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A simulated annealing code for general integer linear programs

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  • D. Abramson
  • M. Randall

Abstract

This paper explores the use of simulated annealing (SA) for solving arbitrary combinatorialoptimisation problems. It reviews an existing code called GPSIMAN for solving0‐1 problems, and evaluates it against a commercial branch‐and‐bound code, OSL. Theproblems tested include travelling salesman, graph colouring, bin packing, quadratic assignmentand generalised assignment. The paper then describes a technique for representingthese problems using arbitrary integer variables, and shows how a general simulated annealingalgorithm can also be applied. This new code, INTSA, outperforms GPSIMAN andOSL on almost all of the problems tested. Copyright Kluwer Academic Publishers 1999

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  • D. Abramson & M. Randall, 1999. "A simulated annealing code for general integer linear programs," Annals of Operations Research, Springer, vol. 86(0), pages 3-21, January.
    • Handle: RePEc:spr:annopr:v:86:y:1999:i:0:p:3-21:10.1023/a:1018915104438
    DOI: 10.1023/A:1018915104438
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    Cited by:

    1. Zhang, Defu & Liu, Yongkai & M'Hallah, Rym & Leung, Stephen C.H., 2010. "A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems," European Journal of Operational Research, Elsevier, vol. 203(3), pages 550-558, June.
    2. Alizadeh, Morteza & Amiri-Aref, Mehdi & Mustafee, Navonil & Matilal, Sumohon, 2019. "A robust stochastic Casualty Collection Points location problem," European Journal of Operational Research, Elsevier, vol. 279(3), pages 965-983.
    3. Stelios Tsafarakis, 2016. "Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover," Annals of Operations Research, Springer, vol. 247(2), pages 617-633, December.

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