Urban crowdsourcing distribution task optimization scheduling method based on path planning

Abstract

The invention provides an urban crowdsourcing distribution task optimization scheduling method based on path planning. The method comprises the following steps: constructing a crowdsourcing distribution network graph; acquiring crowdsourcing riders and crowdsourcing distribution task information; constructing a crowdsourcing distribution task optimization scheduling model based on path planning; solving the initial crowdsourcing task scheduling scheme based on a greedy strategy; and carrying out optimal scheduling on the crowdsourcing distribution task based on variable neighborhood search. According to the invention, according to position information of riders, merchants and clients, an optimized task scheduling scheme including a distribution task set and a shortest distribution path sequence of each rider is formulated according to the time constraint of each task and the real-time load constraint of each rider, so that the tasks can be reasonably distributed, the length of a totaldistribution path can be reduced, and the crowdsourcing distribution cost can be reduced.

Description

Technical field [0001] The invention belongs to the field of logistics resource scheduling, and specifically relates to a method and support tool for optimizing scheduling of urban crowdsourcing distribution tasks based on path planning. Background technique [0002] With the rapid development of Internet-based e-commerce and the O2O industry, people have higher and higher requirements for the timeliness of logistics and distribution. Traditional logistics and distribution are completed by full-time couriers of logistics companies. This method of distribution is difficult to respond to many in time. Consumer service needs. In response to the current shortage of logistics and distribution resources, an urban crowdsourcing distribution model has emerged. Urban crowdsourcing distribution transfers the distribution tasks originally undertaken by the full-time express personnel of logistics companies to the masses outside the enterprise to complete it in a free and voluntary manner. ...

AI Technical Summary

Problems solved by technology

[0005] At present, there are many studies on the optimal scheduling of urban crowdsourcing delivery tasks under the dispatch mode. These studies either optimize task allocation to improve the task allocation rate to optimize task dispatch, or optimize the matching relationship between tasks and riders. Which riders are more suitable for the task, or to optimize the choice of riders, but some studies only assign tasks to the starting point of the task, ignoring the end of the task, the service time of the task merchant and the service time of the task customer, etc. Other factors that affect task allocation and rider distribution

Images