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February 6, 2026Axioms0 citationsOpen Access

Research on Multi-Objective Flexible Job-Shop Scheduling Problem Considering Quality Inspection and Job Priorities

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CZChuchu ZhengZXZhiqiang Xie

Key Points

  • The study aims to optimize job scheduling by minimizing makespan and energy consumption while maximizing processing quality.
  • Developed an optimization model integrating quality inspection and job priority constraints.
  • Designed an improved multi-objective evolutionary algorithm based on decomposition.
  • Utilized a two-vector encoding scheme and a product-group repair mechanism.
  • Implemented a niching-based elite archive strategy for maintaining solution diversity.
  • Incorporated strategies for quality enhancement and a local search mechanism.
  • The proposed algorithm demonstrated superior performance in convergence compared to competing algorithms.
  • Quality enhancement strategies significantly improved processing quality.
  • Diversity among non-dominated solutions was effectively maintained.

Abstract

Quality inspection is a crucial step in ensuring product conformity and avoiding rework waste, while job priority constraints are prevalent in the production of complex products with assembly structures. This paper presents a modeling and solution framework for the multi-objective flexible job shop scheduling problem that incorporates both quality inspection activities and job priority constraints. An optimization model is constructed with the objectives of minimizing the makespan, minimizing the total energy consumption, and maximizing the processing quality. To solve this model, an improved multi-objective evolutionary algorithm based on decomposition is developed, which integrates several well-established mechanisms into a unified framework. The algorithm integrates multi-product assembly structures via virtual nodes, employs a two-vector encoding scheme, and incorporates a product—group repair mechanism based on binary sorting tree to handle job priority constraints. To maintain diversity among non-dominated solutions, a niching-based elite archive strategy is adopted. Furthermore, a quality enhancement strategy and a memory vector-based local search mechanism are embedded to strengthen the algorithm’s search capability. Simulation results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of both convergence and diversity.

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Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/698586238f7c464f2300a1a7https://doi.org/10.3390/axioms15020118
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  4. 4Scheduling stochastic distributed flexible job shops using an multi-objective evolutionary algorithm with simulation evaluation2024 · 29 citations
  5. 5Knowledge-driven teaching-learning-based optimization algorithm for bi-objective flexible job-shop scheduling problem with tool allocation2026