Optimization of Cycle Time in an Assembly Line Balancing Problem

Raj, Abhishek; Mathew, Jeeno; Jose, Peter J.; Sivan, Gishnu

doi:10.1016/j.protcy.2016.08.231

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…A comparison has been carried out shows that the NSGA-2 in most cases is better than the MOPSO in terms of performance. Another iterative procedure has been proposed in [15] for solving mixed model assembly line with parallel workstations using a genetic algorithm. The main objective is to maximize the production rate of the line for a predefined number of operators.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, generated individuals by the genetic operators are evaluated. All these GA steps are repeated for a chosen number known as the number of generations [15,10]. In the proposed GA we used the Elitism technique [19] by keeping 25% best individuals from the total population in each generation to undergo GA operators and to guarantee that the best solutions remain until the final population.…”

Section: A Proposed Genetic Algorithmmentioning

confidence: 99%

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A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

Belkharroubi¹,

Yahyaoui²

2021

IJC

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In manufacturing systems, mixed model assembly lines are used to produce different products to deal with the problem of customers’ demands variety, and minimizing the cycle time in such assembly line is a critical problem. This paper addresses the mixed model assembly line balancing problem type 2 that consists in finding the optimal cycle time for a given number of workstations. A hybrid Greedy randomized adaptive search procedure-Genetic algorithm is proposed to find the optimal assignment of tasks among workstations that minimize the cycle. A Ranked Positional Weight heuristic is used in the construction phase of the proposed GRASP, and in the local search phase, a neighborhood search procedure is used to ameliorate the constructed solutions in the construction phase. The GRASP is executed many times in order to seed the initial population of the proposed genetic algorithm, and the results of the executions are compared with the final solutions obtained by the hybrid GRASP-GA. In order to test the proposed approaches, a numerical example is used.

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Section: Related Workmentioning

confidence: 99%

Section: A Proposed Genetic Algorithmmentioning

confidence: 99%

A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

Belkharroubi¹,

Yahyaoui²

2021

IJC

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“…Heuristic algorithms (e.g., [19,21,35,37] were used to solve line balancing problems. The objectives of various studies include maximization of profit [29], minimization of cycle time [28], work force minimization [3], and ergonomics [4,27].…”

Section: Challenges In Line Balancingmentioning

confidence: 99%

Pace making and worker reassignment for assembly line rebalancing

Budak

Chen

2020

SN Appl. Sci.

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The objective of this article is to develop a mathematical model that helps foremen rebalance assembly lines in real time. Assembly lines with a large number of workstations go through extensive line balancing to determine production rates. Once production begins, one or more workstations may not operate at predetermined production rates due to disturbances, and the assembly line requires rebalancing. Previous research largely focused on task reassignment for rebalancing. Effective and efficient tools for real-time line rebalancing are needed. This research focuses on rebalancing of unpaced and asynchronous assembly lines through pace making and worker reassignment. A mixed integer programming model is developed and its optimal solution determines which workstations pace up (increase production rates) and the amount of increase, which workstations pace down (decrease production rates) and the amount of decrease, and whether workers from certain workstations are reassigned to work at other workstations to decrease the production rates of the former and increase the production rates of the latter. Foremen may use these recommendations to rebalance assembly lines by adjusting production rates and reassigning workers. Numerical results show that the solution time of the model is less than one second, which enables real-time line rebalancing for large assembly lines.

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“…Kraljic's matrix-based model is implemented which reduced the manufacturing CT of feeder hopper from 43 days to 21 days. Similarly, various research articles discussed the CT related problems and suggested possible solutions to efficiently tackle it [19]- [21].…”

Section: Literature Reviewmentioning

confidence: 99%