A Buffer-Based Ant Colony System Approach for Dynamic Cold Chain Logistics Scheduling

Abstract

Cold chain logistics (CCL) scheduling is an emerging research problem in the logistics industry in smart cities, which mainly concerns the distribution of perishable goods. As the quality loss of goods that occurs in the distribution process should be considered, the CCL scheduling problem is very challenging. Moreover, the problem is more challenging when the dynamic characteristics (e.g., the orders are unknown beforehand) of the real scheduling environment are considered. Therefore, this paper focuses on the dynamic CCL (DCCL) scheduling problem by establishing a practical DCCL model. In this model, a working day is divided into multiple time slices so that the dynamic new orders revealed in the working day can be scheduled in time. The objective of the DCCL model is to minimize the total distribution cost in a working day, which includes the transportation cost, the cost of order rejection penalty, and the cost of quality loss of goods. To solve the DCCL model, a buffer-based ant colony system (BACS) approach is proposed. The BACS approach is characterized by a buffering strategy that is carried out at the beginning of the scheduling in every time slice except the last one to temporarily buffer some non-urgent orders, so as to concentrate on scheduling the orders that are preferred to be delivered first. Besides, to further promote the performance of BACS, a periodic learning strategy is designed to avoid local optima. Comparison experiments are conducted on test instances with different problem scales. The results show that BACS is more preferred for solving the DCCL model when compared with the other five state-of-the-art and recent well-performing scheduling approaches.