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

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.

Multi-objective green vehicle scheduling problem considering time window and emission factors in ship block transportation

“…Chen et al 21 studied the cold chain green multi warehouse vehicle routing problem (CC-GMD-VRPTW-MF) with time windows and mixed fleets for urban logistics distribution using electric vehicles (EVs) and gasoline diesel vehicles (GDVs). Xiao et al 22 established a multi-objective comprehensive model of green simultaneous delivery under fuzzy demand to minimize the total cost composed of service cost, fuel consumption cost, and carbon emission cost. For the multi-site vehicle path problem under time-varying road networks, Fan et al 23 proposed an integer planning model with the minimum total cost and designed a hybrid genetic algorithm with variable neighbourhood search for this problem.…”

Research on Multi-UAV Task Assignment Based on a Multi-Objective, Improved Brainstorming Optimization Algorithm

“…To ensure that the generated solution can meet these constraints, researchers usually introduce penalty functions in the model to improve the feasibility and operability of the solution. For example, Xiao et al [11] proposed an improved genetic tabu search algorithm and introduced the penalty factor into the fitness function. Monami et al [12] and Yang et al [13] specified a delivery time range and imposed early and delayed delivery penalties on deliveries beyond that time range to establish a realistic coordination mechanism in the supply chain system.…”