Exact and metaheuristic approaches for a real-world discrete lot-sizing and scheduling problem in the automotive industry

Abstract

We address a production scheduling problem arising in the seat manufacturing auto industry, characterised by multiple racetracks and a wide variety of car models. The challenge is to determine the optimal sequence of molds to mount on each racetrack to maintain inventory levels within target ranges while minimising changeovers. To tackle this, we formulate a mixed-integer programming model evaluated using Gurobi. Additionally, we develop a Greedy Randomised Adaptive Search Procedure (GRASP) to provide a practical, non-commercial alternative. The GRASP incorporates two specialised local search procedures: the first achieves feasibility by reducing shortages, and the second aims to minimise changeovers. Computational experiments on 133 real-world instances demonstrate the merit of our approaches. The exact MIP solver proves optimality in under 30 seconds on average. Given the same 30-second budget, GRASP achieves a 2.42% average deviation, which is equivalent to fewer than a single additional changeover, finding 77% of global optima. Extending the runtime to the 5-minute company limit reduces deviation to 1.65%. Most importantly, compared to the historical manual baseline, this approach reduces tool changeovers by nearly 50%, resulting in estimated annual savings of $250,000 per plant.

Sergio Cavero

Phd in Artificial Intelligence

Sergio Cavero was born Madrid (Spain) on September 24, 1997. He graduated in Software Engineering from Universidad Politécnica de Madrid in 2019. During his undergraduate studies he made a stay at the University of Bradford (UK). In addition, he was awarded twice with the ‘Beca de Excelencia of the Comunidad de Madrid, and also awarded for the Best Final Degree Project. Later, he completed a Master’s Degree in Artificial Intelligence at the same university (UPM) obtaining awards for Best Academic Record (‘Premio José Cuena’) and Best Master’s Thesis. He academic results lend him be beneficiary of one of the ‘Ayudas Para la Formación de Profesorado Universitario (FPU)’, funded by the Spanish Government. He is currently carrying out his doctoral thesis at the Universidad Rey Juan Carlos, supervised by Professors Abraham Duarte and Eduardo G. Pardo. His main research interests focus on the interface among Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristics procedures for optimization problems modeled by graphs.

Isaac Lozano-Osorio

Phd in Artificial Intelligence

Isaac Lozano graduated with a double degree in Computer Engineering and Computer Engineering from the Universidad Rey Juan Carlos, where he was awarded the prize for the Best Final Project. Subsequently, he completed a Master in Artificial Intelligence Research (UIMP). His main research interests are focused on the interface between Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristic procedures for graph modeled optimization problems.