Supply Chain Optimization Considering Sustainability Aspects

Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when conside...

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Autores principales:	Mohammad Ali Beheshtinia, Parisa Feizollahy, Masood Fathi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	genetic algorithm supply chain scheduling sustainability multi-criteria decision-making mathematical model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/52dd4b82de5a45b1a2500727b4a48767
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spelling	oai:doaj.org-article:52dd4b82de5a45b1a2500727b4a487672021-11-11T19:34:20ZSupply Chain Optimization Considering Sustainability Aspects10.3390/su1321118732071-1050https://doaj.org/article/52dd4b82de5a45b1a2500727b4a487672021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11873https://doaj.org/toc/2071-1050Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.Mohammad Ali BeheshtiniaParisa FeizollahyMasood FathiMDPI AGarticlegenetic algorithmsupply chainschedulingsustainabilitymulti-criteria decision-makingmathematical modelEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11873, p 11873 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	genetic algorithm supply chain scheduling sustainability multi-criteria decision-making mathematical model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	genetic algorithm supply chain scheduling sustainability multi-criteria decision-making mathematical model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Mohammad Ali Beheshtinia Parisa Feizollahy Masood Fathi Supply Chain Optimization Considering Sustainability Aspects
description	Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.
format	article
author	Mohammad Ali Beheshtinia Parisa Feizollahy Masood Fathi
author_facet	Mohammad Ali Beheshtinia Parisa Feizollahy Masood Fathi
author_sort	Mohammad Ali Beheshtinia
title	Supply Chain Optimization Considering Sustainability Aspects
title_short	Supply Chain Optimization Considering Sustainability Aspects
title_full	Supply Chain Optimization Considering Sustainability Aspects
title_fullStr	Supply Chain Optimization Considering Sustainability Aspects
title_full_unstemmed	Supply Chain Optimization Considering Sustainability Aspects
title_sort	supply chain optimization considering sustainability aspects
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/52dd4b82de5a45b1a2500727b4a48767
work_keys_str_mv	AT mohammadalibeheshtinia supplychainoptimizationconsideringsustainabilityaspects AT parisafeizollahy supplychainoptimizationconsideringsustainabilityaspects AT masoodfathi supplychainoptimizationconsideringsustainabilityaspects
_version_	1718431465809641472

Supply Chain Optimization Considering Sustainability Aspects

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