An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis

Ghezavati, Vahidreza; Saidi‐Mehrabad, Mohammad

doi:10.1016/j.eswa.2010.07.012

Cited by 50 publications

(24 citation statements)

References 22 publications

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“…Calculate an approximate 100(1 )% con dence upper bound on the true objective value of solution (ẑ ẑ z ;n n n ), that is, w(ẑ ẑ z ;n n n ) by: w U = b w S 0(ẑ ẑ z ;n n n ) + 1 (1 ) S 0; (24) where ( Step 4. Obtain a statistically valid bound with the con dence of at least 100(1 2 )% on the true optimality gap of solution (ẑ ẑ z ;n n n ) by: g(ẑ ẑ z ;n n n ) = w U w L :…”

Section: Mathematical Model Linearizationmentioning

confidence: 99%

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A two-stage stochastic model for designing cellular manufacturing systems with simultaneous multiple processing routes and subcontracting

2017

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Abstract. In recent decades, many researchers have studied the cellular manufacturing system with consideration of various issues such as scheduling, production planning, layout, reliability, etc. However, limited research papers have investigated this problem in an uncertain environment. The present paper addresses a stochastic problem in cellular manufacturing systems considering simultaneous multiple routings and subcontracting. In the developed problem, each part can be simultaneously produced in multiple processing routes. It is also assumed that the unsatis ed part demands as a result of limited machine capacity or high manufacturing cost could be outsourced. A two-stage stochastic programming approach is employed to take the uncertainty into consideration and to formulate the problem. The objective function is to minimize the summation of production, subcontracting, material handling, and machine idleness costs. A sample average approximation method is applied as a solution method. Also, for further illustration of the problem, a numerical example is solved and sensitivity analyses are conducted. Finally, through some numerical examples extracted from related literature, the advantages of constructing a stochastic optimization model for the problem are demonstrated.

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Section: Mathematical Model Linearizationmentioning

confidence: 99%

“…An "-constraint solution method was used to solve the problem. Ghezavati and Saidi-Mehrabad [24] applied a queuing theory approach to design a CMS with exponentially distributed service and arrival times. It was assumed that each machine worked as a server and each part was a customer that should be served by machines.…”

Section: Introductionmentioning

confidence: 99%

A two-stage stochastic model for designing cellular manufacturing systems with simultaneous multiple processing routes and subcontracting

2017

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“…Their paper deals with the self-organizing map method, an unsupervised learning algorithm in artificial intelligence which has been used as a visually decipherable clustering tool of machine-part cell formation. Ghezavati and Saidi-Mehrabad [27] proposed an efficient hybrid self-learning method for stochastic cellular manufacturing problems. It addresses a new version of stochastic mixed-integer model to design cellular manufacturing systems under random parameters, described by continuous distributions.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm

Arunagiri¹,

Uthayakumar

Gopalakrishnan³

et al. 2017

Sustainability

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Abstract:The efficiency and sustainability of a cellular manufacturing system (CMS) in batch type manufacturing is highly valued. This is done using a systematic method of equipment into machine cells, and components into part families, based on the suitable similar criteria. The present work discusses the cell formation problem, with the objective of minimizing the cumulative cell load variation and cumulative intercellular moves. The quantity of parts, operation sequences, processing time, capacity of machines, and workload of machineries were considered as parameters. For the grouping of equipment, the modified artificial bee colony (MABC) algorithm is considered. The computational procedure of this approach is explained by using up to 40 machines and 100 part types. The result obtained from MABC is compared with the findings acquired from the genetic algorithm (GA) and ant colony system (ACS) in the literature.

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“…Cao et al [21] GA-simplex linear programming Ghezavati and Saidi-Mehrabad [22] GA-simulated annealing…”

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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An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis

Cited by 50 publications

References 22 publications

A two-stage stochastic model for designing cellular manufacturing systems with simultaneous multiple processing routes and subcontracting

A two-stage stochastic model for designing cellular manufacturing systems with simultaneous multiple processing routes and subcontracting

Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm

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

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