Scholarlink

<p>We propose an Economic Order Quantity (EOQ) model for perishable items whose lifetimes follow a Gompertz-Makeham law, explicitly modeling two deterioration phases separated by a threshold time: items sold at the regular price in the first (fresh) phase and at a discounted price in the second (discount) late-life phase. The model yields closed-form structural expressions for on-hand inventory, total holding time, sales volume, and wastage, and leads to a highly nonlinear average total cost function that contains nested integrals of survival terms. Because the average total cost landscape is nonlinear, we solve the joint optimization over the threshold time and cycle length by a global search optimization algorithms (Differential Evolution, Particle Swarm Optimization, and Genetic Algorithm), and we perform a targeted sensitivity analysis with respect to: costs, deterioration parameters, search space and demand rate shape. Numerical experiments demonstrate low sensitivity of the optimal threshold time with all three algorithms for the first three changes, while when the shape of the demand rate changes, the algorithms provide slightly different results and their interpretations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Modeling an EOQ Inventory Model with Two-Period Deterioration-Based Threshold Time and Different Selling Prices: Optimization Challenges for Rapid Late-Life Deterioration

  • 期刊论文

  • 发表日期：

    2026年1月29日

查看全文
  • Stefan Mirchevski,
  • Zhivko Atanaskoski

摘要

We propose an Economic Order Quantity (EOQ) model for perishable items whose lifetimes follow a Gompertz-Makeham law, explicitly modeling two deterioration phases separated by a threshold time: items sold at the regular price in the first (fresh) phase and at a discounted price in the second (discount) late-life phase. The model yields closed-form structural expressions for on-hand inventory, total holding time, sales volume, and wastage, and leads to a highly nonlinear average total cost function that contains nested integrals of survival terms. Because the average total cost landscape is nonlinear, we solve the joint optimization over the threshold time and cycle length by a global search optimization algorithms (Differential Evolution, Particle Swarm Optimization, and Genetic Algorithm), and we perform a targeted sensitivity analysis with respect to: costs, deterioration parameters, search space and demand rate shape. Numerical experiments demonstrate low sensitivity of the optimal threshold time with all three algorithms for the first three changes, while when the shape of the demand rate changes, the algorithms provide slightly different results and their interpretations.