A novel vehicle routing algorithm for route optimization using best-worst method and ranking alternatives for similarity to ideal solution

Abstract

The Vehicle Routing Problem (VRP) is important in supply chain management as it optimizes goods and services delivery to customers, resulting in improved organizational productivity. This study introduces an innovative hybrid methodology integrating the Multi-Criteria Decision Making (MCDM) approach with Clarke and Wright’s savings algorithm to tackle the capacitated vehicle routing problem. In addition to the conventional aim of optimizing truck routes, this strategy considers customer satisfaction. The initial step involves clustering all customers through the utilization of Clarke and Wright’s savings algorithm, which efficiently organizes customers into groups based on their geographical closeness. Following this, the hybrid Best-Worst Method (BWM) and Ranking Alternatives For Similarity to Ideal Solution (RAFSI) method are utilized to allocate the best routes and establish customer prioritization. The major objective of this study is to reduce overall transportation expenses while ensuring compliance with vehicle capacity limitations, aiming to improve customer satisfaction. The proposed approach seeks to balance cost-efficiency and customer-centricity in vehicle routing by including customer prioritizing and Clarke and Wright’s savings algorithm. The effectiveness and practical application of the proposed methodology are demonstrated through a case in the food industry. The obtained results using the proposed methodology give a more precise platform for decision-making and highlight its relevance for enhancing supply chain performance and addressing the intricate challenges associated with the capacitated vehicle routing problem. The hybrid technique presented in this study provides a comprehensive framework for effectively tackling the intricate challenges associated with the capacitated vehicle routing problem.