Production planning in manufacturing/remanufacturing environment using genetic algorithm

Lim, Chihoon; Sim, Eoksu

doi:10.1145/1068009.1068382

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…Also the variable workstation constraint causes additional complexity on the problem. Because of these reasons, GA has been used in this study instead of other population-based algorithms in the solution of this new type of WSSP problem [30], [31], [32].…”

Section: Simulation-based Gamentioning

confidence: 99%

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

Karaoğlan¹

2022

Pamukkale J Eng Sci

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Section: Simulation-based Gamentioning

confidence: 99%

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

Karaoğlan¹

2022

Pamukkale J Eng Sci

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“…GA has been used by researchers to optimize remanufacturing optimization such McGovern and Gupta [78] that minimize workstations, and ensures similar idle times, as well as other end-of-life specific concerns for balancing in a disassembly line. In the same light, GA has been employed in field of balancing disassembly lines and remanufacturing [79]- [81]. Recently, Li et al [82] designed a multi-objective model in reverse logistics to optimize cost and service level simultaneously.…”

Section: A Genetic Algorithmmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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“…Therefore, GAs are powerful alternative tools to traditional optimization methods. GAs have been successfully used in many fields, such as scheduling (Wall, 1996;Lim and Sim, 2005), function optimization (Houck et al, 1998), machine learning (Goldberg and Holland, 1988;Grefenstette, 1994;Shapiro, 1998) and have become an important method of soft computing.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Development of soft computing and applications in agricultural and biological engineering

Huang

Lan

Thomson

et al. 2010

Computers and Electronics in Agriculture

250

126

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Soft computing is a set of "inexact" computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed.

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Production planning in manufacturing/remanufacturing environment using genetic algorithm

Cited by 5 publications

References 1 publication

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Development of soft computing and applications in agricultural and biological engineering

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