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Mehdi Lamiri

Other affiliations: École Normale Supérieure
Bio: Mehdi Lamiri is an academic researcher from Ecole nationale supérieure des mines de Saint-Étienne. The author has contributed to research in topics: Stochastic programming & Elective surgery. The author has an hindex of 8, co-authored 10 publications receiving 682 citations. Previous affiliations of Mehdi Lamiri include École Normale Supérieure.

Papers
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Journal ArticleDOI
TL;DR: Numerical results show that important gains can be realized by using a stochastic OR planning model and a Monte Carlo optimization method combining Monte Carlo simulation and Mixed Integer Programming is proposed.

348 citations

Journal ArticleDOI
TL;DR: Several heuristic and meta-heuristic methods for elective surgery planning when operating room capacity is shared by elective and emergency surgery are proposed and compared.

154 citations

Journal ArticleDOI
TL;DR: In this paper, a stochastic mathematical programming model and a column generation approach are proposed to solve the elective surgery planning problem for operating rooms shared between elective and emergency patients, which results in both a near-optimal solution and a lower bound to assess the degree of optimality.
Abstract: The elective surgery planning problem for operating rooms shared between elective and emergency patients is addressed. The planning problem consists in determining the set of elective patients to be operated on in each operating room in each period over a planning horizon in order to minimize patient-related costs and the expected operating rooms' utilization costs. A stochastic mathematical programming model and a column generation approach are proposed. The proposed approach results in both a near-optimal solution and a lower bound to assess the degree of optimality. Solutions within 2% of the optimum are obtained in a short computation time for problems of practical sizes with 12 operating rooms and about 210 elective patients.

129 citations

Proceedings ArticleDOI
08 Oct 2007
TL;DR: A solution approach combining Monte Carlo simulation and column generation has been proposed and Computation experiments show that the solution approach results in near-optimal solutions in a reasonable computation time.
Abstract: This paper addresses the elective surgery planning under uncertainties related to surgery times and emergency surgery demands. Surgery times as well operating rooms' capacities used by emergency surgery are assumed to be random variables. The planning problem consists of assigning elective patients to operating rooms (ORs) over a planning horizon in order to minimize patients' related costs and expected ORs' overtime costs. The planning problem is first formulated as a stochastic mathematical program. Then, a solution approach combining Monte Carlo simulation and column generation has been proposed. Computation experiments show that the solution approach results in near-optimal solutions in a reasonable computation time.

44 citations


Cited by
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Journal ArticleDOI
TL;DR: A diversified and detailed overview of recent operational research on operating room planning and scheduling is obtained that facilitates the identification of manuscripts related to the reader's specific interests.

1,099 citations

Journal ArticleDOI
TL;DR: A stochastic optimization approach for the storage and distribution problem of medical supplies to be used for disaster management under a wide variety of possible disaster types and magnitudes and can aid interdisciplinary agencies to both prepare and respond to disasters by considering the risk in an efficient manner.

623 citations

Journal ArticleDOI
TL;DR: The main aim of this paper is to provide a structured literature review on how Operational Research can be applied to the surgical planning and scheduling processes, with particular attention on the published papers that present the most interesting mathematical models and solution approaches developed to address the problems arising in operating theatres.
Abstract: Operating theatre represents one of the most critical and expensive hospital resources since a high percentage of the hospital admissions is due to surgical interventions. The main objectives are to guarantee the optimal utilization of medical resources, the delivery of surgery at the right time, the maximisation of profitability (i.e., patient flow) without incurring additional costs or excessive patient waiting time. The operating theatre management is a process very complex: the use of mathematical and simulation models, and quantitative techniques plays, thus a crucial role. The main aim of this paper is to provide a structured literature review on how Operational Research can be applied to the surgical planning and scheduling processes. A particular attention is on the published papers that present the most interesting mathematical (optimization and simulation) models and solution approaches developed to address the problems arising in operating theatres. Directions for future researches are also highlighted.

480 citations

Journal ArticleDOI
TL;DR: A comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision.
Abstract: We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.

357 citations

Journal ArticleDOI
TL;DR: Numerical results show that important gains can be realized by using a stochastic OR planning model and a Monte Carlo optimization method combining Monte Carlo simulation and Mixed Integer Programming is proposed.

348 citations