A hybrid algorithm for time-dependent vehicle routing problem with soft time windows and stochastic factors. (March 2022)

• Record Type: Journal Article
• Authors: Jie, Ke-Wei; Liu, San-Yang; Sun, Xiao-Jun
• Is Part Of: Engineering applications of artificial intelligence. Volume 109(2022)
• Publication Date: 2022-03
• Subjects: Combinatorial optimization -- TDVRP -- Soft time windows -- Stochastic factors -- Hybrid algorithm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104606
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In order to meet the logistics and distribution requirements with poor actual traffic conditions, this paper considers adding soft time windows and stochastic factors (road traffic congestion, weather changes, etc.) based on classic vehicle routing problem. Firstly, we construct a time-dependent vehicle routing problem (TDVRP) with soft time windows model with the objective function of minimizing the total cost of distribution. A Hybrid Algorithm (HA) combining Sweep Algorithm (SA) and Improved Particle Swarm Optimization (IPSO) is developed to solve this model. Secondly, we compare IPSO with other algorithms through benchmark test functions and traditional vehicle routing problem cases. Finally, By applying HA to the modified Solomon benchmark test set, this paper compares HA with the existing solutions in the number of vehicles used, total transportation distance and total waiting time. Then experimental results manifest the practicability of HA.