A Hybrid Genetic Algorithm for Machine Part Grouping

Tariq, Adnan; Hussain, Iftikhar; Ghafoor, Abdul

doi:10.1109/icet.2006.335912

Cited by 12 publications

(19 citation statements)

References 10 publications

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“…Throughout the remainder of this paper, we focus on a particular objective criterion known as the grouping efficacy index (GEI: Kumar & Chandrasekharan, 1990). The GEI is arguably one of the most popular indices used in the cell formation literature (Brusco, 2015;Brusco & Steinley, 2007;Bychkov, Batsyn, & Pardalos, 2014;James, Brown, & Keeling, 2007;Li, Baki, & Aneja, 2010;Noktehdan, Karimi, & Kashan, 2010;Paydar & Saidi-Mehrabad, 2013;Tariq, Hussain, & Ghafoor, 2009;Tunnukij & Hicks, 2009;Wu, Chang, & Chung, 2008, Wu, Chung, & Chang, 2010.…”

Section: Introductionmentioning

confidence: 99%

An iterated local search heuristic for cell formation

Brusco

2015

Computers & Industrial Engineering

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Section: Introductionmentioning

confidence: 99%

An iterated local search heuristic for cell formation

Brusco

2015

Computers & Industrial Engineering

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“…The uniqueness of this LSH is that unlike Tariq et al [20] it only changes the cell numbers of those parts/machines which are ill placed that is, having more intercellular moves than the number of operations inside the cell. Such type of local improvement can definitely save time and computational effort.…”

Section: Local Search Heuristic (Lsh)mentioning

confidence: 99%

“…Chee and Ponnambalam [17] GA-particle swarm optimization Defersha and Chen [18] GA-linear programming Tunnukij and Hicks [19] GGA-greedy heuristic Tariq et al [20] G A -l o c a l s e a r c h h e u r i s t i c…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Javaid

Tariq

Hussain

2014

Advances in Mechanical Engineering

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Though there are a number of benefits associated with cellular manufacturing systems, its implementation (identification of part families and corresponding machine groups) for real life problems is still a challenging task. To handle the complexity of optimizing multiple objectives and larger size of the problem, most of the researchers in the past two decades or so have focused on developing genetic algorithm (GA) based techniques. Recently this trend has shifted from standard GA to hybrid GA (HGA) based approaches in the quest for greater effectiveness as far as convergence on to the optimum solution is concerned. In order to prove the point, that HGAs possess better convergence abilities than standard GAs, a methodology, initially based on standard GA and later on hybridized with a local search heuristic (LSH), has been developed during this research. Computational experience shows that HGA maintains its accuracy level with increase in problem size, whereas standard GA looses its effectiveness as the problem size grows.

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“…It operates on two chromosomes at a time and generates offspring by combining both chromosomes' features [5]. There are several methods to crossover, partial-mapped crossover (PMX) is adopted in the proposed GA.…”

Section: Crossovermentioning

confidence: 99%

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province

Cao

Yang

Guo

2011

Computer and Computing Technologies in Agriculture IV

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Abstract. As we know rice is the main food in China. In recent years, the rapid development of agricultural decision support system provides new management methods for rice production. GA has been regarded as an effective analytic tool and stochastic search technique to solve large and complicated problems. However, traditional GA-based decision support system does not always result in good solutions and search efficiency due to random method. This study presents a heuristic genetic algorithm-based decision support system to search the optimal solution. Heuristic method provides a robust and efficient approach for solving complex real-world problems. This hybrid algorithm incorporates concepts from GA and heuristic method and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by heuristic mechanism. The result of experiments reveals the proposed algorithm is more computationally effective and efficient than GA alone in terms of solution quality.

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A Hybrid Genetic Algorithm for Machine Part Grouping

Cited by 12 publications

References 10 publications

An iterated local search heuristic for cell formation

An iterated local search heuristic for cell formation

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province

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