A Novel Modeling Approach for Solving the Cell Formation Problem

Dmytryshyn, Taras; Ismail, Mohamed A.; Rashwan, Ola

doi:10.1115/imece2018-88128

Cited by 3 publications

(2 citation statements)

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“…Mohammad Mohammadi et al [26] approached a layout problem in cellular manufacturing systems with alternative processing routings. Dmytryshyn et al [27] suggested a novel modeling approach for solving the cell-formation problem. Kamalakannan et al [28] developed a simulated annealing algorithm for solving CFP with ratio level data.…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

Modrák

Sudhakarapandian

Semanco³

2021

Applied Sciences

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In manufacturing-cell-formation research, a major concern is to make groups of machines into machine cells and parts into part families. Extensive work has been carried out in this area using various models and techniques. Regarding these ideas, in this paper, experiments with varying parameters of the popular metaheuristic algorithm known as the genetic algorithm have been carried out with a bi-criteria objective function: the minimization of intercell moves and cell load variation. The probability of crossover (A), probability of mutation (B), and balance weight factor (C) are considered parameters for this study. The data sets used in this paper are taken from benchmarked literature in this field. The results are promising regarding determining the optimal combination of the genetic parameters for the machine-cell-formation problems considered in this study.

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Section: A Brief Literature Reviewmentioning

confidence: 99%

Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

Modrák

Sudhakarapandian

Semanco³

2021

Applied Sciences

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“…Dmytryshyn et al presented a new modelling approach called Progressive Modelling and demonstrates it by solving the cell formation problem [13]. In his paper, the Progressive Modelling, a component-based optimization technique, is used to solve the cell formation problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimising Production Systems Using Genetic Algorithms

Kremljak¹

2019

DAAAM Proceedings

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As the most widespread type of evolution algorithms, genetic algorithms (GA) have become a very popular means of heuristically resolving optimisation-related issues. GA have proven extremely powerful particularly in cases when solutions from a wide scope of examination are required. Hitherto, GA have resolved non-deterministic problems. Solutions have not been sought in pre-determined ways and an array thereof has been handled simultaneously which has proven particularly appropriate for problems faced in complex production and business processes. Instead of certainty, order and logical deductions, we are faced with randomisation, evolution and self-organisation.In this paper, GA are applied on the case-basis of production of aluminium products (variation of production parameters or comparison of equipment layout). The main target of the presented model is to achieve an optimum production plan by taking into consideration dynamic conditions, such as: customer orders, inventories, capacity utilisation rate, quality or raw materials and products and original or cellular layout of equipment.

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