Michel Nakhla

Biography

Michel Nakhla is a researcher whose work lies at the intersection of management science, organizational engineering, and public service regulation. His research focuses primarily on the implementation of decision-support models in organizations, with an emphasis on their organizational validity and their impact on managerial dynamics. In particular, he explores methods inspired by lean management and their adaptation to the healthcare sector (hospitals, emergency services) and the public services sector (water, regulation of public-private partnerships). His analyses draw on theoretical frameworks such as incomplete contract theory, performance indicators (E-OEE), and institutional reforms, while incorporating operational and strategic dimensions. His work highlights the tensions between economic efficiency, contractual flexibility, and social issues, particularly in contexts marked by uncertainty and the interdependence of stakeholders.

Publication(s)

2022
Management research viewed from the perspective of decision support modeling. "From management engineering to management sciences"; [La recherche en gestion vue sous l'angle de la modélisation d'aide à la décision « De l'ingénierie de gestion aux sciences de gestion»] DOI : 10.3917/resg.151.0223
2018
Designing extended overall equipment effectiveness: application in healthcare operations DOI : 10.1080/17509653.2017.1373377
2016
Patients flow optimization in ED: An operational research on the impacts of physician triage DOI : 10.1109/IESM.2015.7380220
2016
Innovative regulations, incomplete contracts and ownership structure in the water utilities DOI : 10.1007/s10657-015-9480-5
2012
Emergence of an innovative regulation mode in water utilities in France: Between commission regulation and franchise bidding DOI : 10.1007/s10657-010-9169-8
2010
Designing physician's financial incentive for increased activity: Paying for performance; [Conception de mécanismes de rémunération variable des médecins et incitation au développement de l'activité] DOI : 10.3917/jgem.103.0127
2003
Performance indicators: A key evolution in the management and regulation of water and sewerage services; [Les indicateurs de performance: une évolution clef dans la gestion et la régulation des services d'eau et d'assainissement] DOI : 10.3917/flux.052.0055
2003
Information, coordination and contractual relations in firms DOI : 10.1016/S0144-8188(03)00016-4
1996
Internal contracts, cost engineering, and organizational problems
1995
Production control in the food processing industry: The need for flexibility in operations scheduling DOI : 10.1108/01443579510094107

Teaching

Operations Research

Course Director

1. Graphs and Algorithms Path Problems (Algorithms and Theorems: cases l(u) and l(u), Moore-Dijkstra, Moore, Bellman, Ford, matrix algorithms, Floyd, Dantzig…). Trees and Tree Structures (B&B) (Kruskal’s algorithm, Prim’s algorithm, etc.) Flows and Transport Networks (flows and connectivity, minimum-cost flows, Klein’s algorithm, non-conforming arc methods (primal-dual) …) Matching and assignment (Minty’s lemma, Ford’s algorithm with lower bounds on capacities…). Eulerian and Hamiltonian paths. Enumeration algorithms using separation-evaluation and constraint propagation. Application: Project planning and scheduling problem Applications: TSP, matching problem, assignment problem, facility location problems, bin packing. 2. Linear Programming Definitions and fundamental results (convexity and polyhedra, feasible bases, characterization of optimal bases…). Solving linear programming problems (the primal simplex algorithm and its forms for maximization and minimization, basis change matrices, canonical form, and the simplex table…). The penalty method (the “Big M” method). The revised simplex method. General definition of duality. Primal/Dual relationship (theorems and properties of duality, strong and weak duality, dual variables and marginal costs, Farkas–Minkowski theorem, existence of non-negative solutions). “Transportation simplex” algorithm. Post-optimal analysis, sensitivity analysis. Application: Mixing and assembly problems, capital budgeting. Application: Transportation problem. 3. Dynamic Programming and Queuing Discrete-time dynamic programming, finite and infinite horizons. Stochastic dynamic programming. Queuing theory: M/M/1 and M/M/S models, Little’s law. Applications: Inventory management, sequential investment problems. 4. Introduction to Approximation Algorithms and Metaheuristics Iterative neighborhood algorithms (iterative neighborhood algorithm, simulated annealing metaheuristics, tabu search metaheuristics, greedy algorithms, random greedy algorithms, etc.) Biologically inspired metaheuristics Optimality of approximate algorithms Application: TSP problem

Design Engineering (DE) Option

Guest Lecturer

National and International Context The program collaborates with leading international academic institutions in its field (Chalmers, Stanford, Carnegie Mellon, Imperial College, RWTH Aachen, Delft, etc.) and with France’s top design schools (Strate College, École Nationale Supérieure de Création Industrielle, ENSAD). Compared to the curricula of these leading international scientific institutions, the program allows students to combine courses in “Engineering Design,” “Project Management,” “Innovation Management,” and “Industrial Design” in a unique way. Career Prospects and Opportunities Graduates of this track go on to work in a wide variety of sectors (automotive, aerospace, high tech, luxury goods, services, innovation or intellectual property consulting, healthcare, energy, retail, etc.), including the creative industries. With the expansion of innovation departments in many large corporations, several students have quickly risen to high-level innovation leadership roles (Schneider, Thales, RATP, SNCF, Urgo, Airbus, etc.). Some representative elective topics covered in recent years: The elective project runs from October through June. Topics are carefully selected from a wide range of sectors. They fall into two main categories: • Type 1: Students participate in the development of a range of new products or systems and implement new design approaches. Some examples: Seb / startup incubator: development of devices to combat mosquitoes and vector-borne diseases Soft@Home / Orange: leveraging data from home internet routers Urgo Médical: development of connected devices in healthcare Decathlon: the “freshness” of sportswear: methodology for exploring and structuring a new value proposition. • Type 2: Students participate in the development of design methods. Some examples: Airbus: methodology to support cross-sector technology transfer; innovation support tool for the “design-oriented factory” Thales Avionics: From Operational Need to Innovative Design: The Case of Helmet-Mounted Displays for Helicopter Pilots. SNCF / Zeebra (startup): Development of digital tools and services for coordinating innovation experts within the company. Throughout this work, students receive significant support from the course’s faculty. This is a key educational experience during which students can consolidate their knowledge and gain initial professional experience on a topic that addresses real-world business challenges.

PhD supervision

2023 Smart predictive maintenance models for renewable production assets (wind & solar) BOUILLOUD Marie
2023 Optimization of mineral processing under uncertainties - machine learning approaches GUERIN Nicolas
2022 The management of artificial intelligence projects by the state BARRÉ Antoine
2019 Public policy instruments in ecological transition and systemic change. Case of industrial and territorial symbioses EROHINA Daniela
2017 Smart Grids: Optimal decentralized control and parameterized regulation EL HARRAB Mohamed Saâd

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Team