Seval Ene

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

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.

A hybrid metaheuristic algorithm for the green vehicle routing problem with a heterogeneous fleet

In this study, the green vehicle routing problem (GVRP) with a heterogeneous fleet is presented for both capacity and time-windows constraints to reduce fuel consumption and consequently to minimise CO2 emissions. A hybrid metaheuristic algorithm (HMA) is developed to solve this problem to analyse the effect of a heterogeneous fleet on reducing the fuel consumption for the specified variants of GVRP, such as GVRP with capacity constraints and GVRP with time-windows constraints. The proposed HMA is validated using well-known instances with different numbers of customers and fleet configurations. The computational results indicated that the HMA is capable of obtaining good results for GVRP variants within a reasonable amount of time by providing remarkable reductions in fuel consumption and greener fleet configurations.