Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/34233
Title: A memory structure adapted simulated annealing algorithm for a green vehicle routing problem
Authors: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
0000-0002-5075-0876
0000-0001-8248-8924
0000-0002-2971-2701
0000-0002-9835-0783
Kuçukoğlu, Ilker
Ene, Seval
Aksoy, Aslı
Ozturk, Nursel
D-8543-2015
AAG-8949-2021
AAG-9235-2021
AAG-9336-2021
55763879600
48461146800
35221094400
7005688805
Keywords: CO2 emissions
Fuel consumption
Green logistics
Green vehicle routing problem
Mixed integer linear programming model
Simulated annealing algorithm
Optimization
Routes
Truck
Issue Date: Mar-2015
Publisher: Springer
Citation: Kuçukoglu, I. (2015). "A memory structure adapted simulated annealing algorithm for a green vehicle routing problem". Environmental Science and Pollution Research, 22(5), 3279-3297.
Abstract: Currently, reduction of carbon dioxide (CO2) emissions and fuel consumption has become a critical environmental problem and has attracted the attention of both academia and the industrial sector. Government regulations and customer demands are making environmental responsibility an increasingly important factor in overall supply chain operations. Within these operations, transportation has the most hazardous effects on the environment, i.e., CO2 emissions, fuel consumption, noise and toxic effects on the ecosystem. This study aims to construct vehicle routes with time windows that minimize the total fuel consumption and CO2 emissions. The green vehicle routing problem with time windows (G-VRPTW) is formulated using a mixed integer linear programming model. A memory structure adapted simulated annealing (MSA-SA) meta-heuristic algorithm is constructed due to the high complexity of the proposed problem and long solution times for practical applications. The proposed models are integrated with a fuel consumption and CO2 emissions calculation algorithm that considers the vehicle technical specifications, vehicle load, and transportation distance in a green supply chain environment. The proposed models are validated using well-known instances with different numbers of customers. The computational results indicate that the MSA-SA heuristic is capable of obtaining good G-VRPTW solutions within a reasonable amount of time by providing reductions in fuel consumption and CO2 emissions.
Description: Bu çalışma, 21-24 Eylül 2013 tarihleri arasında Kuşadası[Türkiye]’de düzenlenen 2. International Conference on Water, Energy, and Environment (ICWEE)'da bildiri olarak sunulmuştur.
URI: https://doi.org/10.1007/s11356-014-3253-5
http://hdl.handle.net/11452/34233
ISSN: 0944-1344
https://link.springer.com/article/10.1007/s11356-014-3253-5
Appears in Collections:Scopus
Web of Science

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