An exploration of quantitative models and algorithms for vehicle routing optimization and traveling salesman problems

Abstract

This study presents optimization models for large vehicle routing problems using a spreadsheet solver and Python programming language with extended graphic card boosting computing power. Near optimality is feasible and attainable with spreadsheet tools and models for solving real-life problems. However, increasing the availability of additional computing power through graphics processing and visualization is now a viable option for decision-makers and problem-solvers. This study shows that decision-makers can solve vehicle routing optimization problems with limited access to high-end optimization tools. This study shows managers and decision-makers can use vehicle routing optimization even with limited access to sophisticated optimization tools.

Keywords

• Vehicle Route Optimization
• Traveling Salesman Problem
• Branch and Bound
• Logistics
• Parallel Computing
• Graphics Processing Unit