Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy co...

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Bibliographic Details
Published in:International journal of production research 2020-02, Vol.58 (3), p.846-862
Main Authors: Fang, Yilin, Ming, Hao, Li, Miqing, Liu, Quan, Pham, Duc Truong
Format: Article
Language:English
Subjects:
Balancing
Computer simulation
Cycle time
Disassembly tasks
Dismantling
Energy consumption
Evolutionary algorithms
Heuristic methods
Mathematical programming
mixed-model disassembly line
multi-objective optimisation
multi-robotic workstation
Multiple objective analysis
Optimization
Production scheduling
Robots
Simulated annealing
simulated annealing algorithm
uncertain processing time
Uncertainty
Workstations
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Summary:This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2019.1602290