Optimizing Capacitated Vehicle Routing Problem-A Case Study Of Agriculture Product Company

Abstract

In today's competitive business landscape, optimizing transportation costs while ensuring timely and efficient delivery has become paramount for companies with value based delivery policies. This thesis presents a comprehensive investigation into transportation cost optimization strategies, focusing on the application of Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA), and Capacitated Vehicle Routing Problem (CVRP) in the context of value-based delivery policies. Through a detailed case study approach, this research examines the effectiveness of these optimization techniques in real-world scenarios, considering factors such as delivery time constraints, customer preferences, and cost-efficiency. The study explores how MILP formulations, genetic algorithms, and CVRP methodologies can be tailored and integrated to address the unique challenges faced by companies with value-based delivery policies. By leveraging these advanced optimization tools, businesses can enhance their logistics operations, minimize transportation costs, and improve overall service quality. The findings of this thesis contribute valuable insights and practical recommendations for decision-makers seeking to optimize transportation operations within the framework of value-based delivery policies, thereby fostering sustainable growth and competitiveness in the marketplace