A two-stage decomposition method on fresh product distribution problem
针对生鲜配送中冷藏车受环境温度影响导致制冷成本变化的问题，建立时间依赖混合整数规划模型，提出结合变邻域搜索和粒子群优化的自适应启发式算法，并设计两阶段分解方法将问题拆解为最短路径和发车时间调度两个子问题，以降低总运营成本。
Refrigerator cars are widely used for fresh product distribution. The energy consumption of these vehicles is sensitive to the environment temperature, and changes continuously due to fluctuations of the environment temperature. As a result, the total refrigeration cost is influenced by the car’s departure time. To reduce operation costs of third-party transportation providers, the refrigerator car scheduling problem is addressed in this research. A time-dependent mixed-integer programming model is established to reduce total operation costs, including routing, time penalty, cargo damage and refrigeration costs. An adaptive heuristic method is proposed by combining the variable neighbourhood search and particle swarm optimisation. To improve the algorithm quality, a two-stage decomposition method is developed. The problem is divided into two echelon sub-problems. One is the shortest path problem, and the other is the departure time scheduling problem. A feedback strategy is utilised to avoid local optimal solutions and design of experiments methodology is adopted to derive the optimal parameter setting of the algorithm. Numerical experiments are conducted to demonstrate the effectiveness of the proposed time-dependent decision model.