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<p>This paper presents the first application of discrete Shark Smell Optimization (SSO) for solving multiple-row, non-identical machine layout problems. The tool, which minimizes the total rectilinear material travel distance, was applied to job shop design problems with high product variety. The work used a systematic design of experiments approach to identify appropriate parameter settings. Experiments were conducted using five benchmark datasets obtained from the literature and comparisons were made with other common nature-inspired algorithms. The results indicated that the SSO performed better than other metaheuristics for the larger problems. Novel modifications were made to the discrete SSO to further improve its performance: (i) the shark rotation movement was modified to incorporate a neighbourhood search operator; (ii) a method for selecting candidate solutions based on the flow intensity between facility pairs was included; and (iii) the number of shark rotations was increased to improve local search. The SSO was also hybridized with the Marine Predator and Whale Optimization Algorithms. The modified and hybridized SSOs were evaluated using ten datasets, which found variants that were superior up to 6.5% to existing metaheuristics and the conventional SSO. The considered strategies for modifying and hybridizing metaheuristics provide general insight into the refinement of metaheuristics.</p>

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A modified and hybrid-based optimization tool for solving multi-row, non-identical machine layout problems in multiple-product job shop production environments

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Kla Wareepitak,
  • Saisumpan Sooncharoen,
  • Pupong Pongcharoen,
  • Christian Hicks

摘要

This paper presents the first application of discrete Shark Smell Optimization (SSO) for solving multiple-row, non-identical machine layout problems. The tool, which minimizes the total rectilinear material travel distance, was applied to job shop design problems with high product variety. The work used a systematic design of experiments approach to identify appropriate parameter settings. Experiments were conducted using five benchmark datasets obtained from the literature and comparisons were made with other common nature-inspired algorithms. The results indicated that the SSO performed better than other metaheuristics for the larger problems. Novel modifications were made to the discrete SSO to further improve its performance: (i) the shark rotation movement was modified to incorporate a neighbourhood search operator; (ii) a method for selecting candidate solutions based on the flow intensity between facility pairs was included; and (iii) the number of shark rotations was increased to improve local search. The SSO was also hybridized with the Marine Predator and Whale Optimization Algorithms. The modified and hybridized SSOs were evaluated using ten datasets, which found variants that were superior up to 6.5% to existing metaheuristics and the conventional SSO. The considered strategies for modifying and hybridizing metaheuristics provide general insight into the refinement of metaheuristics.