Optimization methods for a stochastic surgery planning problem
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.
About: This article is published in International Journal of Production Economics.The article was published on 2009-08-01. It has received 154 citations till now. The article focuses on the topics: Stochastic optimization & Global optimization.
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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
Cites background from "Optimization methods for a stochast..."
...The first mentioned drawback has been overcome in [107], where the convergence properties of the Monte Carlo optimization method have been investigated in depth and, based on these theoretical findings, solution approaches, that allow to handle efficiently problems with realistic size, are developed and tested....
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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
Cites background or methods from "Optimization methods for a stochast..."
...Hence, incorporating knowledge about emergency cases, for example predicted demand, in surgical case scheduling decreases staff overtime and patient waiting time [25, 95, 146, 147, 148]....
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...The latter is preferred in some hospital environments [34, 146]....
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...Methods: computer simulation [8, 25, 61, 64, 66, 67, 70, 83, 141, 143, 146, 147, 205, 224, 247], heuristics [8, 10, 33, 54, 59, 85, 87, 105, 122, 146, 148, 200, 232], Markov processes [95, 107, 171], mathematical programming [10, 32, 33, 38, 52, 53, 54, 85, 86, 87, 105, 128, 146, 147, 148, 163, 186, 187, 200, 212, 223], queueing processes [247] miscellaneous [179], literature review [20, 34, 108, 161, 167]....
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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
TL;DR: A new surgical case scheduling approach is proposed which uses a novel extension of the Job Shop scheduling problem called multi-mode blocking job shop (MMBJS) as a mixed integer linear programming (MILP) problem and the use of the MMBJS model for scheduling elective and add-on cases is discussed.
338 citations
TL;DR: A concrete model that integrates both the nurse and the operating room scheduling process is presented and it is shown how the column generation technique approach can easily cope with this model extension.
191 citations
References
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
Book•
01 Jan 1982
TL;DR: The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering, business, computer science and operations research.
Abstract: From the Publisher:
This second edition of Simulation Modeling and Analysis includes a chapter on "Simulation in Manufacturing Systems" and examples. The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering,business,computer science and operations research.
9,905 citations
Book•
27 Mar 1998
TL;DR: The LDP for Abstract Empirical Measures and applications-The Finite Dimensional Case and Applications of Empirically Measures LDP are presented.
Abstract: LDP for Finite Dimensional Spaces.- Applications-The Finite Dimensional Case.- General Principles.- Sample Path Large Deviations.- The LDP for Abstract Empirical Measures.- Applications of Empirical Measures LDP.
5,578 citations
TL;DR: A Monte Carlo simulation--based approach to stochastic discrete optimization problems, where a random sample is generated and the expected value function is approximated by the corresponding sample average function.
Abstract: In this paper we study a Monte Carlo simulation--based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and the expected value function is approximated by the corresponding sample average function. The obtained sample average optimization problem is solved, and the procedure is repeated several times until a stopping criterion is satisfied. We discuss convergence rates, stopping rules, and computational complexity of this procedure and present a numerical example for the stochastic knapsack problem.
1,728 citations
Book•
17 Sep 2004
TL;DR: This prologue explains the background to SLS, and some examples of applications can be found in SAT and Constraint Satisfaction, as well as some of the algorithms used to solve these problems.
Abstract: Prologue Part I Foundations 1 Introduction 2 SLS Methods 3 Generalised Local Search Machines 4 Empirical Analysis of SLS Algorithms 5 Search Space Structure and SLS Performance Part II Applications 6 SAT and Constraint Satisfaction 7 MAX-SAT and MAX-CSP 8 Travelling Salesman Problems 9 Scheduling Problems 10 Other Combinatorial Problems Epilogue Glossary
1,500 citations