Modelling and Solving a Scheduling Problem with Hazardous Products Dynamic Evolution

In this study, we propose a scheduling problem that stems from technological disasters, characterized by risks and the dynamic evolution of hazardous products. The goal is to clean or neutralize these hazardous products to reduce the overall risk of contamination spreading throughout the environment, affecting inhabitants and agricultural areas. The problem is approached as a Resource-Constrained Project Scheduling with Risk and Product Transformation Dynamics. We present a mathematical formulation and introduce an Iterated Local Search metaheuristic. We fine-tune the metaheuristic parameters using a machine learning package and conduct several numerical experiments to assess performance and gain insights into this innovative application.