Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption

Ieva Meidute-Kavaliauskiene, Nihal Sütütemiz, Figen Yıldırım, Shahryar Ghorbani and Renata Činčikaitė
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Ieva Meidute-Kavaliauskiene: Department of Business Technologies and Entrepreneurship, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania
Nihal Sütütemiz: Department of Management Information Systems, University of Sakarya, Sakarya 54050, Turkey
Figen Yıldırım: Department of International Trade, Istanbul Commerce University, Istambul 34445, Turkey
Shahryar Ghorbani: Department of Production Management, University of Sakarya, Sakarya 54050, Turkey
Renata Činčikaitė: Department of Business Technologies and Entrepreneurship, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania

Energies, 2022, vol. 15, issue 4, 1-24

Abstract: Cross-docking is an excellent way to reduce the space required to store goods, inventory management costs, and customer order delivery time. This paper focuses on cost optimization, scheduling incoming and outgoing trucks, and green supply chains with multiple cross-docking. The three objectives are minimizing total operating costs, truck transportation sequences, and carbon emissions within the supply chain. Since the linear programming model is an integer of zero and one and belongs to NP-hard problems, its solution time increases sharply with increasing dimensions. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) were used to find near-optimal solutions to the problem. Then, these algorithms were compared with criteria such as execution time and distance from the ideal point, and the superior algorithm in each criterion was identified.

Keywords: non-dominated sorting genetic algorithm-II (NSGA-II); multi-objective particle swarm optimization (MOPSO); cross-docking (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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