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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Bibliographic Details
Published inSustainability Vol. 13; no. 21; p. 11873
Main Authors Beheshtinia, Mohammad Ali, Feizollahy, Parisa, Fathi, Masood
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 2021
Subjects
Algorithms
Decision making
Environmental aspects
genetic algorithm
Genetic algorithms
Integer programming
Job shops
Manufacturing
mathematical model
multi-criteria decision-making
Multiple criterion
Optimization
Production and Automation Engineering
Produktion och automatiseringsteknik
scheduling
Suppliers
supply chain
Supply chains
Sustainability
TOPKOR
genetic algorithm
multi-criteria decision-making
supply chain
scheduling
mathematical model
TOPKOR
sustainability
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su132111873