基于 IPSO-SA 算法的医用耗材多周期库存优化

Multi-period Inventory Optimization of Medical Consumables Based on an IPSO-SA Algorithm

摘要

针对医用耗材企业分布式仓储体系下，多仓补货决策复杂、库存成本与服务水平难以协同优化的问题，提出一种面向多仓协同补货的库存优化方法。在此基础上，建立以库存持有成本、缺货损失成本和运输成本最小化为目标的库存优化模型，并将产品效期、批次管理、运输方式经济性及补货量限制等业务约束纳入模型。算法部分提出一种粒子群优化−模拟退火两阶段混合算法。在粒子群优化算法基础上，通过全局搜索以获得较优初始解，引入模拟退火算法的概率突跳机制以跳出局部最优，实现多周期补货量与运输方案的协同优化，同时结合滚动时域机制动态生成未来 4 周期的补货计划，提高补货决策的动态适应能力。案例研究结果表明，与企业现行补货策略相比，优化方案使总运营成本下降约 39.23%，其中库存持有成本降低 38.60%，运输成本降低 62.31%，缺货损失成本降低 39.43%。研究结果表明，该方法能够有效支持多仓环境下医用耗材补货决策，为企业供应链库存优化提供可行的决策支持方法。

Abstract

To address the complexity of multi-warehouse replenishment decision-making and the difficulty of balancing inventory cost and service levels in distributed warehousing systems for medical consumables, a collaborative inventory optimization method for multi-warehouse replenishment is proposed. To this end, an inventory optimization model is developed to minimize inventory holding, shortage, and transportation costs, while incorporating practical operational constraints such as product shelf life, batch management, transportation mode selection, and replenishment quantity limits. Furthermore, a two-stage hybrid algorithm integrating particle swarm optimization (PSO) and simulated annealing (SA) is proposed. On this basis, global search is first performed to obtain high-quality initial solutions. Then, the probabilistic jumping mechanism of SA is introduced to escape local optima, enabling coordinated optimization of multi-period replenishment quantities and transportation schemes. A rolling horizon mechanism is further incorporated to dynamically generate replenishment plans for the next four periods, thereby improving the adaptability of replenishment decisions. Case study results show that, compared with the enterprise′s current replenishment strategy, the proposed optimization scheme reduces the total operating cost by approximately 39.23%, including reductions of 38.60% in inventory holding cost, 62.31% in transportation cost, and 39.43% in shortage cost. The results also demonstrate that the proposed method can effectively support replenishment decision-making in multi-warehouse environments and provide practical decision support for inventory optimization in medical consumables supply chains.

关键词

医用耗材 / 库存优化 / 多周期补货 / 改进粒子群优化−模拟退火算法 (IPSO-SA)

Key words

medical consumables / inventory optimization / multi-period replenishment / improved particle swarm optimization-simulated annealing (IPSO-SA) algorithm

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基金资助

国家自然科学基金项目(52375498)