Hybrid genetic algorithm for inventory routing problem with carbon emission consideration

Abstract

Inventory Routing Problem (IRP) has been continuously developed and improved due to pressure from global warming issue particularly related to greenhouse gases (GHGs) emission. The burning of fossil fuel for transportations such as cars, trucks, ships, trains, and planes primarily emits GHGs. Carbon dioxide (CO2) from burning of fossil fuel to power transportation and industrial process is the largest contributor to global GHGs emission. Therefore, the focus of this study is on solving a multi-period inventory routing problem (MIRP) involving carbon emission consideration based on carbon cap and offset policy. Hybrid genetic algorithm (HGA) based on allocation first and routing second is used to compute a solution for the MIRP in this study. The objective of this study is to solve the proposed MIRP model with HGA then validate the effectiveness of the proposed HGA on data of different sizes. Upon validation, the proposed MIRP model and HGA is applied on real data and parameter sensitivity analysis is performed on the MIRP model. The HGA is found to be able to solve small size and large size instances effectively by providing near optimal solution in relatively short CPU execution time. In addition, the increase in unit carbon price results in the increase of the supply chain’s total cost while the increase in carbon cap results in the decrease of supply chain’s total cost. The results from the analysis gave an indication that the unit carbon price and carbon cap need to be thoroughly designed so that it will not burden the participating companies of carbon emission regulation and environment.