The effect of reliability consideration on the application of quality index

Seifoddini, Hamid; Djassemi, Manoocher

doi:10.1016/s0360-8352(00)00072-3

Cited by 33 publications

(19 citation statements)

References 10 publications

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“…Diallo et al (2001) carried out a reliability analysis of the individual machines and manufacturing system states in the presence of unreliable machines, and proposed an approach to design manufacturing cells which can change process plans to handle machine breakdowns. In another study by Seifoddini and Djassemi (2001) it was shown that the impact of delays, due to machine breakdowns, is not limited exclusively to the production rate but affects the scheduling and productivity of the entire manufacturing operations as well.…”

Section: Introductionmentioning

confidence: 99%

Reliability and maintainability analysis of strudel production line with experimental data – A case study

Tsarouhas

Varzakas

Arvanitoyannis

2009

Journal of Food Engineering

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Section: Introductionmentioning

confidence: 99%

Reliability and maintainability analysis of strudel production line with experimental data – A case study

Tsarouhas

Varzakas

Arvanitoyannis

2009

Journal of Food Engineering

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“…Although advantages of the cellular manufacturing systems are properly addressed by either users or researchers, some others have criticized about drawbacks of these systems (Flynn & Jacobs, 1986;Morris & Tersine, 1990;Boughton & Arokiam, 2000;Agarwal & Sarkis, 1998;Das et al, 2007b). Their results can be summarized as below: (Das et al, 2007b), (Seifoddini and Djassemi, 2001;Das et al, 2007b).…”

Section: Literature Reviewmentioning

confidence: 99%

A two-layer genetic algorithm for the design of reliable cellular manufacturing systems

Rezazadeh¹,

Khiali-Miab²

2017

10.5267/j.ijiec

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This study presents a new mathematical model for the design of reliable cellular manufacturing systems, which leads to reduced manufacturing costs, improved product quality and improved total reliability of the manufacturing system. This model is expected to provide a more noticeable improvement in time and solution quality in comparison with other existing models. Each part to be manufactured may select each of the predefined manufacturing routes, such that the total reliability of the system is increased. On the other hand, the model adopts to categorize the machines to determine the manufacturing cells (cell formation) and reduce the transportation costs. Thereby, both criteria of system reliability and manufacturing costs will be simultaneously improved. Due to the complexity of cell formation problems, a two-layer genetic algorithm is applied on the problem in order to achieve near optimal solutions. Furthermore, the performance of the proposed algorithm is shown for solving some computational experiments. Finally, the results of a practical study for designing a cellular manufacturing system as a case study in Iranian Diesel Engine Manufacturing Co., Tabriz, Iran are present.

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“…Probability distribution of machining center failures was studied in Yazhau et al [21]. Seifoddini and Djassemi [23] mentioned that machine reliability was an influential factor in the performance of a cellular manufacturing system. Diallo et al [24] proposed an approach to design manufacturing cells, which can change process, plans to handle machine breakdowns.…”

Section: Machine Reliability In Cellular Manufacturing Systemsmentioning

confidence: 99%

A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis

Aghajani

Didehbani

Zadahmad

et al. 2014

Int J Adv Manuf Technol

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This paper presents a dynamic multi-objective mixed integer mathematical model for cell formation problem with probabilistic demand and machine reliability analysis, where the total system costs, machine underutilization cost, and maximum system failure rate over the planning time periods are to be minimized simultaneously. The total system cost objective function calculates machine operating, internal part production, intercellular material handling, and subcontracting costs. Since in this type of problems, objectives are in conflict with each other, so finding an ideal solution (a solution that satisfies all objectives simultaneously) is not possible. Therefore, this study uses the augmented ε-constraint method (to solve small size problems) and a nondominated sorting genetic algorithm (NSGAII) (to solve large size problems) to find the Pareto optimal frontier that decision makers can select her/his preferred solution. Numerical examples will be solved to demonstrate the efficiency of the proposed algorithm.

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The effect of reliability consideration on the application of quality index

Cited by 33 publications

References 10 publications

Reliability and maintainability analysis of strudel production line with experimental data – A case study

Reliability and maintainability analysis of strudel production line with experimental data – A case study

A two-layer genetic algorithm for the design of reliable cellular manufacturing systems

A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis

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