A cellular manufacturing system based on new similarity coefficient which considers alternative routes during machine failure

Jeon, Geonwook; Leep, Herman R.; Parsaei, Hamid R.

doi:10.1016/s0360-8352(97)00148-4

Cited by 25 publications

(14 citation statements)

References 20 publications

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“…Their results revealed that preventive maintenance will improve the reliability in case of increasing the failure rate. Jeon et al (1998) proposed a CMS model with regards to machine failure and alternative routings. Their study had a predefined number of machine's breakdown.…”

Section: Reliability In Cmsmentioning

confidence: 99%

Literature Review on Machine Reliability in Cellular Manufacturing System

Houshyar¹,

Leman²,

Moghadam³

et al. 2014

American Journal of Applied Sciences

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Nowadays, in the global markets companies have to produce highquality product, low cost product in order to be alived in competetive markets. Group technology helps manufacturer to raech this aim. One of the most proper applications of group technology is called Cellular Manufacturing System (CMS). CMS is able to implement in the manufacturing system for overcoming the waeakness of traditional manufacturing system such as Job shop, Flow shop. One of the main challenging issues in CMS is named machine reliability and breakdown. Although, production facilities and machines are improving and becoming more sophisticated, they are not free from deterioration due to the aging which leads to shifting from in-control to out-of-control state for the machines and also breakdown of machines in the planning horizon. This study provides a comprehensive review on literature of machine reliability in cellular manufacturing system. This study made attempts to gather all the researches which have been done on machine reliability in CMS. In fact this issue was considered by the author since, moving from in-control state to out-of-control state and also machine breakdown have effective impacts on the system; therefore, it is essential for manufacturers to consider this subject in their study for preventing the huge wastage of time and cost in the system which will be occurred as results of this phenomena.

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Section: Reliability In Cmsmentioning

confidence: 99%

Literature Review on Machine Reliability in Cellular Manufacturing System

Houshyar¹,

Leman²,

Moghadam³

et al. 2014

American Journal of Applied Sciences

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“…The overall manufacturing system availability was evaluated by considering the probabilities of subsets of machines in working condition in each cell. Jeon, Leep, and Parsei [20] proposed a CMS-design model to consider alternative routes during the predefined number of breakdowns for each of the machines. The authors attempted to reduce waiting cost, early/late finish penalty cost, and sum of inventory holding cost by selecting alternative routes to handle the 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 main objective of Phase I is to develop a new similarity coefficient which considers the number of alternative routes available during machine failure (Jeon et al [14]) and identify part families by using a genetic algorithm.…”

Section: Similarity and Dissimilarity Coefficients For Determining Pamentioning

confidence: 99%

“…The constraints in Eq. (14) ensure that X (mo) ic and Y moi are sets of integer variables and that N mc is a set of integer variables greater than or equal to zero. Also, waiting time, early/late finish times, and completion time are greater than or equal to zero.…”

Section: Mathematical Programming Modelsmentioning

confidence: 99%