GA-Based Fuel-Efficient Transfer Path Selection Model for Delivering Construction Materials

Abstract

Oil prices may continue to rise indefinitely until alternative fossil fuels or renewable energies are commercialized. In this sense, general contractors who have the responsibility of delivering construction materials should select an optimized alternative that needs the least oil among the various combinations of truck types of logistic companies, routes, and suppliers. In this context, the objective of this paper was to develop a genetic-algorithm (GA)-based fuel-efficient transfer path selection model (GAFETPSM) for delivering construction materials with minimum fuel consumption. A GAFETPSM considers the truck type, road type, and fuel-efficiency variation by load, and the constraints of capacity, number of trucks, and total load to be delivered. Finally, a case study showed that GAFETPSM is superior to the simulated-annealing (SA)-based model in terms of fuel consumption in delivering the same weight of construction materials. It is expected that GAFETPSM could contribute to reduce the carbon-dioxide emission by using less oil.