Balancing Stochastic Mixed-Model Two-Sided Disassembly Line Using Multiobjective Genetic Flatworm Algorithm

Liang, Junyong; Guo, Sen; Xu, Wenxiang

doi:10.1109/access.2021.3117070

Cited by 4 publications

(2 citation statements)

References 50 publications

(73 reference statements)

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“…Equation (22) indicates that the actual number of units processed in station 1 equals its own theoretical number of units processed. Equations (23) and ( 24) compute the number of units processed in the other stations in period 0 (when there is no WIP inventory), and in subsequent periods (when there may exist some WIP inventory), respectively. Q(j, `) = P (j, `)…”

Section: Constraintsmentioning

confidence: 99%

“…In this way, we would be automating the fine-tuning process and avoiding the timeconsuming activity of systematically testing values. Recently, in the ALB literature, innovative optimization algorithms are being adapted and/or improved to solve specific assembly line balancing problems; for example: lexicographical whale optimization algorithm for the type-II ALBP considering preventive maintenance [24]; water-flow like algorithm for solving U-shaped ALBPs [25]; and multi-objective genetic flatworm algorithm for solving stochastic, mixed-model, two-sided disassembly lines [23]; to mention a few.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

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A Demand-Driven Model for Reallocating Workers in Assembly Lines

Perez-Wheelock

Yenradee

et al. 2022

IEEE Access

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This paper introduces the demand-driven assembly line rebalancing problem (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously readjusted along a planning horizon in order to satisfy as much as possible a given demand forecast; thus, dealing not with a one-time rebalance, but with a multi-period rebalance, fact that exponentially increases the complexity and combinatorial nature of the problem. Adapting or regulating the production output of the AL to some demand forecast or production plan is a relatively new idea in the assembly line balancing (ALB) / rebalancing (ALR) literature; and we address this problem by reallocating workers to stations, taking into consideration their learning and forgetting (L&F) curves. Our proposed model was solved by implementing a genetic algorithm (GA) in 162 cases (three problem instances under 54 scenarios each), obtaining useful insights about the dynamic of worker reallocation under different scenarios: optimistic, most-likely, pessimistic L&F coefficients; experienced and inexperienced workers; and different demand scenarios.

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Section: Constraintsmentioning

confidence: 99%