Energy aware semi-automatic assembly line balancing problem considering ergonomic risk and uncertain processing time

The semiautomated assembly line balancing problem (SAALBP) is a complex planning problem in which the production process comprises human workers, robots, and human-robot collaboration. With increasing awareness of sustainable practices in the manufacturing environment, this study addresses a sustain...

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Vydáno v:Expert systems with applications Ročník 231; s. 120737
Hlavní autoři: Rahman, Humyun Fuad, Janardhanan, Mukund Nilakantan, Ponnambalam, S.G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 30.11.2023
Témata:
Chance constraint programming
Cycle time
Energy
Ergonomic risks
Memetic algorithm
Semi-automated assembly line
Ergonomic risks
Chance constraint programming
Memetic algorithm
Energy
Semi-automated assembly line
Cycle time
ISSN:0957-4174, 1873-6793
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Shrnutí:The semiautomated assembly line balancing problem (SAALBP) is a complex planning problem in which the production process comprises human workers, robots, and human-robot collaboration. With increasing awareness of sustainable practices in the manufacturing environment, this study addresses a sustainable SSALBP, where a mixed integer linear programming (MILP) model is formulated for the first time for this type of assembly systems. The model consists of three objectives and each of the objectives represents the three pillars of sustainability. The problem becomes more realistic by considering uncertainty in processing time and the influence of human workers’ age and skill in processing time. The uncertainty is adopted in the MILP model by using a chance-constraint programing approach. Since the problem is complex, Q-learning and Monte-Carlo simulation-assisted based genetic algorithm (GA) based memetic algorithm (MA) are proposed to solve the problem. The performance of MA is evaluated against the well-known non-dominated sorting GA-III (NSGA-III) and state-of-the-art algorithms for solving the problem. The experimental results show that MA surpasses its contest significantly in finding better Pareto-fronts.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.120737