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<p>In recent years, the efficiency and reliability of supply chain operations have become critical for businesses striving to maintain competitiveness in an increasingly dynamic market environment. This study proposes an optimized design to enhance supply chain efficiency by improving system reliability and implementing co-design policies. Specifically, we develop a production-inventory system that incorporates a dual maintenance mechanism and an (<i>s</i>, <i>S</i>) replenishment policy, considering the potential failure risks associated with material distribution equipment. Through state analysis and steady-state probability derivation, we present system operation indices, the material distribution equipment and system reliability indices, and a cost function. We then construct a bi-objective optimization model to maximize market demand satisfaction ability (MDSA) and minimize the system cost per unit of time. To solve this model, we employ the multi-objective particle swarm optimization (MOPSO) technique to determine the optimal number of production workshops and the replenishment policy. Additionally, we propose an algorithm based on Monte Carlo simulation to estimate the system performance indices. Numerical experiments validate the effect of maintenance policy on system reliability.</p>

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Performance Analysis and Policy Optimization of Production-Inventory Systems Based on Dual Maintenance Mechanism

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Jing Li,
  • Lin-Min Hu,
  • Rui Peng,
  • Fan Xu

摘要

In recent years, the efficiency and reliability of supply chain operations have become critical for businesses striving to maintain competitiveness in an increasingly dynamic market environment. This study proposes an optimized design to enhance supply chain efficiency by improving system reliability and implementing co-design policies. Specifically, we develop a production-inventory system that incorporates a dual maintenance mechanism and an (s, S) replenishment policy, considering the potential failure risks associated with material distribution equipment. Through state analysis and steady-state probability derivation, we present system operation indices, the material distribution equipment and system reliability indices, and a cost function. We then construct a bi-objective optimization model to maximize market demand satisfaction ability (MDSA) and minimize the system cost per unit of time. To solve this model, we employ the multi-objective particle swarm optimization (MOPSO) technique to determine the optimal number of production workshops and the replenishment policy. Additionally, we propose an algorithm based on Monte Carlo simulation to estimate the system performance indices. Numerical experiments validate the effect of maintenance policy on system reliability.