A Generalized Decomposition Algorithm for Real-Time Truck Routing Problems

Abstract

This paper is based on a practical project jointly conducted by a major trucking company and a renowned operations research consulting firm. It studies a large-scale, real-time truckload pickup and delivery problem. A number of cost factors are carefully measured such as loaded/empty travel distance, travel time, crew labor, equipment rental or operational cost, and revenue for completing the movements. This paper proposes a generalized decomposition algorithm that is capable of considering sophisticated business rules. The goal is to recommend executable and efficient truck routing decisions to minimize operating costs. Numerical tests are conducted with operational data from J.B.HUNT. A fleet of 5,000 trucks is considered in this experiment. The test result not only shows significant cost savings but also demonstrates computational efficiency for real-time application.