Designing a cellular manufacturing system considering decision style, skill and job security by NSGA-II and response surface methodology

Azadeh, A.; Pashapour, Shima; Zadeh, Saeed Abdolhossein

doi:10.1080/00207543.2016.1178407

Cited by 15 publications

(9 citation statements)

References 38 publications

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“…In the literature, there are two main techniques for parameters tuning of algorithms: RSM and Taguchi. Based on our experience in using Taguchi and RSM for optimizing production scheduling problems (i.e., Shahriari et al 2016 ; Ghaleb et al 2020a ; Sharifi and Taghipour 2021 ) and the related literature (i.e., Assid et al 2015 ; Najafi and Behnoud 2015 ; Azadeh et al 2016 ; Zahraee et al 2018 ), we decided to use the RSM technique for tuning the parameters of the provided NSGA-II and NRGA algorithms.…”

Section: Case Studymentioning

confidence: 99%

Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic

et al. 2022

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This paper, for the first time, presents a production scheduling model for a production line considering physical distancing between the machines' workforces. The production environment is an unrelated parallel-machine, in which for producing each part, different machines with different production rates and the required number of workers are available. We propose a three-objective mixed-integer linear programming mathematical model that aims to maximize the manufacturer's total benefit, parts' safety stock (SS) index, and the workforce's physical distance over a finite horizon (one year) by determining the optimal scheduling of the parts on the machines. Since a large production scheduling problem belongs to the Np-Hard category of problems, a non-dominated sorting genetic algorithm, and a non-dominated ranked GA algorithm are developed to solve the presented model in two stages using the empirical data from a Canadian plastic injection mold company. In the first stage, the LP-metrics approach is utilized for validating the meta-heuristics on a reduced-size problem. In the second stage, the validated meta-heuristics are utilized to optimize the company's yearly production schedule. The results indicate both metaheuristics are performing well in determining the optimal solution. Moreover, implementing physical distancing in the company reduces the company's monthly net benefit by around 9.56% compared to the normal operational conditions (without considering physical distancing).

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Section: Case Studymentioning

confidence: 99%

Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic

et al. 2022

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“…From the searched literature, the problems considered under the category of assignment problems are generalized assignment problem [46], cell formulation problem [47], [48], [49], critical job assignment problem [50], load balancing of network traffic [51], platform-assignment problem [52], [53], frequency assignment problem [54], multi-stage weapon target assignment (MWTA) problem [55], [56], [57], location problem [58], [59], land-use optimization [60], optimal configuration selection [61], optimal transmission line assignment [62], multi-skilled worker assignment problem [63], spectrum assignment problem [64] and multi-stage assignment optimization for emergency rescue teams [65].…”

Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

“…In [49], Azadeh et al utilized NSGA-II to solve a large-sized cell formation problem, a traditional problem of assignment of parts, operators, and machines to the cells. In this study, the operators' personality and decision-making styles, expertise in dealing with machines, and job security are also incorporated, which demonstrates the novelty of the proposed model.…”

Section: A) Assignment Problemmentioning

confidence: 99%

“…The choice of the best-compromised solution among the set of all compromised solutions is made by decision-makers using the preference information. Many techniques based on MCDM, data clustering, fuzzy logic, and some other criteria that are applied to find the best-compromised solution in the studied literature is given in Table XII [201], [136], [139], [49], [183], [54] t-test [127], [45], [199], [133], [117], [50] Wilcoxon sign rank test [141], [168], [109], [208], [93] Wilcoxon rank-sum test [201], [114], [205], [80] Statistical hypothesis tests [159], [47], [107], [194] Kruskal-Wallis test [201], [94], [54] Friedman test [184], [110], [208] Wilcoxon paired test [137], [45] Shapiro-Wilk statistical hypothesis test [45], [54] ANOM test [79], [85] Sign test, Permutation test, Bonferroni/Dunn's test, Welch test, Levene test, Kolmogorov-Smirnov (K-S) test, Post-hoc test [137], [116], [110], [54], [54],…”

Section: Post-pareto Optimality Analysismentioning

confidence: 99%

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A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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“…Azadeh et al, proposed a new mathematical programming model for the formation of production cells in which the personality of operators, decision-making style, skill in working with machines, as well as job security are simultaneously incorporated. They solved their model with the NSGA-II metaheuristic algorithm and used the MOPSO multi-objective particle multi-objective algorithm to validate their solution method [98]. Shirzadi et al, provide an integer bi-objective model for designing a cellular production system by minimizing total costs (intracellular and intercellular transport, overhead costs, and restarting) and maximizing the reliability of processing paths.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Cell Production System Design: A Literature Review

Ghahremani-Nahr

Bathaee

Mazloumzadeh

et al. 2021

IJIMES

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In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology:To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously.Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified.Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed.

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Designing a cellular manufacturing system considering decision style, skill and job security by NSGA-II and response surface methodology

Cited by 15 publications

References 38 publications

Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic

Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

Cell Production System Design: A Literature Review

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