|Table of Contents|

Vehicle routing optimization of intra-city freight considering demand change and time-varying road network(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年4期
Page:
137-150
Research Field:
交通工程
Publishing date:

Info

Title:
Vehicle routing optimization of intra-city freight considering demand change and time-varying road network
Author(s):
XI Jiang-peng1; 2;  ZHANG Jia-rui2;  DONG Hong-xia2;  WANG Ning2
(1. Shaanxi Transportation Holding Group Co. Ltd., Xi'an 710065, Shaanxi, China; 2. School of Transportation Engineering, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
traffic engineering;  vehicle routing;  quantum-behaved particle swarm optimization;  dynamic demand;  urban less-than-truckload transportation;  time varying road network
PACS:
U492.3
DOI:
10.19721/j.cnki.1671-8879.2023.04.014
Abstract:
In order to provide customers with higher quality services, a scientifically and reasonably designed algorithm was utilized to match vehicles with cargo and optimize vehicle routes. The real-time changes in orders from the local freight platform were combined comprehensive factors such as vehicle utilization costs, fuel costs, default costs incurred by delays or early arrivals at the consignee's location, and waiting costs to avoid road congestion or excessive default costs. Aimed to minimize the objective function, a vehicle path optimization model considering vehicle-cargo matching, time-varying urban road networks and other constraints was built. A hybrid quantum-behaved particle swarm optimization algorithm was designed to determine the optimal cargo matching scheme, vehicle routes, and vehicle operation time schedule. The results show that the improved quantum particle swarm algorithm produces optimized solutions for small-scale cases with a relative deviation of 3.7%, compared to the optimal solutions obtained by theCplex optimization software. However, the average solving time for the proposed algorithm is only54.84 s, while the average solving time for Cplex is 8 640.80 s. Within a reasonable planning period, the proposed route planning model can effectively convert default costs into lower-cost waiting costs by adjusting the departure time of vehicles. Alternatively, it can reduce default and waiting costs by sacrificing fuel consumption costs, thereby reducing the overall operational costs of the platform.When unit waiting costs account for 30% of unit default costs, considering time-varying road networks can effectively reduce the overall operational costs of the platform. When unit waiting costs account for 20% of unit default costs, considering time-varying road networks can reduce the platform's operational costs by 0.7%. When waiting costs are not considered, considering time-varying road networks can reduce the platform's operational costs by 10.6%.11 tabs, 11 figs, 28 refs.

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Last Update: 2023-08-20