Scheduling a Constrained Hybrid Flowshop Using a Variable Representation Cooperative Co-Evolutionary Algorithm

doi:10.1016/j.eswa.2026.131136

Scheduling a Constrained Hybrid Flowshop Using a Variable Representation Cooperative Co-Evolutionary Algorithm

2026-01-13

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杨圣祥 (Shengxiang Yang)

DOI：10.1016/j.eswa.2026.131136

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The research addresses a hybrid flowshop scheduling problem incorporating worker competency constraints. Unlike most existing studies that assume workers can operate all machines, our work accounts for the absence of certain worker skills. The added constraints substantially increase the problem’s complexity, rendering traditional algorithms inadequate for obtaining feasible solutions. Therefore, a mixed-integer programming model is formulated, and a variable representation cooperative co-evolutionary algorithm (VRCCEA) is designed to achieve makespan minimization. Based on the decomposition idea, we use two populations to address the multi-coupled problem and implement a cooperative mechanism by introducing a solution archive to promote the co-evolution of populations. Given the limitations of a single encoding–decoding strategy, a variable representation mechanism is provided to balance the exploration scale and search efficiency. To prevent the failures of worker assignment, we design a heuristic based on resource constraint matrix (RCM), which conducts a greedy search within the feasible region. For the problem-specific knowledge, a reduced insertion neighborhood and an accelerated evaluation strategy are proposed to swiftly identify the best neighborhood solution. Finally, analytical experiments show the practical value of the algorithmic components and demonstrate that VRCCEA significantly outperforms five advanced metaheuristics.

Scheduling a Constrained Hybrid Flowshop Using a Variable Representation Cooperative Co-Evolutionary Algorithm

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