SMC and RAS Chapter Seminar on An Improved Multi-objective Discrete Brainstorming Optimization Algorithm for Multi-robotic Multi-product U-shaped Disassembly-line-balancing Problems : vTools Events

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SMC and RAS Chapter Seminar on An Improved Multi-objective Discrete Brainstorming Optimization Algorithm for Multi-robotic Multi-product U-shaped Disassembly-line-balancing Problems

#Multi-objective #Discrete #Brainstorming #Optimization #Multi-robotic #Multi-product #U-shaped #Disassembly-line

IEEE Northern Jersey Section, SMCS Chapter and RAS Chapter Seminar

An Improved Multi-objective Discrete Brainstorming Optimization Algorithm for Multi-robotic Multi-product U-shaped Disassembly-line-balancing Problems

Dr. Xiwang Guo, Associate Professor

College of Computer and Communication Engineering

Liaoning Shihua University, Fushun, China

Location: Room 405 FMH, Newark, NJ 07102 and https://njit.webex.com/meet/zhou

Time: 6:15-7:15pm, US Eastern Time, Tue., Dec. 7, 2021

Abstract—A disassembly line is an effective way to deal with large-scale end-of-life products. Robotic disassembly can improve efficiency and reduce the cost of disassembly. This paper is the first attempt to study an Multi-robotic Multi-product U-shaped Disassembly-line-balancing (MMUD) problem. In the MMUD problem, multiple robots are assigned to a workstation to perform the disassembly task without precedence and conflict relationships, so that different types of end-of-life products can be disassembled on a U-shaped disassembly line at the same time. A mathematical model is established to maximize the disassembly profit and minimize the smoothness index. An Improved Multi-objective Discrete Brainstorming Optimization (IMDBO) algorithm is proposed to solve the MMUD problem. The proposed IMDBO algorithm is applied to a set of MMUD problem instances introduced in this paper and compared with four classical multi-objective optimization algorithms. The experimental results show that the proposed IMDBO algorithm achieves better performance in most instances.

Xiwang Guo received his B.S. degree in Computer Science and Technology from Shenyang Institute of Engineering, Shenyang, China, in 2006, M.S. degree in Aeronautics and Astronautics Manufacturing Engineering. from Shenyang Aerospace University, Shenyang, China, in 2009, Ph. D. degree in System Engineering from Northeastern University, Shenyang, China, in 2015. He is currently an associate professor of the College of Computer and Communication Engineering at Liaoning Shihua University. From 2016 to 2018, he was a visiting scholar of Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA. He has authored 80+ technical papers in journals and conference proceedings, including IEEE Transactions on Cybernetics, IEEE Transactions on System, Man and Cybernetics: Systems, IEEE Transactions on Intelligent Transportation Systems, and IEEE/CAA Journal of Automatica Sinica. His current research interests include Petri nets, remanufacturing, recycling and reuse of automotive, intelligent optimization algorithm.