A simulated annealing algorithm for manufacturing cell formation problems

Wu, Tai‐Hsi; Chang, Chin-Chih; Chung, Shu-Hsing

doi:10.1016/j.eswa.2007.01.012

Cited by 128 publications

(43 citation statements)

References 34 publications

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“…By considering the (13) and (14), the definition of P p (i) is shown in (17), where f it mean (i) is the fit mean value at the i-th iteration. P p (i) is the third fuzzy input.…”

Section: The Evolutionary Fuzzy Algorithmsmentioning

confidence: 99%

“…(12) best(t) = min j∈1,...,n max j∈1,...,n f it j (t) (13) worst(t) = max j∈1,...,n min j∈1,...,n f it j (t) (14) In order to find a good compromise between exploration and exploitation, the number of agents with a lapse of time has to be reduced. To improve the performance of GSA by controlling exploration and exploitation, only the K best agents will attract the others [22].…”

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

“…The gravitational and inertial masses are updated by the Equations (10)- (12), where f it i (t) represents the fitness value of the agent i at time t, and worst(t) and best(t) are defined as in (13) and (14) for minimization (maximization) problems. (12) best(t) = min j∈1,...,n max j∈1,...,n f it j (t) (13) worst(t) = max j∈1,...,n min j∈1,...,n f it j (t) (14) In order to find a good compromise between exploration and exploitation, the number of agents with a lapse of time has to be reduced.…”

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

“…Another evolutionary algorithm is the Particle Swarm Optimization (PSO), which depends on the simulation of social behavior [10]. This search method has been applied in various optimization problems [11][12][13][14][15]. An algorithm that operates through the computational steps of a standard evolutionary algorithm is the Differential Evolution (DE).…”

Section: Introductionmentioning

confidence: 99%

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Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems

2017

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Abstract:The choice of the best optimization algorithm is a hard issue, and it sometime depends on specific problem. The Gravitational Search Algorithm (GSA) is a search algorithm based on the law of gravity, which states that each particle attracts every other particle with a force called gravitational force. Some revised versions of GSA have been proposed by using intelligent techniques. This work proposes some GSA versions based on fuzzy techniques powered by evolutionary methods, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), to improve GSA. The designed algorithms tune a suitable parameter of GSA through a fuzzy controller whose membership functions are optimized by GA, PSO and DE. The results show that Fuzzy Gravitational Search Algorithm (FGSA) optimized by DE is optimal for unimodal functions, whereas FGSA optimized through GA is good for multimodal functions.

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“…By considering the (13) and (14), the definition of P p (i) is shown in (17), where f it mean (i) is the fit mean value at the i-th iteration. P p (i) is the third fuzzy input.…”

Section: The Evolutionary Fuzzy Algorithmsmentioning

confidence: 99%

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems

2017

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“…1996) presents a complete review of production oriented manufacturing cell formation techniques. They proposed a classification to group the resolution methods into different categories as: Methods classified in the artificial intelligence are group into Neural Network (Mahdavi and al., 2007) Simulated Annealing method (Wu and al. 2008), Tabu Search (Logendran and al.…”

Section: Introductionmentioning

confidence: 99%

An adapted Genetic Algorithm to solve Generalized Cell Formation

Vin

Francq

Delchambre

2009

IFAC Proceedings Volumes

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This paper addresses the cell formation problem with alternative process plans and machine capacity constraints. Given alternative process plans, machine capacities and quantities of parts to produce, the problem consists in defining preferential process and routing for each part (grouping of operations into machines) optimizing machines grouping into manufacturing cells. The problem can be decomposed in two distinct sub-problems: operations grouping on resources, yielding flows between the machines, and grouping of these latter into independent cells. The objective of the proposed method is to optimize both groupings (operations on machines and machines into cell) minimizing of the inter-cellular moves. To solve simultaneously both grouping interdependent problems, we propose a modified grouping genetic algorithm (SIGGA). In our adapted grouping genetic algorithm, each chromosome is composed of two parts, one part for each problem. According to different application rates, the genetic operators are applied on the first, on the second problem, or on both problems. Finally, the population chromosomes simultaneously evolve in both problems.

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