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Abstract
This paper studies the take-out route delivery problem (TRDP) with order allocation and unilateral soft time window constraints. The TRDP considers the order allocation and delivery route optimization in the delivery service process. The TRDP is a challenging version of vehicle routing problem. In order to solve this problem, this paper aims to minimize the total cost of delivery, builds an optimization model of this problem by using cumulative time, and adds time dimension in order allocation and path optimization dimensions. It can not only track the real-time location of delivery personnel but also record the delivery personnel to perform a certain task. The main algorithm is the dynamic allocation algorithm designed from the perspective of dispatch efficiency, and the subalgorithm is the improved genetic algorithm. Finally, some experiments are designed to verify the effectiveness of the established model and the designed algorithm, the order allocation and route optimization are calculated with/without the consideration of traffic jam, and the results show that the algorithm can generate better solution in each scene.
Highlights
At present, the take-out market in China continues to develop at an extraordinary speed, and the take-out platforms are competing in all aspects, such as high-quality supply and delivery experience. e COVID-19 has begun to erupt in all parts of the world and is spreading rapidly since the end of 2019
In the current delivery problem, depending on the experience of delivery personnel to choose the delivery order and delivery path is easy to cause secondary delivery, which reduces the delivery efficiency and increases the delivery cost and customer dissatisfaction, and less consideration is given to Mathematical Problems in Engineering the real-time distribution of delivery orders. erefore, how to improve the delivery efficiency and customer satisfaction is very important for delivery companies, businesses, and customers
The research on the deformation and algorithm of the vehicle routing problem has been relatively mature, for the delivery path optimization problem, we need to consider the constraints of one-sided soft time window and the special requirements of first take and send; for the regional delivery system, we must consider the distribution between the delivery order and delivery personnel
Summary
The take-out market in China continues to develop at an extraordinary speed, and the take-out platforms are competing in all aspects, such as high-quality supply and delivery experience. e COVID-19 has begun to erupt in all parts of the world and is spreading rapidly since the end of 2019. Liu et al [17] in order to solve the problem of resource allocation and carbon emission in green vehicle path optimization, proposed a hybrid quantum immune algorithm based on cloud model (c-hqia), which can effectively improve the distribution efficiency and reduce the distribution demand Ben. Li et al [18] comprehensively considered the vehicle routing and fleet size decision-making problems of various vehicle types when studying the multistage heterogeneous fleet scheduling problem, transformed the problem into a mixed integer programming model, and proposed a new heuristic algorithm based on greedy algorithm and simulated annealing algorithm. The research on the deformation and algorithm of the vehicle routing problem has been relatively mature, for the delivery path optimization problem, we need to consider the constraints of one-sided soft time window and the special requirements of first take and send; for the regional delivery system, we must consider the distribution between the delivery order and delivery personnel.
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