Author(s)

Qi Zhang¹, Qiaozhen Zhang^2*

¹School of Mathematical Sciences, Nankai University, Tianjin, China.
²The Key Laboratory of Pure Mathematics and Combinatorics, Ministry of Education, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Laboratory for Economic Behaviors and Policy Simulation, School of Statistics and Data Science, Nankai University, Tianjin, China.

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

Electric Material Handling Vehicles, Battery Swap Station Location, Scheduling Scheme, NSGA-II Algorithm, Ant Colony Optimization Algorithm

Zhang, Q. and Zhang, Q.Z. (2023) Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis. Journal of Applied Mathematics and Physics, 11, 2630-2643. doi: 10.4236/jamp.2023.119172.