Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 8, No. 1, 2017 23 Copyright © 2017Inderscience Enterprises Ltd. Genetic algorithm hybridised by a guided local search to solve the emergency coverage problem Meryam Benabdouallah* Center of Sciences and Engineer Technologies, Normal Superior School of Technical Education/Ecole Normale, Supérieure de l’Enseignement Technique (ENSET), Mohamed V University, Rabat, 10100, Morocco Email: meryam_benabdouallah@um5.ac.ma *Corresponding author Othmane El Yaakoubi Mathematics Department, Ecole Supérieure des Sciences et Technologies de l’Ingénieur (ESSTI), Rabat, 10090, Morocco Email: Othmane.elyaakoubi@essti.ac.ma Chakib Bojji Center of Sciences and Engineer Technologies, Normal Superior School of Technical Education/Ecole Normale, Supérieure de l’Enseignement Technique (ENSET), Mohamed V University, Rabat, 10100, Morocco Email: c.bojji@um5s.net.ma Abstract: The management of emergency logistics is addressed by several researchers. This paper addresses the ambulance allocation in order to cover sectors in the Rabat region of Morocco. Our model takes into account the dynamic and stochastic nature of emergency calls arrival. This work proposes a mathematical model of the coverage problem, resolved using a genetic algorithm (GA) initialised by a heuristic and hybridised by a guided local search (GLS). We consider 12 emergency locations; seven hospitals of the region and five fire stations. These algorithms are approved comparing to the optimal solutions done by Cplex software. As a result, the GA hybridised by a GLS provides a distribution of ambulances in each potential waiting site (hospital or fire station), and minimises the total lateness of emergency intervention. Keywords: coverage model; emergency; genetic algorithm; hospital logistics; local search; simulation. Reference to this paper should be made as follows: Benabdouallah, M., El Yaakoubi, O. and Bojji, C. (2017) ‘Genetic algorithm hybridised by a guided local search to solve the emergency coverage problem’, Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 8, No. 1, pp.23–41.