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MO-COMPASS: a fast convergent search algorithm for multi-objective discrete optimization via simulation

Author

Listed:
  • Haobin Li
  • Loo Hay Lee
  • Ek Peng Chew
  • Peter Lendermann

Abstract

Discrete Optimization via Simulation (DOvS) has drawn considerable attention from both simulation researchers and industry practitioners, due to its wide application and significant effects. In fact, DOvS usually implies the need to solve large-scale problems, making the efficiency a key factor when designing search algorithms. In this research work, MO-COMPASS (Multi-Objective Convergent Optimization via Most-Promising-Area Stochastic Search) is developed, as an extension of the single-objective COMPASS, for solving DOvS problems with two or more objectives by taking into consideration the Pareto optimality and the probability of correct selection. The algorithm is proven to be locally convergent, and numerical experiments have been carried out to show its ability to achieve high convergence rate.

Suggested Citation

  • Haobin Li & Loo Hay Lee & Ek Peng Chew & Peter Lendermann, 2015. "MO-COMPASS: a fast convergent search algorithm for multi-objective discrete optimization via simulation," IISE Transactions, Taylor & Francis Journals, vol. 47(11), pages 1153-1169, November.
    • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:11:p:1153-1169
    DOI: 10.1080/0740817X.2015.1005778
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    Cited by:

    1. Haobin Li & Giulia Pedrielli & Loo Hay Lee & Ek Peng Chew, 2017. "Enhancement of supply chain resilience through inter-echelon information sharing," Flexible Services and Manufacturing Journal, Springer, vol. 29(2), pages 260-285, June.
    2. Kyle Cooper & Susan R. Hunter & Kalyani Nagaraj, 2020. "Biobjective Simulation Optimization on Integer Lattices Using the Epsilon-Constraint Method in a Retrospective Approximation Framework," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1080-1100, October.
    3. Wang, Honggang, 2017. "Multi-objective retrospective optimization using stochastic zigzag search," European Journal of Operational Research, Elsevier, vol. 263(3), pages 946-960.

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