Multi-objective dynamic feedback algorithm for solving the multi-drop three-dimensional multiple bin-size bin packing problem

Abstract

Three-dimensional multiple bin-size bin packing problem (3D-MBSBPP) is a crucial component of logistics and transportation systems. Prior studies focus on 3D-MBSBPP in cases where unloading is not considered. However, as the variety of vehicle types and the number of customers’ cargo increase, it not only reduces the efficiency of cargo loading and unloading but also increases the diversified needs of companies. To address these difficulties, more efficient methods are imperatively required. In this study, we construct a novel model for the multi-drop 3D-MBSBPP, which incorporates three objectives: maximizing vehicle space utilization rate, minimizing vehicle usage costs and spatial blockage index, while considering some practical constraints. Given the high complexity of this model, we propose a novel multi-objective dynamic feedback algorithm to solve it, which consists of three stages. Among them, Stage 1 focuses on optimizing the placement relationship between cargoes with different unloading sequences; Stage 2 dynamically adjusts and optimizes the vehicle types; and Stage 3 further optimizes the quality of the solution. More importantly, the proposed algorithm helps decision makers with the trade-off between multiple objectives. We demonstrate the effectiveness of the proposed algorithm through comparative experiments on benchmark instances and apply it to solve muti-drop 3D-MBSBPP. The results indicate that based on generating smaller spatial blockage index, the proposed algorithm can improve the average vehicle space utilization rate and reduce the average vehicle usage costs. This demonstrates the superiority of the proposed algorithm in solving the multi-drop 3D-MBSBPP.