Service Quality Improvement in the Operating Rooms Using Optimization, Problematic Solved by a Discrete Particle Swarm and Queues Simulation Approaches

Firdaous Bennis(1*), Mustapha Amghar(2), Sbiti Nawal(3), Abdelmajid Elouadi(4)

(1) The Mohammadia School of engineering, Mohammed V University Agdal, Rabat, Morocco
(2) The Mohammadia School of engineering, Mohammed V University Agdal, Rabat,
(3) The Mohammadia School of engineering, Mohammed V University Agdal, Rabat,
(4) Ibn Sina Hospital, Rabat, Morocco
(*) Corresponding author

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


The good organization of the operating rooms is the key factor to improvepatient satisfaction. The deployedoptimization methods are implemented to guarantee the operating rooms management and ensure crucial productivity increase while ensuring the quality and safety of patient care. We study in this article a day scheduling problem concerning spreading surgeries in the operating rooms. We propose a new production model akin to a manufacturer of hybrid flow shop hierarchy.
Concerning the operating time data that we use in the scheduling algorithm, we model the operating time of surgical intervention using fuzzy theory and we solve the scheduling problem by exposing a new approach based on the travelling salesman problem approach and the discrete particle swarm optimization algorithm developed in C++. We develop a simulation in C + + using the management of queues to allocate different actors and necessary means which both should be available for the unfolding of this optimum scheduling and to measure the maximum of patients that can be accepted a day.

Copyright © 2014 Praise Worthy Prize - All rights reserved.


Discrete Particle Swarm optimization; Fuzzy theory; travelling salesman problem; Simulation

Full Text:



Chieh-Sen Huang, A., Yi-Chen Huang, A. and Peng-Jen Lai, B. (2012). Modified genetic algorithms for solving fuzzy flow shop scheduling problems and their implementation with CUDA, Expert Systems with Applications 39 4999–5005,

Ciganek, J., Noge, F., Kozak, S., Modeling and control of mechatronic systems using fuzzy logic, (2014) International Review of Automatic Control (IREACO), 7 (1), pp. 45-51.

M. N. Lakhoua, Using the Fuzzy Logic to Control the Quality of a Product: Case Study of a Grading System of Cereals, (2009) International Review of Automatic Control (IREACO), 2 (1), pp. 115-120.

Ya Liu, A.B., Chengbin Chu, B. and KanliangWangd, C. (2011) A new heuristic algorithm for the operating room scheduling problem,Computers & Industrial Engineering 61 865–871.

Fei, H., Chu, C., Meskens, N. and Artiba, &A., Solving surgical cases assignment problem by a branch-and price approach, International Journal of Production Economics, 112(1), 96–108.

Fei, H., Meskens, N. and Chu, & C., A planning and scheduling problem for an operating theatre using an open scheduling strategy, Computers and Industrial Engineering, 58(2), 221–230.

Topaloglu, S. and Selim, H. (2010) Nurse scheduling using fuzzy modeling approach, Fuzzy Sets and Systems 161 1543–1563,

HuaKe, A. and Baoding Liu, B. (2010) Fuzzy project scheduling problem and its hybrid intelligent alogorithm, Applied Mathematical Modeling 34 301–308.

B Changsheng Zhang, A., JiaxuNing, C. and DantongOuyang, B. (2010) A hybrid alternate two phases particle swarm optimization algorithm for flow shop scheduling problem, Computers & Industrial Engineering 58 1–11.

Deming, L. (2008) A Pareto archive particle swarm optimization for multi-objective job shop scheduling, Computers & Industrial Engineering 54 960–971.

JeroenBelie, N. and Demeulemeester, E. (2006) Discrete Optimization Scheduling trainees at a hospital department using a branch-and-price approach, European Journal of Operational Research 175 258–278.

Widmer, M. and Hertz, A. (1989)A new heuristic method for the flow shop sequencing problem. European Journal of Operational Research, 41:186–193.

RINNOOY KAN, H.G. (1976) Machine scheduling problems: classification, complexity and computations, Nijhoff, The Hague.

Quéré, A.( 1998) Gestion et optimisation des flux matières dans les systèmes de production de type flow-shop hybride . Doctorat d'informatique, Université Blaise Pascal, Clermont-Ferrand II.

Zadeh, L.A. (1965) Fuzzy sets. Information and control;8:333–53.

Meunier, B., La logique floue et ses applications, Addison-Wesley France, Paris, 257 p.

Chang, T.M. and Yih, J.H. (1999) Constructing a fuzzy rule system from examples, Integrated Computer-Aided Engineering, 6 (3), 213-222.

Dubois, D. and Henri, P. (1987) The mean value of a fuzzy number, Fuzzy Sets Syst. 24 (3), 279–300.

Shu-Jen, C. and Ching-Lai, H., Fuzzy Multiple Attribute Decision Making: Methods and Applications, Fuzzy Sets,1992.

Eberhart, R.C. and Kennedy, J. (1995) A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micromachine and Human Science, Nagoya, Japan. p. 39-43.

Pierreval, H. and Mebarki, N.( 1997) Dynamic selection of dispatching rules for manufacturing systems scheduling, International Journal of Production Research, vol.35, n°6 , pp. 1575-1591.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2021 Praise Worthy Prize