Multi-objective green meal delivery routing problem based on a two-stage solution strategy

Abstract

This paper studies a Green Meal Delivery Routing Problem (GMDRP) which integrates the meal delivery and vehicle routing problem. It focuses on two important issue in the actual meal delivery process: order combination and routing optimization. A multi-objective scheduling model is proposed to maximize customer satisfaction and rider balance utilization, and minimize carbon footprint. Then, Nondominated Sorting Genetic Algorithm II (NSGA-II) is adopted to find an initial rider number at the first stage. Principal Component Analysis (PCA) and k-means way are used to merge the customers’ orders and generate the initial delivery routing. Adaptive Large Neighborhood Search (ALNS) is developed to improve the quality of initial solutions, thus the optimal number of riders and final delivery routing are determined at the second stage. Computational comparison on simulation instances indicate the superiority of the proposed two-stage strategy with Tabu search (TS) and Genetic Algorithm (GA): a) the two-stage strategy could optimize the number of riders, reduce carbon emissions in distribution and ensure high customer satisfaction; b) the obtained results demonstrate the efficiency of the proposed method no matter in convergence rate or solution quality; c) for different scale instances, the proposed method can ensure that all orders are delivered within 45 min, and the average delivery time of each order is no more than 4 min. Hence, this proposed method could both improve the intelligent level of delivery and greatly support the sustainable development for the dispatching system of the meal delivery platform.