Habib Chabchoub, Ph.D

Director, MBA Program


Abu Dhabi Campus

+971 2 6133594



Ph.D. Operations and Decision Systems, Laval University, Canada

Master Degree in Management Science, University of Tunis, Tunisia

Bachelor Degree in Mathematics, Faculty of Sciences, University of Tunis, Tunisia

Research Interests

Supply Chain and Logistics Management, Multicriteria Decision Making, Multiple Objective Programming, Applied Operations Research, Meta-Heuristics

Selected Publications

  • Louati A., Son, L-H., Chabchoub, H., Lahiani, H., (2020), “Analysis of Municipal Solid Waste Collection using GIS and Multi-Criteria Decision Aid”, Applied Geomatics, DOI: 10.1007/s12518-019-00291-6, Volume 12, Issue 2, Pages 193-208.
  • Jmal, S., Haddar, B, Chabchoub, H., (2019), “Apply the Quantum Particle Swarm Optimization for the K-Traveling Repairman Problem”, Soft Computing, DOI: 10.1007/s00500-019-03805-x.
  • Louati A., Son, L-H., Chabchoub, H., (2019), “Smart Routing for Municipal Solid Waste Collection: a Heuristic Approach”, Journal of Ambient Intelligence and Humanized Computing, DOI: 10.1007/s112652-018-0778-3, Vol. 778.
  • Louati A., Son, L-H., Chabchoub, H., (2018), “SGA: Spatial GIS-based Genetic Algorithm for Route Optimization of Municipal Solid Waste Collection”, Environmental Science and Pollution Research, DOI: 10.1007/s11356-018-2826-0, Vol. 25, No. 27, pp. 27569-27582.
  • Ayadi, M., Chabchoub, H., and Yassine, A. (2017), “A New Mathematical Formulation for the Static Demand Responsive Transport Problem”, International Journal of Operational Research, Vol. 29, No. 4, (495- 507).
  • Rekik, I., Elkosantini, S., and Chabchoub, H. (2017), “A Case Based Reasoning Based Multi-Agent System for the Reactive Container Stacking in Seaport Terminals”, Procedia Computer Science, Vol. 108 (927–936).
  • Moalla Frikha, H., H. Chabchoub and J.-M. Martel, (2017), “Location of a New Banking Agency in Sfax: A Multi-Criteria Approach”, International Journal of Information and Decision Sciences, Vol. 9, No. 1, (45-76).
  • Espinilla, M., N. Halouani and H. Chabchoub, (2015), “Pure Linguistic PROMETHEE I and II Methods for Heterogeneous MCGDM Problems”, International Journal of Computational Intelligence Systems, Vol. 8, Issue 2, (250-264).
  • Ammar, M.-H., M. Benaissa and H. Chabchoub, (2014), “Seafaring Staff Scheduling”, International Journal of Services and Operations Management, 19(2), (229-249).
  • Dhahri I. and H. Chabchoub, (2007), “Non Linear Goal Programming Models Quantifying the Bullwhip Effect in Supply Chain Based on ARIMA Parameters”, European Journal of Operational Research, Vol. 177, Issue 3, (1800-1810).
  • Chabchoub, H. and J.-M. Martel, (2004), “A Mathematical Programming Procedure for the Choice Problematic”, European Journal of Operational Research, Vol. 153 (2), (297-306).

Teaching Courses

Production and Operations Management (U, G), Supply Chain and Logistics Management (U, G), Quality and Operations Management (G), Modeling and Decision (U), Advanced Topics in Mathematical Programming (G), Quality Management (U), Quantitative Business Analysis (U), Research Methodology (G).


European Operations Management Association, (EurOMA), Belgium.


Smart routing for municipal solid waste collection: a heuristic approach

Published in: Journal of Ambient Intelligence and Humanized Computing

Jul 08, 2019

Louati Son Chabchoub

Municipal solid waste (MSW) is considered as one of the primary factors that contribute greatly to the rising of climate change and global warming affecting sustainable development in many different ways. It is indeed necessary to investigate an efficient computerized method for the optimization of MSW collection that minimizes the environmental and other factors according to a given waste collection scenario. In this paper, we propose a heuristic-based smart routing algorithm for MSW collection and implement it by Python scripts in ArcGIS to calculate optimal solutions of the model including routes and total travelling distances and operational time of vehicles. The algorithm will be validated on a case study of Sfax city which is the second largest and among the most polluted cities in Tunisia. A novel optimization model for the MSW collection in Sfax is designed and given to the algorithm for calculation. The achieved results are then compared with those of the current real scenario as well as evaluated by a multi-criteria decision aid method namely PROMETHEE in terms of environment and economic criteria.


Apply the quantum particle swarm optimization for the K-traveling repairman problem

Published in: Soft Computing

Jun 03, 2019

Jmal Syrine Haddar Boukthir Chabchoub Habib

This paper deals with an optimization problem encountered in the field of transport of goods and services, namely the K-traveling repairman problem (K-TRP). This problem is a generalization of the metric traveling repairman problem (TRP) which is also known as the deliveryman problem and the minimum latency problem. The K-TRP and the related problems can be considered as “customer-centric” routing problems because the objectif function consists in minimize the sum of the waiting times of customers rather than the vehicles travel time. These problems are also considered as problems with “cumulative costs.” In this paper, we propose a quantum particle swarm optimization (QPSO) method to solve the K-TRP. In order to avoid the violations of problem constraints, the proposed approach also incorporates a heuristic repair operator that uses problem-specific knowledge instead of the penalty function technique commonly used for constrained problem. To the best of our knowledge, this study is the first to report on the application of the QPSO method to the K-TRP. Experimental results obtained on sets of the Capacitated Vehicle Routing Problem test instances, of up to 100 customers, available in the literature clearly demonstrate the competitiveness of the proposed method compared to the commercial MIP solver CPLEX 12.5 of IBM-ILOG and the state-of-the-art heuristic methods. The results also demonstrate that the proposed approach was able to reach more optimal solutions and to improve 5 best known solutions in a short and reasonable computation time.