Optimization strategy of mixed-integer linear planning in logistics distribution

Abstract

Abstract The evolution of the logistics and distribution industry, notably the express delivery sector, has significantly increased its prevalence in everyday life. This escalation necessitates an ongoing innovation in logistics strategies and enhancements in service quality, positioning these elements at the heart of industry focus. This study initially addresses the vehicular route planning issue within logistics distribution, selecting Company A as a case study to examine inherent logistical challenges. Subsequently, it develops an optimal route planning model under time constraints. It is converted into a mixed-integer linear programming model through techniques such as variable substitution and segmented linear approximation. This conversion facilitates rapid solutions by mathematical planning solvers. The research contrasts the logistics and distribution performance before and after optimization to assess the efficacy of the proposed strategies. Findings indicate that optimized vehicular route planning achieves a more equitable distribution of delivery tasks, along with substantial improvements in the complexity of transportation routes and the reduction of travel distances. Specifically, the optimization results in a significant decrease in the penalty costs associated with the vehicles, with the penalty cost for the third vehicle involved in the study reduced by 66.40%. This evidence supports the implementation of scientific vehicle scheduling strategies by logistics firms, underscoring the model’s practical implications.