A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

Bensmaine, Abderrahmane; Dahane, Mohammed; Benyoucef, Lyès

doi:10.1016/j.cie.2012.09.008

Cited by 114 publications

(22 citation statements)

References 11 publications

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“…To map the whole Pareto optimal frontier, the optimization procedure often should be repeated many times, which is a time-consuming process [18]. Evolutionary algorithms have been recognized to be well suited to multiobjective optimization, for example, one of the most efficient and commonly used versions of multi-objective GA (NSGA-II) can handle large and complex constraints by natural-inspired operators, and the NSGA-II algorithm has low computational complexity and good convergence by applying effective elite strategy than the previous evolutionary algorithms [15]; it has been successfully applied in solving many complex engineering optimization problems and achieved remarkable results [19][20][21][22][23][24][25][26]. First of all, NSGA-II is selected to solve the multi-objective optimization model for oil-gas production process, and then, in order to further improve the diversity and convergence of Pareto optimal solutions obtained by NSGA-II algorithm when solving the complicated and constrained optimization problems, an improved NSGA-II algorithm (I-NSGA-II) is proposed in this paper.…”

Section: Multi-objective Optimization For Oil-gas Production Process mentioning

confidence: 99%

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Tan

Gao

Wang

2015

Journal of the Taiwan Institute of Chemical Engineers

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Section: Multi-objective Optimization For Oil-gas Production Process mentioning

confidence: 99%

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Tan

Gao

Wang

2015

Journal of the Taiwan Institute of Chemical Engineers

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“…With the help of a genetic algorithm, they computed lot sizes, analogous configurations, and optimal scheduling of production activities. Furthermore, a non-dominated sorting genetic algorithm (NSGA-II) was projected by Bensmaine et al [37] to choose the candidate reconfigurable machines. The main aim was a reduction of the overall cost (sum of manufacturing, reconfiguration, tool changing, and tooling costs) and overall completion time.…”

Section: Literature Surveymentioning

confidence: 99%

Strategic Outcome Using Fuzzy-AHP-Based Decision Approach for Sustainable Manufacturing

et al. 2019

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The manifestation of new part designs and continuously changing market demands as well as the requirements of new functions and technologies often results in higher material cost, lesser machine utilization, and extensive wastage of energy. As a consequence, companies across the world are striving for sustainable manufacturing, which can ensure flexibility as well as adaptability, with higher productivity and lesser wastage of resources. The dynamic and competitive nature of the world market emphasizes the importance of economically sustainable setups, such as reconfigurable cellular manufacturing systems (RCMSs). Indeed, among several cutting-edge strategies, the RCMS is the most prominent owing to its versatility, rationality, and resilience. However, one of the limitations associated with RCMS is the evaluation and selection of the best configuration that can meet abrupt changes and achieve manufacturing sustainability. Therefore, organizations intending to reconfigure have to address the issue of evaluating all possible alternatives and selecting the best one using a well-defined methodology. This paper focuses on evaluating and finding the best configuration using multi-criteria decision-making (MCDM) approaches depending on PROMETHEE and VIKOR. The fuzzy analytic hierarchy process (FAHP) is utilized to compute the weights of various criteria because it also considers any uncertainty and vagueness existing in the problem. The assessing attributes in this study are selected by keeping in mind the objective of sustainable manufacturing. The two MCDM methods are utilized for ranking different configurations to ascertain the results obtained from the other. The study accomplished in this paper is related to manufacturing setups that need to be reconfigured. The number of manufacturing configurations is determined, and simulation models are established for each configuration. The simulation outcomes are examined using the FAHP-PROMETHEE and FAHP-VIKOR to assess the appropriateness of each configuration depending on the identified performance measures. The results of the experiments show the importance of employing MCDM in RCMS to achieve sustainable manufacturing and determining the most effective setup.

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“…A quantitative model was also developed for RMS scalability by Wang et al [7] which calculated the number of reconfigurations based on adjustment gradient. NSGA-II technique has also been used by Bensmaine et al [19] in the selection of optimal machines from the set of candidate machine configurations. In this research work multi product case with high degree of freedom can be considered as future work and with the idea of co-evolution, the machine configurations can be used for different product designs over and over again preserving the feasibility of the system for a long period of time.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum machine capabilities for reconfigurable manufacturing systems

Asghar

Zaman

Baqai

et al. 2018

Int J Adv Manuf Technol

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This is an author-deposited version published in: https://sam.ensam.eu Handle ID AbstractReconfigurable manufacturing systems constitute a new manufacturing paradigm and are considered the future of manufacturing because of their changeable and flexible nature. In a reconfigurable manufacturing environment, basic modules can be rearranged, interchanged or modified, to adjust the production capacity according to production requirements. Reconfigurable machine tools have modular structure comprising of basic and auxiliary modules that aid in modifying the functionality of a manufacturing system. As the product's design and its manufacturing capabilities are closely related, the manufacturing system is desired to be customizable to cater for all the design changes. Moreover, the performance of a manufacturing system lies in a set of planning and scheduling data incorporated with the machining capabilites keeping in view the market demands. This research work is based on the coevolution of process planning and machine configurations in which optimal machine capabilities are generated through the application of multi-objective genetic algorithms. Furthermore, based on these capabilities, system is tested for reconfiguration in case of production changeovers. Since, in a reconfigurable environment, same machine can be used to perform different tasks depending on the required configuration, the subject research work assigns optimum number of machines by minimizing the machining capabilities to carry out different operations in order to streamline production responses. An algorithm has also been developed and verified on a part family. As a result of the proposed methodology, an optimized reconfigurable framework can be achieved to realize optimal production of a part family. Finally, the proposed methodology was applied on a case study and respective conclusions were drawn.

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A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

Cited by 114 publications

References 11 publications

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Strategic Outcome Using Fuzzy-AHP-Based Decision Approach for Sustainable Manufacturing

Optimum machine capabilities for reconfigurable manufacturing systems

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