A multi-objective solution framework for the assembly inventory routing problem considering supply risk and carbon offset policies

Abstract

This study aims to provide novel insights into the multi-objective optimisation of an inbound assembly inventory routing problem (IRP) by considering customer demand uncertainty, stochastic supply failure risks, and a carbon offset policy. An inbound IRP network generally comprises several geographically dispersed suppliers, vehicle depots, and assembly plants. In this study, a new mixed-integer nonlinear optimisation model is formulated considering two objective functions: (i) minimising the total supply network cost and (ii) maximising a low-risk supplier priority function with practical constraints. A hybrid non-dominated sorting genetic algorithm-II was developed to obtain near-optimal Pareto solutions for various small, medium, and large IRP instances. The Pareto solutions were compared with the efficient solution obtained using the ε-constraint method. Finally, the best Pareto solution was obtained using the multi-criteria decision-making technique. The results indicate that implementing supply risk-mitigation strategies leads to higher overall supply network costs and carbon emissions. However, a sensitivity analysis based on factorial simulation experiments indicates that a supply risk-mitigation strategy is advantageous if component prices are low and supply capacity, fuel price, and demand variations are high. In addition, the lower the demand variation, the lower the expected overall network cost and carbon emissions.