Scheduling Long-Distance Transport Operations under Labor Regulations: A Hybrid Optimization Approach

Abstract

This work solves a case study of a transportation company that aims to plan the maximum number of jobs requested by customers that must be performed over a week in order to minimize the cost of assigning such jobs to vehicles. In this specific problem every customer pays for a job which is composed by a set of different operations (representing loading and unloading), which in turn has associated a location and a time window. Once a job is assigned to a vehicle, all operations must be completed by the same vehicle within the specific time window. We have addressed this problem using two approaches. The first approach addresses the problem globally by formulating its constraints and solving it using the Hexaly solver (a black-box optimizer), while the second approach makes a partition of the problem to make the best decision at each step using a matheuristic. To compare the pros and cons of each approach, different scenarios have been considered.

Cristina Tobar Fernández

Artificial Intelligence PhD Student

Cristina Tobar graduated in Mathematics and Statistics from the University of Seville in 2023. She subsequently completed the Master’s Degree in Analytical Methods for Big Data at Carlos III University in 2024. Her main research interests focus on the hybridization of metaheuristic and exact methods with simulation and Machine Learning techniques to improve the resolution of stochastic optimization problems. Since 2023, she has combined these studies with her role as an Optimization Scientist at OGA.ai. Additionally, since 2024, she has been pursuing a PhD in Artificial Intelligence applied to the stochastic optimization of critical business processes within the framework of an Industrial PhD program.

Jesús Sánchez-Oro

Associate Professor

Associate Professor at the Computer Science Department, being one of the senior researchers of the Group for Research on Algorithms For Optimization GRAFO.