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Energy‐efficient and sustainable supply chain in the manufacturing industry
Industrial Engineering Department, Faculty of Engineering, Semnan University, Iran.
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Uppsala University, Sweden. (Production and Automation Engineering)ORCID iD: 0000-0001-5530-3517
2023 (English)In: Energy Science & Engineering, ISSN 2050-0505, Vol. 11, no 1, p. 357-382Article in journal (Refereed) Published
Abstract [en]

This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023. Vol. 11, no 1, p. 357-382
Keywords [en]
energy consumption, genetic algorithm, mathematical model, scheduling, supply chain, sustainability
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-22065DOI: 10.1002/ese3.1337ISI: 000883782600001Scopus ID: 2-s2.0-85143224043OAI: oai:DiVA.org:his-22065DiVA, id: diva2:1712127
Note

CC BY 4.0

First published: 27 October 2022

Correspondence: Masood Fathi, Division of Intelligent Production Systems, University of Skövde, P.O. Box 408, SE‐541 28 Skövde, Sweden. Email: masood.fathi@his.se and fathi.masood@gmail.com

Available from: 2022-11-20 Created: 2022-11-20 Last updated: 2023-01-31Bibliographically approved

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