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Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems

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  • Kulkarni, Anand J.
  • Baki, M.F.
  • Chaouch, Ben A.

Abstract

The real world problems in the supply-chain domain are generally constrained and combinatorial in nature. Several nature-/bio-/socio-inspired metaheuristic methods have been proposed so far solving such problems. An emerging metaheuristic methodology referred to as Cohort Intelligence (CI) in the socio-inspired optimization domain is applied in order to solve three selected combinatorial optimization problems. The problems considered include a new variant of the assignment problem which has applications in healthcare and inventory management, a sea-cargo mix problem and a cross-border shipper selection problem. In each case, we use two benchmarks for evaluating the effectiveness of the CI method in identifying optimal solutions. To assess the quality of solutions obtained by using CI, we do comparative testing of its performance against solutions generated by using CPLEX. Furthermore, we also compare the performance of the CI method to that of specialized multi-random-start local search optimization methods that can be used to find solutions to these problems. The results are robust with a reasonable computational time and accuracy.

Suggested Citation

  • Kulkarni, Anand J. & Baki, M.F. & Chaouch, Ben A., 2016. "Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 250(2), pages 427-447.
    • Handle: RePEc:eee:ejores:v:250:y:2016:i:2:p:427-447
    DOI: 10.1016/j.ejor.2015.10.008
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    Cited by:

    1. Alejandro Vital-Soto & Mohammed Fazle Baki & Ahmed Azab, 2023. "A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 626-668, September.

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