Two-Stage Solution for Meal Delivery Routing Optimization on Time-Sensitive Customer Satisfaction

Abstract

The online-to-offline (O2O) meal delivery mode in which takeout meals are ordered online and delivered offline is recently emerging. The fast delivery of huge meal orders for time-sensitive customers imposes great challenges on O2O meal delivery platforms. This study establishes a two-stage solution for meal delivery routing optimization with the objective of maximizing time-sensitive customer satisfaction. In the first stage, a large number of meal orders are hierarchically classified and merged into delivery bundles based on the nearest pickup location rule by applying the hierarchical agglomerative clustering (HAC) algorithm, to increase fast meal delivery efficiency. In the second stage, a genetic algorithm (GA) is applied to solve the cluster-based delivery routing optimization model to find an optimal delivery route for meal orders in each delivery bundle. The numerical simulation results verify that the two-stage routing optimization solution is effective to schedule timely meal delivery and improve customer time satisfaction. The comparison of the results indicates the superiority of the proposed two-stage solution with HAC and GA on customer satisfaction while ensuring the delivery of all orders within 60 minutes. The sensitivity analysis shows the impact of time-sensitive customer heterogeneity on meal delivery satisfaction. This research has significant managerial insights for fast delivery services of O2O meal delivery platforms.

Waiting Strategy for the Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers Using a Hybrid Adaptive Genetic Algorithm and Adaptive Large Neighborhood Search Algorithm

“…(1) We develop a multi-objective optimization model based on timesensitive heterogeneous customers to solve the MDRP, which considers both perspectives of improving customer time satisfaction and reducing delay penalty cost and riding cost. This study enriches previous works on MDRP [7,8,13]. (2) A novel waiting strategy with time-sensitive priority is proposed, which further extends the research methods of MDRP for handling dynamic meal orders.…”

“…In order to avoid order delays caused by uncertain information during dynamic delivery, Ulmer et al [7] proposed an anticipatory customer assignment (ACA) policy, which introduced a postponement to improve the flexibility of order assignment and a time buffer to reduce decisions that may cause delays. Wang and Jiang [13] developed a two-stage optimization algorithm to study customer satisfaction with time-sensitive heterogeneity. They found that considering time-sensitive heterogeneity was effective in improving customer time satisfaction.…”

“…Unlike previous works such as [7,8,11,12,14], which assumed customers' homogeneous demands, we consider the time-sensitive heterogeneity of customers when performing meal delivery routes, and develop a multi-objective optimization model for improving customer time satisfaction and reducing delay penalty cost and riding cost. Particularly, while Wang and Jiang [13] explored the MDRP considering the time-sensitive heterogeneity of customers, their study only analyzed the delivery problem for meal orders in a static scenario. In contrast, we extend their work to investigate a dynamic scenario where orders are generated dynamically.…”

“…In this section, we first give a detailed problem sent the relevant settings for the dynamic problem. lation of the static subproblem is presented in detail RH Heuristic Wang and Jiang [13] MDRP 1. Our study stands out by integrating various factors such as time-sensitive heterogeneity of customers, dynamic demands, time window, pick and delivery, multiple objectives, and waiting strategy into the MDRP.…”

“…In the MDRP, a critical aspect of maximizing customer time satisfaction is ensuring that meal orders are delivered within the estimated time window [13]. In order to evaluate whether an order is delivered at the appropriate time, we employ the fuzzy membership function to formulate the customer satisfaction model.…”