1 Variables," Management Science, INFORMS, vol. 15(4), pages 196-207, December.
  • Handle: RePEc:inm:ormnsc:v:15:y:1968:i:4:p:b196-b207
    DOI: 10.1287/mnsc.15.4.B196
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    1. Cemal AKTÜRK & Sevinç GÜLSEÇEN, 2018. "Sipariş Teslim Tarihi Problemi İçin Çok Kriterli ve Çok Yöntemli Karar Destek Sistemi Önerisi," Istanbul Management Journal, Istanbul University Business School, vol. 29(84), pages 65-78, June.
    2. Yanhong Feng & Hongmei Wang & Zhaoquan Cai & Mingliang Li & Xi Li, 2023. "Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    3. Lokketangen, Arne & Glover, Fred, 1998. "Solving zero-one mixed integer programming problems using tabu search," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 624-658, April.
    4. Ivan Derpich & Carlos Herrera & Felipe Sepúlveda & Hugo Ubilla, 2021. "Complexity indices for the multidimensional knapsack problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 589-609, June.
    5. Sabah Bushaj & İ. Esra Büyüktahtakın, 2024. "A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack," Journal of Global Optimization, Springer, vol. 89(3), pages 655-685, July.
    6. Hanafi, Said & Freville, Arnaud, 1998. "An efficient tabu search approach for the 0-1 multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 659-675, April.
    7. Zahra Beheshti & Siti Shamsuddin & Siti Yuhaniz, 2013. "Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems," Journal of Global Optimization, Springer, vol. 57(2), pages 549-573, October.
    8. Oliver Bastert & Benjamin Hummel & Sven de Vries, 2010. "A Generalized Wedelin Heuristic for Integer Programming," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 93-107, February.
    9. Yalçın Akçay & Haijun Li & Susan Xu, 2007. "Greedy algorithm for the general multidimensional knapsack problem," Annals of Operations Research, Springer, vol. 150(1), pages 17-29, March.
    10. Slotnick, Susan A., 2011. "Order acceptance and scheduling: A taxonomy and review," European Journal of Operational Research, Elsevier, vol. 212(1), pages 1-11, July.
    11. Matteo Fischetti & Ivana Ljubić & Michele Monaci & Markus Sinnl, 2019. "Interdiction Games and Monotonicity, with Application to Knapsack Problems," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 390-410, April.
    12. Edward Y H Lin & Chung-Min Wu, 2004. "The multiple-choice multi-period knapsack problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(2), pages 187-197, February.
    13. Martí, Rafael & Resende, Mauricio G.C. & Ribeiro, Celso C., 2013. "Multi-start methods for combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 226(1), pages 1-8.
    14. Dimitris Bertsimas & Ramazan Demir, 2002. "An Approximate Dynamic Programming Approach to Multidimensional Knapsack Problems," Management Science, INFORMS, vol. 48(4), pages 550-565, April.
    15. Balev, Stefan & Yanev, Nicola & Freville, Arnaud & Andonov, Rumen, 2008. "A dynamic programming based reduction procedure for the multidimensional 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 186(1), pages 63-76, April.
    16. Anurag Agarwal & Selcuk Colak & Jason Deane, 2010. "NeuroGenetic approach for combinatorial optimization: an exploratory analysis," Annals of Operations Research, Springer, vol. 174(1), pages 185-199, February.
    17. Lin, Feng-Tse & Yao, Jing-Shing, 2001. "Using fuzzy numbers in knapsack problems," European Journal of Operational Research, Elsevier, vol. 135(1), pages 158-176, November.
    18. Bahram Alidaee & Vijay P. Ramalingam & Haibo Wang & Bryan Kethley, 2018. "Computational experiment of critical event tabu search for the general integer multidimensional knapsack problem," Annals of Operations Research, Springer, vol. 269(1), pages 3-19, October.
    19. Yalçin Akçay & Susan H. Xu, 2004. "Joint Inventory Replenishment and Component Allocation Optimization in an Assemble-to-Order System," Management Science, INFORMS, vol. 50(1), pages 99-116, January.
    20. G. Edward Fox & Christopher J. Nachtsheim, 1990. "An analysis of six greedy selection rules on a class of zero‐one integer programming models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(2), pages 299-307, April.
    21. Louis Anthony (Tony) Cox, Jr., 2012. "Evaluating and Improving Risk Formulas for Allocating Limited Budgets to Expensive Risk‐Reduction Opportunities," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1244-1252, July.
    22. Jakob Puchinger & Günther R. Raidl & Ulrich Pferschy, 2010. "The Multidimensional Knapsack Problem: Structure and Algorithms," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 250-265, May.

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