Dynamic cellular manufacturing systems design—a comprehensive model

Saxena, Lokesh Kumar; Jain, P.K.

doi:10.1007/s00170-010-2842-9

Cited by 59 publications

(37 citation statements)

References 75 publications

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“…Many authors proposed models, which consider dynamic cell reconfiguration over multi periods [1][2][3][4][5][6].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

Aljuneidi¹,

Bulgak²

2016

JMSS

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This article presents a comprehensive mathematical model for the design and analysis of Dynamic Cellular Manufacturing Systems (DCMS). The proposed DCMS model considers several manufacturing attributes such as multi period production planning, dynamic system reconfiguration, duplicate machines, machine capacity, the available time for workers, worker assignments, and machine procurement. The objective is to minimize total costs; consisting of holding cost, outsourcing cost, inter-cell material handling cost, maintenance and overhead cost, machine relocation cost. While a study of published articles in the area of Cellular Manufacturing Systems (CMS) shows that workforce management issues have not sufficiently been addressed in the literature, the model presented also incorporates CMS workforce management issues such as salaries, hiring and firing costs of workers in addition to the manufacturing attributes. In-depth discussions on the results for two numerical examples are presented to illustrate applications of the proposed model. The model developed aims to raise the envelope by expanding and improving several CMS models previously presented in the literature.

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“…Many authors proposed models, which consider dynamic cell reconfiguration over multi periods [1][2][3][4][5][6].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

Aljuneidi¹,

Bulgak²

2016

JMSS

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“…Mahdavi et al (2011) presented a fuzzy goal programming based approach for solving a multi-objective mathematical model of CF and PP in a dynamic virtual CMS considering worker flexibility. Saxena and Jain (2011) presented a comprehensive mixed integer programming model including cutting tooling requirements that integrates CF and PP. Rafiee et al (2011) presented a comprehensive mathematical model for integrated CF and inventory lot sizing problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A three-phase heuristic approach to solve an integrated cell formation and production planning problem

Kheirkhah¹,

Ghajari²

2018

10.5267/j.uscm

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In today's dynamic business environment, the product mix and demand may vary from period to period. Therefore, the same configuration of cellular systems will not be optimal in different periods. In this paper, a new mixed integer non-linear mathematical programming model is presented to design dynamic cellular manufacturing systems and combines several design features including multi-period production planning, alternate routings, system reconfiguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material flow between machines. The required capacity for parts production is modeled based on flow shop perspective and the aim of mixed integer model is to find optimal independent cells, the quantity of machine types in each cell, and production quantity of the parts during each period of the time horizon. Since this problem belongs to NPhard class, a three-phase approach is developed to solve the model for practical purposes. Phase 1 finds a feasible solution, phase 2 finds the neighbor solutions, and phase 3 improves a feasible solution. To analyze the computational efficiency, eight test problems with different sizes are considered and the optimal and near-optimal solutions are compared. The efficiency of the algorithm in terms of the objective function values and computational times is shown by the obtained results.Growing Science Ltd. All rights reserved. 8

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“…A considerable amount of researchers on the field of CMS previously have presumed the machinery 100% reliable, while more recent works have embedded the reliability in a rather complicated and undesirable way in the CMS problems (Das et al, 2007a;Das et al, 2007b;Jabal Ameli et al, 2007;Jabal Ameli & Arkat, 2008;Saxena & Jain, 2011). Das et al have treated reliability of the manufacturing system as being serial so they have deemed breakdown of a machine equal to that of the whole manufacturing system (Das et al, 2007a;Das et al, 2007b).…”

Section: -Among Parameters Which Influence the Cms Allocation Of Mamentioning

confidence: 99%

“…Das et al have treated reliability of the manufacturing system as being serial so they have deemed breakdown of a machine equal to that of the whole manufacturing system (Das et al, 2007a;Das et al, 2007b). Approximately 75% of the manufacturing systems produce their products as a large set of parts via batch processing (Akturk and Turkcan, 2000 ;Saxena & Jain, 2011). In this case, the whole system does not necessarily stop with breakdown of a machine, because the system will never stop if repairing the failed machine lasts for some time shorter than the current batch operation.…”

Section: -Among Parameters Which Influence the Cms Allocation Of Mamentioning

confidence: 99%

“…Complexity of the manufacturing systems in comparison with other common engineering systems will make it impossible to be dealt with them like electronic systems. This will practically limit application of the paper of K. Das et alHowever, other authors have just considered the costs of system breakdown in a way that can be improved (Jabal Ameli et al, 2007;Saxena & Jain, 2011).…”

Section: -Among Parameters Which Influence the Cms Allocation Of Mamentioning

confidence: 99%

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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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Dynamic cellular manufacturing systems design—a comprehensive model

Cited by 59 publications

References 75 publications

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

A three-phase heuristic approach to solve an integrated cell formation and production planning problem

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

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