Winner determination problem in multiple automated guided vehicle considering cost and flexibility. (April 2020)

• Record Type: Journal Article
• Authors: Lee, Chen Wei; Wong, Wai Peng; Ignatius, Joshua; Rahman, Amirah; Tseng, Ming-Lang
• Is Part Of: Computers & industrial engineering. Volume 142(2020)
• Publication Date: 2020-04
• Subjects: Automated guided vehicle -- Combinatorial auction -- Genetic algorithm -- Knowledge-based systems -- Autonomous vehicles; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106337
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Suggest a framework to reduce the cost of using multiple automated guided vehicle. The proposed genetic algorithm provides convincing solutions. The proposed GA provides better solutions and better flexibility than MILP. For large complex cases, the proposed GA method supersedes MILP. Abstract: This study solves the integration difficulty between scheduling and routing aspects of the multiple Automated Guided Vehicle (AGV) problem through the winner determination problem (WDP). Our model reduces the cost of using multiple AGV for logistics and warehousing applications. We show that solving the WDP by the mixed integer linear programming (MILP) is inefficient as the assignment of routes is made complicated by the combination of large number of AGVs. We illustrate an efficient approach through our proposed genetic algorithm using knowledge based operators, which decomposes a non-linear combinatorial auction model into a linear model. Simulation results showed the efficacy of MILP, the conventional GA and our proposed GA-based method. However, MILP only works well for small scale data. When the number of routes and AGV increases, the proposed GA method supersedes the other methods as indicated by cost and route flexibilities.