A simulation-based modified backtracking search algorithm for multi-objective stochastic flexible job shop scheduling problem with worker flexibility

Gnanavelbabu, A.; Caldeira, Rylan H.; Vaidyanathan, T.

doi:10.1016/j.asoc.2021.107960

Cited by 12 publications

(1 citation statement)

References 65 publications

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“…Based on fuzzy flexible job shop scheduling problem (FFJSP), it can reduce the impact of uncertain events in the production process and better meet the needs of actual production scheduling. Gnanavelbabu et al [7] investigated the the FFJSP with worker flexibility and uncertain processing times. Their objective was to minimize both the makespan and the standard deviation of the makespan, and they integrated Monte Carlo simulation into a multi-objective improved backtracking search algorithm framework.…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Dynamic Evolution-Based Improved MOEA/D Algorithm for Solving Multi-Objective Fuzzy Flexible Job Shop Scheduling Problem

Liu

Hou

et al. 2023

IEEE Access

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A scheduling model was developed to optimize the maximum completion time, total machine load, and maximum machine load for the fuzzy flexible job shop problem with uncertain processing times. To solve this problem, a multi-strategy dynamic evolution-based improved multi-objective evolutionary algorithm based on decomposition(IMOEA/D) was proposed. In order to enhance the quality of the non-dominated solution set and improve the algorithm efficiency. The algorithm firstly employs a strategy based on minimum processing time and workload, along with a non-dominated solution prioritization mechanism to generate the initial population. Secondly, three evolutionary strategies are incorporated, and their probabilities are dynamically adjusted with the increase of evolution generations. Finally, a variable neighborhood search method is introduced to improve the search performance of the algorithm. The effectiveness of the proposed algorithm was demonstrated through experimental validation.

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Section: Introductionmentioning

confidence: 99%