Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand

Yongmao Xiao, Jincheng Zhou (), Xiaoyong Zhu and Fajun Yu
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Yongmao Xiao: School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China
Jincheng Zhou: School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China
Xiaoyong Zhu: School of Economics & Management, Shaoyang University, Shaoyang 422099, China
Fajun Yu: School of Electric and Information Engineer, Zhongyuan University of Technology, Zhengzhou 450007, China

Sustainability, 2022, vol. 14, issue 19, 1-25

Abstract: In order to solve the problem that the existing low-carbon vehicle scheduling model ignores the economic benefits of enterprises and cannot fully reflect the fuzzy needs of customers, the green simultaneous pick-up and delivery vehicle scheduling problem is studied here. With the goal of minimizing the total cost composed of service cost, fuel consumption cost, and carbon emission cost, a multi-objective comprehensive model of green simultaneous pick-up and delivery under fuzzy demand is established. In order to fully consider the objective uncertainty of customer demand and customer service time, triangular fuzzy numbers are introduced and simultaneous delivery demand is considered. An improved genetic tabu search algorithm is proposed to solve this problem. In the improved GA-TS algorithm, the penalty factor is introduced into the fitness function, the selection operator combined with elite strategy is adopted, and a mutation operator combined with tabu search algorithm is proposed. The Taguchi analysis method is used to obtain reasonable parameter settings of the GA-TS algorithm. Finally, a case study is used to verify the effectiveness of the model and hybrid algorithm. The experimental results show that the proposed comprehensive model can effectively optimize the scheduling of low-carbon simultaneous pick-up and delivery vehicles under fuzzy demand, and the effectiveness and feasibility of genetic tabu search algorithm are verified by comparing the experimental results of different algorithms and different case sizes.

Keywords: uncertain demand; low carbon; pick-up and delivery problems; genetic tabu search algorithm; comprehensive cost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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