memory structure adapted simulated annealing algorithm for a green vehicle routing problem

• Published in: Environmental science and pollution research international Vol. 22; no. 5; pp. 3279 - 3297
• Main Authors: Küçükoğlu, İlker, Ene, Seval, Aksoy, Aslı, Öztürk, Nursel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.03.2015; Springer Berlin Heidelberg; Springer Nature B.V
• Subjects: Air Pollutants - analysis; Air Pollution - prevention & control; Algorithms; Analysis; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Carbon dioxide; Carbon Dioxide - analysis; Carbon dioxide emissions; Climate change; Conservation of Energy Resources; consumers (people); Consumption; Customers; Earth and Environmental Science; ecosystems; Ecotoxicology; Emissions; Energy consumption; energy use and consumption; Environment; Environmental Chemistry; Environmental effects; Environmental Health; Environmental impact; Environmental policy; Environmental science; Environmental Science and Pollution Sensing; Fuel consumption; Government regulations; Heuristic; laws and regulations; Linear programming; Literature reviews; Logistics; Low emission vehicles; Mathematical models; Modeling and Remediation; Models, Theoretical; Monitoring; Postal & delivery services; Reduction; Route selection; Routing; Studies; supply chain; Supply chain sustainability; Supply chains; toxicity; Transportation; Vehicle Emissions - analysis; Vehicles; Waste Water Technology; Water Management; Water Pollution Control; Windows (intervals); Green vehicle routing problem; Green logistics; Simulated annealing algorithm; Fuel consumption; CO; Mixed integer linear programming model; emissions
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-014-3253-5

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., CO₂emissions, 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 CO₂emissions. 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 CO₂emissions 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 CO₂emissions.