A Weighted Multiobjective Optimization Method for Mixed-Model Assembly Line Problem

Şeker, Şükran; Özgürler, Mesut; Tanyaş, Mehmet

doi:10.1155/2013/531056

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…They developed a mathematical model for this problem and a decomposition scheme for this problem. Sekar [10] developed multi-objective mixed-integer programming (MOMIP) model to minimizing the work overload and station-to-station product flows in the mixed-model assembly line balancing problem. Akpinar and Baykasoglu [11] [12] considered the mixed-model assembly line balancing problem with parallel workstations, zoning constraints and sequence dependent setup times between tasks.…”

Section: Mathematical Modelsmentioning

confidence: 99%

Design and Comparison of Genetic Algorithms for Mixed-Model Assembly Line Balancing Problem with Original Task Times of Models

Sivasankaran¹,

Shahabudeen²

2016

AJIBM

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Assembly line balancing is a key for organizational productivity in terms of reduced number of workstations for a given production volume per shift. Mixed-model assembly line balancing is a reality in many organizations. The mixed-model assembly line balancing problem comes under combinatorial category. So, in this paper, an attempt has been made to develop three genetic algorithms for the mixed-model assembly line balancing problem such that the combined balancing efficiency is maximized, where the combined balancing efficiency is the average of the balancing efficiencies of the individual models. At the end, these three algorithms and another algorithm in literature are compared in terms of balancing efficiency using a randomly generated set of problems through a complete factorial experiment, in which "Algorithm", "Problem Size" and "Cycle Time" are used as factors with two replications under each of the experimental combinations to draw inferences and to identify the best of the four algorithms. Then, through another set of randomly generated small and medium size data, the results of the best algorithm are compared with the optimal results obtained using a mathematical model. It is found that best algorithm gives the optimal solution for all the problems in the second set of data, except for one problem which cannot be solved using the model. This observation supports the fact that the best algorithm identified in this paper gives superior results. KeywordsAssembly Line Balancing, Cycle Time, Genetic Algorithm, Crossover Operation, Mixed-Model, Mathematical Model P. Sivasankaran, P. M. Shahabudeen 675

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Section: Mathematical Modelsmentioning

confidence: 99%

Design and Comparison of Genetic Algorithms for Mixed-Model Assembly Line Balancing Problem with Original Task Times of Models

Sivasankaran¹,

Shahabudeen²

2016

AJIBM

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“…Their linear programming module updated design variables in their problems. Seker et al [6] conducted research under the philosophy of using the weighted method for a mixed-model assembly line problem. Their study was based on the results obtained by a specialized software [7] indicating the proper balancing among the optimization objectives and the corresponded weights.…”

Section: Introductionmentioning

confidence: 99%

Balancing multiple criteria in formulation of weighted, single-objective genetic algorithm optimization for CNC machining problems

Krimpenis

Fountas

2016

Adv. Manuf.

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This paper presents results obtained from the implementation of a genetic algorithm (GA) to a simplified multi-objective machining optimization problem. The major goal is to examine the effect of crucial machining parameters imparted to computer numerical control machining operations when properly balanced conflicting criteria referring to part quality and process productivity are treated as a single optimization objective. Thus the different combinations of weight coefficient values were examined in terms of their significance to the problem's response. Under this concept, a genetic algorithm was applied to optimize the process parameters exist in typical; commercially available CAM systems with significantly low computation cost. The algorithm handles the simplified linear weighted criteria expression as its objective function. It was found that optimization results vary noticeably under the influence of different weighing coefficients. Thus, the obtained optima differentiate, since balancing values strongly affect optimization objective functions.

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“Mathematical formulations for the optimal sequencing and lot sizing in multiproduct synchronous assembly lines”

Camussi

Cerdá

Cafaro

2021

Computers & Industrial Engineering

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A Weighted Multiobjective Optimization Method for Mixed-Model Assembly Line Problem

Cited by 6 publications

References 36 publications

Design and Comparison of Genetic Algorithms for Mixed-Model Assembly Line Balancing Problem with Original Task Times of Models

Design and Comparison of Genetic Algorithms for Mixed-Model Assembly Line Balancing Problem with Original Task Times of Models

Balancing multiple criteria in formulation of weighted, single-objective genetic algorithm optimization for CNC machining problems

“Mathematical formulations for the optimal sequencing and lot sizing in multiproduct synchronous assembly lines”

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