Solving fuzzy assembly-line balancing problem with genetic algorithms

“…In the 90s Tsujimura et al [43] and Gen et al [44] initialized using fuzzy GA for this problem. With a typical GA provided that the tasks processing time was presented in fuzzy numbers, they solved SALBP-1.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

Alavidoost

Tarimoradi

Zarandi

2015

Applied Soft Computing

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“…Moreover, there is a lack in the literature for population heuristics for solving the multi-objective fuzzy SALBP-2. The works of Tsujimura et al (1995) and Gen et al (1996) were the first that dealt with fuzzy SALBP-1 via GAs. Brudaru and Valmar (2004) also proposed a rather time lengthy hybrid GA which combines a branch and bound method with a GA for solving fuzzy SALBP-1.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective fuzzy assembly line balancing using genetic algorithms

Zacharia

Nearchou

2010

J Intell Manuf

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This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 (SALBP-2) with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The jobs processing times are formulated by triangular fuzzy membership functions. The total fuzzy cost function is formulated as the weighted-sum of two bi-criteria fuzzy objectives: (a) Minimizing the fuzzy cycle time and the fuzzy smoothness index of the workload of the line. (b) Minimizing the fuzzy cycle time of the line and the fuzzy balance delay time of the workstations. A new multi-objective genetic algorithm is applied to solve the problem whose performance is studied and discussed over known test problems taken from the open literature.

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“…In this study, fuzzy literature possesses fundamental importance in balancing disassembly line. The fuzzy approach was posited for the first time in literature of assembly line balancing in references (Tsujimura et al, 1995) and (Gen et al, 1996) by solving a simple fuzzy assembly line balancing problem for a sample of a plain product. A line balancing problem of a hybrid model was examined with a fuzzy binary linear programming model in reference (Hop, 2006) using heuristic solution approach that dealt with fuzzy processing times.…”

Section: A Review On Subject Literaturementioning

confidence: 99%

The Balancing of Disassembly Line of Automobile Engine Using Genetic Algorithm (GA) in Fuzzy Environment

Seidi¹,

Saghari²

2016

Industrial Engineering and Management Systems

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Disassembly is one of the important activities in treating with the product at the End of Life time (EOL). Disassembly is defined as a systematic technique in dividing the products into its constituent elements, segments, sub-assemblies, and other groups. We concern with a Fuzzy Disassembly Line Balancing Problem (FDLBP) with multiple objectives in this article that it needs to allocation of disassembly tasks to the ordered group of disassembly Work Stations. Tasks-processing times are fuzzy numbers with triangular membership functions. Four objectives are acquired that include: (1) Minimization of number of disassembly work stations; (2) Minimization of sum of idle time periods from all work stations by ensuring from similar idle time at any work-station; (3) Maximization of preference in removal the hazardous parts at the shortest possible time; and (4) Maximization of preference in removal the high-demand parts before low-demand parts. This suggested model was initially solved by GAMS software and then using Genetic Algorithm (GA) in MATLAB software. This model has been utilized to balance automotive engine disassembly line in fuzzy environment. The fuzzy results derived from two software programs have been compared by ranking technique using mean and fuzzy dispersion with each other. The result of this comparison shows that genetic algorithm and solving it by MATLAB may be assumed as an efficient solution and effective algorithm to solve FDLBP in terms of quality of solution and determination of optimal sequence.

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Solving fuzzy assembly-line balancing problem with genetic algorithms

Cited by 74 publications

References 5 publications

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

Multi-objective fuzzy assembly line balancing using genetic algorithms

The Balancing of Disassembly Line of Automobile Engine Using Genetic Algorithm (GA) in Fuzzy Environment

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