A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations

Şahin, Murat; Kellegöz, Talip

doi:10.1016/j.cie.2019.04.056

Cited by 51 publications

(11 citation statements)

References 32 publications

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“…Meanwhile, to evaluate the result of each experiment, the relative percentage deviation (RPD) similar to Zhang et al [36] is introduced. It can be calculated by Equation (29).…”

Section: Computational Experiments and Resultsmentioning

confidence: 99%

“…Instead, meta-heuristic algorithms show strong strengths like flexibility, derivation-free mechanisms, and local optima avoidance [25]. And, a group of promising meta-heuristic algorithms is utilized to solve the ALBP-related problems, which include grey wolf algorithm [26], discrete artificial swarm algorithm [27], multi-objective cellular genetic algorithm [28], and hybrid particle swarm optimization algorithm [29]. Among the emerging meta-heuristic algorithms, whale optimization algorithm (WOA) shows advantages such as simple concept, less adjustment parameter, and easy implementation [30].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Lexicographical Whale Optimization Algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

et al. 2020

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In the traditional assembly line balancing, all the workstations are assumed available and hence the unavailability of any workstation brings about the stoppage of the whole line and the waste of the production capacity in the rest workstations. Considering the planning characteristic of preventive maintenance, this paper proposes a novel methodology of integrating the preventive maintenance scenarios into assembly line balancing problems to bypass the unavailable workstation. A lexicographic model is formulated to generate multiple task assignment plans that ensure primarily the high productivity under regular operation scenario and guarantee secondarily the production continuity under preventive maintenance scenarios. And, an improved whale optimization algorithm (IWOA) with three modifications is proposed to solve this problem. Specifically, a combined crossover operator enhances better combination in exploration; three best search agents promote the exploitation; partial regeneration avoids being trapped in local optima. More than five thousand experiments demonstrate that the joint of three modifications endows the IWOA significant superiority over six variants and other six well-known algorithms. Moreover, integrating preventive maintenance scenarios into the assembly line balancing problem increases the production efficiency by 1% at the cost of small production adjustment.

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“…Meanwhile, to evaluate the result of each experiment, the relative percentage deviation (RPD) similar to Zhang et al [36] is introduced. It can be calculated by Equation (29).…”

Section: Computational Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Lexicographical Whale Optimization Algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

et al. 2020

View full text Add to dashboard Cite

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“…They aimed to minimize the number of workers as a primary objective and the number of stations as secondary. Şahin et al [28] developed a mixedinteger linear programming model and particle swarm for solving resource investment and balancing multi-manned assembly lines. They aimed to minimize the total cost.…”

Section: Multi-manned Assembly Line Balancing Problemmentioning

confidence: 99%

Stochastic Local Search for Solving Chance-Constrained Multi-Manned U-shaped Assembly Line Balancing Problem with Time and Space Constraints

Zakarai¹,

Zaher²,

Ragaa³

2021

JUSST

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The assembly line balancing problems have great importance in research and industry fields. They allow minimizing the learning aspects and guaranteeing a fixed number of products per day. This paper introduces a new problem that combines the multi-manned concept with the U-shaped lines with time and space constraints under uncertainty. The processing time of the tasks is considered as random variables with known means and variances. Therefore, chance-constraints appear in the cycle time constraints. In addition, each task has an associated area, where the assigned tasks per station are restricted by a total area. The proposed algorithm for solving the problem is a stochastic local search algorithm. The parameter levels of the proposed algorithm are optimized by the Taguchi method to cover the small, medium, and large-sized problems. Well-known benchmark problems have been adapted to cover the new model. The computational results showed the importance of the new problem and the efficiency of the proposed algorithm.

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“…Constraints (17) and (18) ensure that the range of the finish time of task i is between its completion time and the cycle time. Constraints (19)- (23) are the internality constraints.…”

Section: E Mathematicalmentioning

confidence: 99%

“…Giglio et al [22] assumed workers were skilled and a MIP model was also built. Şahin and Kellegöz [23] additionally considered the resource constraint in the MALBP-C; a MIP model and a particle swarm optimization algorithm were both developed to solve it.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model and a Simulated Annealing Algorithm for Balancing Multi-manned Assembly Line Problem with Sequence-Dependent Setup Time

Yang

Cheng

2020

Mathematical Problems in Engineering

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Multi-manned assembly lines have been widely applied to the industrial production, especially for large-sized products such as cars, buses, and trucks, in which more than one operator in the same station simultaneously performs different tasks in parallel. This study deals with a multi-manned assembly line balancing problem by simultaneously considering the forward and backward sequence-dependent setup time (MALBPS). A mixed-integer programming is established to characterize the problem. Besides, a simulated annealing algorithm is also proposed to solve it. To validate the performance of the proposed approaches, a set of benchmark instances are tested and the lower bound of the proposed problem is also given. The results demonstrated that the proposed algorithm is quite effective to solve the problem.

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A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations

Cited by 51 publications

References 32 publications

An Improved Lexicographical Whale Optimization Algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

An Improved Lexicographical Whale Optimization Algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

Stochastic Local Search for Solving Chance-Constrained Multi-Manned U-shaped Assembly Line Balancing Problem with Time and Space Constraints

A Mathematical Model and a Simulated Annealing Algorithm for Balancing Multi-manned Assembly Line Problem with Sequence-Dependent Setup Time

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