Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to...

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Vydáno v:International journal of production research Ročník 57; číslo 8; s. 2531 - 2547
Hlavní autoři: Touzout, Faycal A., Benyoucef, Lyes
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 18.04.2019
Taylor & Francis LLC
Témata:
AMOSA
Classification
Completion time
Computer simulation
Evolutionary algorithms
Genetic algorithms
Greenhouse effect
Greenhouse gases
Integer programming
Iterative methods
Linear programming
Manufacturing
multi-objective integer linear programming
Multiple objective analysis
NSGA-II
process plan generation
Production costs
reconfigurable manufacturing system (RMS)
Reconfiguration
Simulated annealing
Sorting algorithms
Sustainability
ISSN:0020-7543, 1366-588X
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Shrnutí:Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to solve the multi-objective sustainable process plan generation problem in a reconfigurable environment. In addition to the total production cost and the completion time, we use the amount of greenhouse gases (GHG) emitted during the manufacturing process as a sustainability criterion. We propose an iterative multi-objective integer linear programming (I-MOILP) approach and its comparison with adapted versions of the two well-known evolutionary algorithms, respectively, the Archived Multi-Objective Simulated Annealing (AMOSA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Moreover, we study the influence of the probabilities of genetic operators on the convergence of the adapted NSGA-II. To illustrate the applicability of the three approaches, an example is presented and obtained numerical results analysed.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2018.1522006