Optimisation of takeaway delivery routes considering the mutual satisfactions of merchants and customers

Ren, Teng; Xu, Hongbo; Kang-ning, Jin; Luo, Tianyu; Ling, Wang; Xing, Lining

doi:10.1016/j.cie.2021.107728

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…We compare this study with previous research work to show the differences in Table 1. Firstly, we propose a meal delivery routing optimization model with the objective of time-sensitive customer satisfaction extending the prior work of Liao et al [7] and Teng et al [12], unlike the cost minimization objective in most existing MDRP literature [6]. Secondly, we are differently taking into consideration of heterogeneous time-sensitive customers by introducing the time sensitivity coefficient in the customer time satisfaction function of the MDRP model.…”

Section: Literature Reviewmentioning

confidence: 97%

“…Customer satisfaction with delivery time that could be employed to evaluate the quality of the fast services is receiving increasing attention [12,13]. Kuppelwieser and Maggard [14] analyzed customer satisfaction with wait time in a two-stage service process.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Liao et al [7] described customer satisfaction of delivery time with fuzzy membership function in multi-objective green meal delivery routing problem. Teng et al [12] constructed a VRP model for takeaway delivery by transforming the satisfaction of merchants and customers into a penalty function and aiming to minimize the total delivery cost.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zhou et al [33] designed a hybrid multi-population genetic algorithm to solve a real-world territory design problem of a major dairy company with the objective of minimizing the total operational cost. A variety of heuristic algorithms including the genetic algorithm and the greedy adaptive neighborhood heuristic [7,[9][10][11][12] have been applied in meal delivery routing optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e time-sensitive customer satisfaction function S i [12] of meal order i with the time sensitivity coefficient β is shown in equation ( 2). e average time-sensitive customer satisfaction in equation ( 1) is calculated by dividing the sum of m k meal orders' satisfaction by the number of meal orders m k in the delivery bundle k. e time-sensitive customer satisfaction function, as shown in equation (2), is applied to measure customer satisfaction with the considerations of heterogeneous time sensitivity to delayed meal delivery.…”

Section: Mathematical Formulationmentioning

confidence: 99%

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

Wang

Jiang

2022

Journal of Advanced Transportation

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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.

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Section: Literature Reviewmentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%