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Energy‐efficient an...
Energy‐efficient and sustainable supply chain in the manufacturing industry
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- Beheshtinia, Mohammad Ali (författare)
- Industrial Engineering Department, Faculty of Engineering, Semnan University, Iran,Semnan Univ, Fac Engn, Ind Engn Dept, Semnan, Iran.
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- Fathi, Masood (författare)
- Uppsala universitet,Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Division of Industrial Engineering and Management, Uppsala University, Sweden,Production and Automation Engineering,Industriell teknik,Univ Skövde, Div Intelligent Prod Syst, POB 408, SE-54128 Skövde, Sweden.
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Industrial Engineering Department, Faculty of Engineering, Semnan University, Iran Semnan Univ, Fac Engn, Ind Engn Dept, Semnan, Iran (creator_code:org_t)
- 2022-11-15
- 2023
- Engelska.
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Ingår i: Energy Science & Engineering. - : John Wiley & Sons. - 2050-0505. ; 11:1, s. 357-382
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Energisystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Energy Systems (hsv//eng)
Nyckelord
- energy consumption
- genetic algorithm
- mathematical model
- scheduling
- supply chain
- sustainability
- Production and Automation Engineering
- Produktion och automatiseringsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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