Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

• Published in: Complex & intelligent systems Vol. 8; no. 6; pp. 4425 - 4440
• Main Authors: Zhang, Min-Xia, Wu, Jia-Yu, Wu, Xue, Zheng, Yu-Jun
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2022; Springer Nature B.V
• Subjects: Complexity; Computational Intelligence; Customer satisfaction; Data Structures and Information Theory; Engineering; Evolutionary algorithms; Heuristic methods; Machine learning; Optimization; Original Article; Path planning; Tabu search; Water waves; Water wave optimization (WWO); Path planning; Takeaway delivery; Order selection; Evolutionary optimization; Machine learning
• ISSN: 2199-4536; 2198-6053; 2198-6053
• DOI: 10.1007/s40747-021-00410-0

The last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms.