Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times

Zhang, Jiahua; Li, Aiping; Liu, Xuemei

doi:10.1007/s40436-019-00256-3

Cited by 24 publications

(7 citation statements)

References 32 publications

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“…For example, an IT tool has been developed [2] to help engineers in controlling manufacturing resources, and in addition to increase the production rate using three heuristics: Largest candidate rule (LCR) method, Kilbridge and Wester Column (KWC) method and Ranked positional weight that combines the strategies of LCR and KWC methods. A hybrid genetic algorithm (HGA) has been proposed in [9] to solve a robust mixed model ALB problem type 2 with uncertain task times. A heuristic method has been used in this algorithm to seed the initial population, in addition, an adaptive local search and a discrete levy flight have been hybridized with this HGA to enhance its performance.…”

Section: Related Workmentioning

confidence: 99%

“…The genetic algorithm is an effective popular metaheuristic, it has been used to solve different assembly line balancing problems [9]. This metaheuristic starts with initial solutions (population), each solution (individual) is evaluated after the calculation of its fitness using a fitness function F. Best solution are selected to undergo operators (crossover and mutation).…”

Section: A Proposed Genetic Algorithmmentioning

confidence: 99%

“…The two famous problems are: Mixed Model Assembly Lines Balancing Problem type 1 (MiMALBP-1) and Mixed Model Assembly Lines Balancing Problem type 2 (MiMALBP-2). In MiMALBP-1 the objective is to minimize the number of workstations for a given cycle time, and in MiMALBP-2 the objective is to minimize the cycle time for a given number of workstations [9]. The MiMALBP is more complex than the SALBP because the line must be balanced and in addition, the sequence of models must be determined to maximize the line efficiency [10].…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

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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“…Pereira (2018a) considered an interval data version of the assembly line worker assignment and balancing problem (ALWABP), where exact and heuristic solution methods were proposed and analyzed. The robust optimization models for mixed-model assembly line balancing problems were also formulated and solved by Zhang et al (2019) and Samouei and Ashayeri (2019). Yilmaz (2020) constructed a robust optimization model to solve U-shaped assembly line worker assignment and balancing problem (UALWABP), which has been applied in a company producing water meters.…”

Section: Robust Optimization Of Line Balancing Problemmentioning

confidence: 99%

Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

Jin

2021

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PurposeDue to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability.Design/methodology/approachA generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated.FindingsTheory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy.Originality/valueFor the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional Whale Optimization Algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

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“…A mixed-integer programming (MIP) model solved ALBP. Zhang et al [27] considered uncertain task times in type-II mixed-model ALBP. A robust optimization model was formulated to hedge against uncertainty, and a hybrid genetic algorithm was proposed to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimization Model for Assembly Line Balancing Problem with Uncertain Cycle Time

Cao

Yuan

Liu

et al. 2020

Mathematical Problems in Engineering

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With the drastic change in the market, the assembly line is susceptible to some uncertainties. This study introduces the uncertain cycle time to the assembly line balancing problem (ALBP) and explores its impact. Firstly, we improve the traditional precedence graph to express the precedence, spatial, and incompatible constraints between assembly tasks, which makes ALBP more realistic. Secondly, we establish the assembly line balancing model under an uncertain cycle time, which is defined as an interval whose size can be adjusted according to the level of uncertainty. The objective of the model was to minimize the number of stations and the cycle time. Thirdly, we integrate the operator’s skill level into the model, and a multipopulation genetic algorithm is used to solve it. The method proposed in this study is verified by several test problems of different sizes. The results show that when the cycle time is uncertain, the proposed method can be used to obtain more reasonable results.

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Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times

Cited by 24 publications

References 32 publications

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

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

Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

An Optimization Model for Assembly Line Balancing Problem with Uncertain Cycle Time

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