Energy‐efficient and sustainable supply chain in the manufacturing industry

• Published in: Energy science & engineering Vol. 11; no. 1; pp. 357 - 382
• Main Authors: Beheshtinia, Mohammad Ali, Fathi, Masood
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.01.2023; Wiley
• Subjects: Air pollution; Algorithms; Complexity; Costs; Energy consumption; Energy efficiency; Fines & penalties; genetic algorithm; Genetic algorithms; Heuristic; Integer programming; Linear programming; Literature reviews; Manufacturers; Manufacturing; Manufacturing industry; mathematical model; Mathematical models; Mixed integer; Optimization; Optimization techniques; Production and Automation Engineering; Production scheduling; Produktion och automatiseringsteknik; Scheduling; Suppliers; supply chain; Supply chains; sustainability; Transportation; Vehicles
• ISSN: 2050-0505; 2050-0505
• DOI: 10.1002/ese3.1337

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. Reducing energy consumption in supply chain networks. Optimal integrated production and transportation scheduling. Novel development of the genetic algorithm and mathematical optimization model.