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

Ghosh, Tamal; Sengupta, Sourav; Chattopadhyay, Manojit; Dan, Pranab K.

doi:10.5267/j.ijiec.2010.04.005

Cited by 59 publications

(38 citation statements)

References 118 publications

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“…Equation (7) is the constraint for batch weight, indicating the total weight of work pieces in a batch shall not exceed the maximum load capacity of the furnace. Equation (8) indicates the intersection of holding temperature intervals of all work pieces in a batch and shall be not void, which means all work pieces in a charging batch shall be temperature compatible. Equation (9) indicates the holding temperature for a charging batch and shall be defined in a manner all work pieces can be covered but as low as possible.…”

Section: Mathematical Modelmentioning

confidence: 99%

See 1 more Smart Citation

Research on Energy-Saving Charging Combination Based on Group Genetic Algorithm (GGA) and Best Fit (BF) Algorithm

Zhu¹,

Haixing²,

Tong³

et al. 2014

TOAUTOCJ

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As a traditional high energy-consuming industry, the forging industry consumes a lot of energy. The activity consuming highest energy during forging process is the forging and heating. In order to solve the problem regarding how to separate work pieces with different holding temperature intervals and combine them for charging, a group of work pieces with compatible holding temperatures are analyzed for energy-saving charging combination. To aim this, an optimal charging combination model for t energy saving is proposed as well as a group genetic algorithm based on temperature compatibility rule for problem solving. To obtain better optimal solutions, a BF (best fit) heuristic algorithm is designed to generate individuals and initial population as well as an improved crossover operator. At last an instance is analyzed to verify the effectiveness of the proposed model and algorithm, and compared with the present charging planning used in forging enterprises.

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Section: Mathematical Modelmentioning

confidence: 99%

“…The group genetic algorithm (GGA) [8] is adopted in this paper to solute the optimizations. GGA is different than other genetic algorithms in its coding way and genetic operations [9].…”

Section: Group Genetic Algorithm Solution Based On Temperature Compatmentioning

confidence: 99%

Research on Energy-Saving Charging Combination Based on Group Genetic Algorithm (GGA) and Best Fit (BF) Algorithm

Zhu¹,

Haixing²,

Tong³

et al. 2014

TOAUTOCJ

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“…Scheduling jobs machines' layout in individual cells are operational features which must be determined at the design stage. There are many practical cases where the processing times and other inputs to classical CMS problems are highly uncertain and a small change on processing time could change the results, significantly (Shanker & Vrat, 1998;Ghosh et al, 2011;Ghezavati & SaidiMehrabd, 2010). In addition, there are also many other realistic problems of operations management where both machines and workers can improve their performance by repeating the production operations.…”

Section: Introductionmentioning

confidence: 99%

A new stochastic mixed integer programming to design integrated cellular manufacturing system: A supply chain framework

Ghezavati¹

2011

IJIEC

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This research defines a new application of mathematical modeling to design a cellular manufacturing system integrated with group scheduling and layout aspects in an uncertain decision space under a supply chain characteristics. The aim is to present a mixed integer programming (MIP) which optimizes cell formation, scheduling and layout decisions, concurrently where the suppliers are required to operate exceptional products. For this purpose, the time in which parts need to be operated on machines and also products' demand are uncertain and explained by set of scenarios. This model tries to optimize expected holding cost and the costs regarded to the suppliers network in a supply chain in order to outsource exceptional operations. Scheduling decisions in a cellular manufacturing framework is treated as group scheduling problem, which assumes that all parts in a part group are operated in the same cell and no inter-cellular transfer is required. An efficient hybrid method made of genetic algorithm (GA) and simulated annealing (SA) will be proposed to solve such a complex problem under an optimization rule as a sub-ordinate section. This integrative combination algorithm is compared with global solutions and also, a benchmark heuristic algorithm introduced in the literature. Finally, performance of the algorithm will be verified through some test problems.

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“…The walks are performed by iterative dealings that move from the present answer to a different one within the search area. Population based metaheuristics [19] share the same concepts and viewed as an iterative improvement in a population of solutions. First, the population is initialized.…”

Section: Definition1mentioning

confidence: 99%