关键词: 生鲜商品物流配送, 多车舱多车型, 温度控制, 车辆路径问题, NI-MOACO算法

Abstract: The demand for fresh commodities has been growing continuously in recent years, with the improvement of life quality and high demands for a healthy diet. Accordingly, the fresh commodity logistics distribution business is constantly expanding due to the requirements of fresh commodities. However, due to the characteristics of perishability, timeliness and temperature control heterogeneity of fresh commodities, the fresh commodity logistics delivery demand is notably more than those of other commodities. As different types of fresh commodities are different in temperature control conditions suitable for storage and the traditional single compartment, a single-type refrigerated vehicle distribution mode fails to meet the distribution demand of multi-type fresh commodities. In the fresh commodity logistics delivery process, multi-compartment multi-type refrigerated vehicles can effectively fulfill the temperature control conditions of multi-type fresh commodities and flexibly respond to the diverse distribution demand, contributing to the shared scheduling of refrigerated vehicles in different service periods and reducing the fresh logistics operating cost. Therefore, in view of the deficiencies of the fresh commodity logistics distribution vehicle routing problem research in the combination of temperature control of fresh commodity and multi-compartment multi-type vehicle distribution, a fresh commodity logistics distribution vehicle routing problem integrating temperature control and multi-compartment multi-type vehicle distribution optimization is proposed.
In the first part, considering the perishability of fresh commodity and the timeliness characteristic of the delivery process, a bi-objective optimization model with the minimum fresh logistics operating cost, including distribution cost, vehicle rental cost, temperature control cost, value loss of fresh commodity, penalty cost for violating the time window and the minimum number of refrigerated distribution vehicles, is proposed.
In the second part, a NI-MOACO algorithm is designed to solve the developed model. The NI algorithm is applied to generate the initial solution by combining the geographic locations of the fresh distribution center and the customers. In MOACO, integrating the convergence and diversity evaluation values of optimized solutions, the adaptive pheromone updating mechanism strengthens the global search and optimization performance of the solution space. Besides, based on the customer traversal sequence, the number of compartments and loading restrictions, a vehicle-type-matching strategy segments the routes and matches the vehicle types according to the loading rates. In addition, a vehicle-sharing strategy is designed to achieve the sharing of refrigerated distribution vehicles among multiple service periods. Then, the NI-MOACO algorithm is compared with multi-objective particle swarm algorithm, non-dominated sorting genetic algorithm and multi-objective variable neighborhood search algorithm in 10 groups of case data, and then the fresh logistics operating cost, the number of refrigerated distribution vehicles and computation time are compared and analyzed to verify the effectiveness and stability of the proposed model and algorithm.
In summary, the proposed model and algorithm are demonstrated with the real-world data of a fresh commodity logistics and distribution enterprise in Chongqing, China. The changes in value loss, temperature control cost, fresh logistics operating cost, and the number of refrigerated distribution vehicles before and after the optimization are explored. The results show that flexible scheduling of multi-compartment multi-type refrigerated distribution vehicles and consideration of shared vehicles among multiple service periods can effectively respond to the distribution demand for fresh commodities, reduce fresh logistics operating costs and the number of refrigerated distribution vehicles, and promote the efficiency of the fresh logistics delivery network. Furthermore, this study compares and analyzes the optimization results for four different compartment capacities, and the results suggest that 500kg is the optimal compartment capacity, which is preferable to the other compartment capacities in fresh logistics operating costs and the number of vehicles. This study provides methodological references and decision support for fresh commodity logistics distribution enterprises in multi-compartment multi-type vehicle distribution scheduling.

Key words: fresh commodity logistics distribution, multi-compartment multi-type vehicle, temperature control, vehicle routing problem, NI-MOACO algorithm