DESIGN OF AN OPTIMIZED FORKLIFT ROUTES FOR A FOUR-DOOR DANGEROUS GOODS MONOLAYER WAREHOUSE THROUGH GENETIC PARTICLE SWARM OPTIMIZATION ALGORITHM

Authors

  • Jiaru Li Shanghai Maritime University
  • Fangwei Zhang Shanghai Maritime University
  • Jing Sun University of Macau
  • Guan Ke Liew National University of Singapore
  • Jinhao Zuo Shenzhen Urban Transport Planning Center

DOI:

https://doi.org/10.23055/ijietap.2020.27.2.4899

Abstract

The transportation and storage of dangerous goods are gradually increasing with the rapid development of China’s economy. To ensure safety and increase the operational efficiency of warehouses, we propose a four-door dangerous goods warehouse and a kind of route planning method for forklifts in the newly proposed warehouse. The main innovation of this study is to revolutionize the warehouse design through elevating the routing optimization problem of two forklifts operating in the four-door warehouse considered as a quadratic assignment problem (QAP). Theoretically, the classic particle swarm algorithm (PSO) is used to develop a unique genetic–discrete particle swarm algorithm (DPSO) to solve the proposed QAP. For the simulation, this study utilizes the database from real dangerous goods warehouse using the proposed genetic DPSO algorithm, after which it compares the results with the classic DPSO algorithm and other mathematical calculations. In conclusion, the four-door dangerous goods warehouse concept can improve the efficiency of warehouse management and reduce the cost of management under the condition of maintaining the original safety level, which provides a train of thought for the reform of the dangerous goods warehouse.

Published

2020-06-10

How to Cite

Li, J., Zhang, F., Sun, J., Liew, G. K., & Zuo, J. (2020). DESIGN OF AN OPTIMIZED FORKLIFT ROUTES FOR A FOUR-DOOR DANGEROUS GOODS MONOLAYER WAREHOUSE THROUGH GENETIC PARTICLE SWARM OPTIMIZATION ALGORITHM. International Journal of Industrial Engineering: Theory, Applications and Practice, 27(2). https://doi.org/10.23055/ijietap.2020.27.2.4899

Issue

Section

Supply Chain Management