Solving the Goods Transportation Problem Using Genetic Algorithm with Nearest-Node Pairing Crossover Operator

Goods transportation is a critical part of supply chain management and the distance covered in the delivery process indirectly reflects the sustainability level of the supply chain, especially in the environmental aspect. In the wake of climate change issues faced worldwide and the increasing public concern in pollution reduction, it would be in the best interest of companies to not just minimize their operational costs but to also do their part in reducing externalities such as air pollution and noise pollution. In this study, a goods transportation problem is tackled to reduce the distance covered in the transportation process by modelling the problem as a Travelling Salesman Problem (TSP) and solving it using Genetic Algorithm. We propose a new crossover operator namely the Nearest-Node Pairing Crossover (NNPX) that is specifically designed to tackle a Travelling Salesman Problem (TSP) by exploiting the distance aspect of the problem. We evaluate the performance of NNPX compared to two other crossover operators: Order Crossover (OX) and Position-Based Crossover (PBX). The results reveal that the performance of NNPX is outstanding compared to OX and PBX. We found that NNPX has a better rate of convergence as it consistently yields lower distances in fewer iterations. In addition, NNPX does not depend on a large population size for faster convergence. In a nutshell, this study proposes a new crossover operator NNPX that is comparatively more efficient when used to solve the goods transportation problem, thus reducing the associated operational cost and externalities. (paper)