Genetic algorithm variants for solving multi-objective multi-route shortest path problem: application to transportation and logistics

This paper mainly examines a multi-objective multi-route shortest path problem (MOMRSPP) for a directed graph. For solving MOMRSPP, we have proposed two variants of genetic algorithm (GA), the aspiration level (AL)-based non-dominated sorting genetic algorithm (NSGA)-II and the AL-based NSGA-III. The transportation and logistics problem has been considered as an application in the context of MOMRSPP. A comparison has been performed between the newly developed techniques and some existing GA variants. The sensitivity of objectives has been determined with shape parameters and AL. The coverage has been calculated to assess the effectiveness of the proposed methodologies. Moreover, fuzzy MOMRSPP (FMOMRSPP) and uncertain MOMRSPP (UMOMRSPP) are modeled and solved using GA variants. This research concludes that the proposed AL-based techniques are appropriate for decision-makers concerning AL.