An effective p-median model considering production factors in machine cell/part family formation

Won, Youkyung; Currie, Kenneth R.

doi:10.1016/s0278-6125(06)80033-6

Cited by 30 publications

(12 citation statements)

References 24 publications

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“…Ashayeri et al (2005) applied an improved version of Teitz and Bart's vertex substitution heuristic for solving facility layout and location problems that were formulated as PMP. Won and Currie (2006) developed a new version of PMP by calculating similarity coefficients in a non-binary MCIM through clustering process while operation sequences and production volumes were taken into account. Goldengorin et al (2012) proposed a compact representation of PMP by using Mixed-Boolean pseudo-Boolean formulation for minimizing dissimilarities between center and machines within a cell that caused reducing computation time.…”

Section: Partitioning Methodsmentioning

confidence: 99%

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Delgoshaei¹,

Delgoshaei²,

Ali³

2019

10.5267/j.ijiec

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This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS).

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Section: Partitioning Methodsmentioning

confidence: 99%

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Delgoshaei¹,

Delgoshaei²,

Ali³

2019

10.5267/j.ijiec

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“…Assignment of products to cells. model of [25] was chosen because it considers a number of production factors and a complexity level relatively similar to the proposed methodology, and it can be solved in Lingo© in a reasonable computation time. In Table 10, it is seen that the proposed methodology takes more time to get the global optimal solution as well as a greater number of iterations compared to the model of [25].…”

Section: 5mentioning

confidence: 99%

A Methodology to Design and Balance Multiple Cell Manufacturing Systems

Valdivia¹,

Palominos²

2020

Mass Production Processes

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Manufacturing cell formation and its balance in just-in-time (JIT) type production environments have usually been studied separately in the literature. This practice is unrealistic since both problems interact and affect each other when the cells are operating. This chapter proposes a methodology to design multiple manufacturing cells and simultaneously balance their workload. The cells considered are U-shaped and process mixed models of product families. A nonlinear integer programming mathematical model is proposed, which integrates cell formation and their balancing, considering various production factors. For illustration, the method is applied to the redesign of a rack manufacturing process.

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“…Modularity, integrability and diagnosability allow rapid reconfiguration, but they do not guarantee modifications in production capacity and functionality. Customization, an essential RMS characteristic, is based upon design for a part family or a product family, a concept already mentioned by other researchers [18]. The six key RMS characteristics reduce the time and effort of reconfiguration, and consequently enhance system responsiveness.…”

Section: Characteristics and Principles Of Reconfigurationmentioning

confidence: 99%