An optimal design approach for a cellular manufacturing system

Giri, R.; Srinivas, J.; Mouli, K. V. V. Chandra

doi:10.1243/09544054jem813sc

Cited by 9 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An optimal machine sequence, maximizing the overall flow of elements between the machines, is found out. 12 A Pareto-optimality-based multi-objective Tabu search (MOTS) algorithm is offered for the problem of machine-part grouping to incorporate objectives such as minimization of total cost, which consists of intracellular and intercellular transportation cost and machine investment cost, minimization of intracell loading unbalance and minimization of the intercell loading unbalance. 13 A new methodology for CF consuming a syntactic recognition approach is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of cell formation and job sequencing to minimize energy consumption with minimum make-span

Iqbal

Aziz

Jahanzaib

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Cell formation is the fundamental step while designing a cellular manufacturing system. Integration of job sequencing with cell formation can attain lower make-spans. The traditional cell formation and scheduling problems consider performance indicators such as productivity, time and flexibility in cellular manufacturing system; however, energy consumption has not been given due attention. Therefore, this research addressed the minimization of total energy consumption by implementing an energy-efficient schedule at the cell formation stage of cellular manufacturing system. For this purpose, a two-phase approach is proposed; in phase I, formation of independent cells is being carried out by considering energy-efficient routings and genetic algorithm is used for improving search performance. In phase II, a formulation is being developed to compute the total energy of the system based on optimal job sequence with respect to minimum idle running of the machines in each independent cell. For the proposed approach, a code is being developed in MATLAB software. Different sample problems have been evaluated. The results showed that the proposed approach is effective in generating independent cells and sequences with minimum energy consumption and make-span.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Integration of cell formation and job sequencing to minimize energy consumption with minimum make-span

Iqbal

Aziz

Jahanzaib

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…The result shown ACS outperformed existing GA method in several cases. Giri et al (2007) reported a TSP based heuristic embedded with ACO to form optimal part/machine clusters based on the overall machine sequence which leads to configurations with a minimum number of movements among the cells. The ACO is used here to obtain optimum machine sequence for maximum part volume flow.…”

Section: Aco In Cell Formationmentioning

confidence: 99%

Meta-heuristics in cellular manufacturing: A state-of-the-art review

Ghosh¹,

Sengupta²,

Chattopadhyay³

et al. 2011

IJIEC

View full text Add to dashboard Cite

Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various metaheuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area.

show abstract

“…Giri et al (2007) introduced the machine sequence assumption to the CFP, then developed a heuristic algorithm which finds, at first step, an optimal machine sequence maximizing the overall flow of parts between the machines, secondly, an optimal number of cells by regarding the total inter-cell movements using the obtained machine sequence. They showed that the grouping of parts and machines based on the overall machine sequence leads to assignments with a minimum number of movements between the cells.…”

Section: Introductionmentioning

confidence: 99%