Incorporating uncertainty in optimal decision making: Integrating mixed integer programming and simulation to solve combinatorial problems

Acar, Yavuz; Kadipasaoglu, Sükran N.; Day, Jamison M.

doi:10.1016/j.cie.2008.04.003

Cited by 34 publications

(16 citation statements)

References 25 publications

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“…The idea of an iterative simulation-optimization approach for hospital layout planning is adapted from Acar et al (2009) who presented a generic approach to combine mixed integer programming and simulation in order to solve combinatorial problems under uncertainty. Regarding the application of this approach for a hospital layout planning problem, an optimization model for layout planning has to be set up in a first step.…”

Section: An Iterative Simulation-optimization Approach For Hospital Lmentioning

confidence: 99%

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Layout Planning Problems in Health Care

Arnolds

Nickel

2015

International Series in Operations Research &Amp; Management Science

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In general, layout planning problems can be classified as in-house location problems where the aim is to minimize traveling or material handling costs based on distances by deciding on the relative positions of any kind of organizational units inside a building. This class of operations research problems originates from industrial applications, for example, planning the location of different machines of an assembly line needed to manufacture a product or the arrangement of racks and shelves within a warehouse.The special case of layout planning problems in health care has been first introduced by Elshafei in 1977(Elshafei 1977. He modeled a hospital layout problem as a quadratic assignment problem (QAP) and developed heuristics to solve it. In the framework for hospital planning and control the hospital layout planning problem is classified as a resource capacity planning problem on a strategic level . Although it is a long-term decision, the spatial organization within hospitals also directly influences the quality and efficiency of health care and secondary services of the daily routine (Choudhary et al. 2010; Hignett and Lu 2010) as well as patient satisfaction (Chaudhury et al. 2005). In practice, hospital buildings are commonly planned by architects based on experience, design aspects and legal regulations. Instead of that, it is important to develop and follow a holistic approach in order to combine the architectural and legal aspects with logistics, i.e., patient, personnel and material flows inside the future hospital building. In this context, the established operations research methodologies, especially optimization and simulation techniques, can be applied in order to support finding an optimal or robust hospital layout. On the one hand, optimal can mean to minimize traveling costs for I. Arnolds ( ) · S. Nickel Discrete Optimization and Logistics at the IOR,

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Section: An Iterative Simulation-optimization Approach For Hospital Lmentioning

confidence: 99%

“…In order to facilitate a feedback loop between the optimization and DES solutions according to Acar et al (2009), additional parameters, decision variables and constraints have to be introduced in the QAP. Furthermore, the objective function has to be adapted.…”

Section: An Iterative Simulation-optimization Approach For Hospital Lmentioning

confidence: 99%

Layout Planning Problems in Health Care

Arnolds

Nickel

2015

International Series in Operations Research &Amp; Management Science

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“…To overcome these challenges, we propose using a combination of different methods that complement each other. An iterative solution approach for different types of problems that integrate optimization and simulation methodologies have been developed by several researchers in the literature (Acar et al 2009). Here, we make use of the receding horizon control principle together with a linear optimization approach to develop our novel RHMIP model.…”

Section: Methodsmentioning

confidence: 99%

“…More than seven billion tons of goods are carried by ships every year (Acar et al 2009). In Norway, several hundred oil tankers transit each year along its northern coastline (Bye 2012).…”

Section: Introductionmentioning

confidence: 99%

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Assimizele¹,

Oppen²,

Bye³

2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

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Oil tanker traffic constitutes a vital part of the maritime operations in the High North and is associated with considerable risk to the environment. As a consequence, the Norwegian Coastal Administration (NCA) administers a number of vessel traffic services (VTS) centers along the Norwegian coast, one of which is located in the town of Vardø, in the extreme northeast part of Norway. The task of the operators at the VTS center in Vardø is to command a fleet of tug vessels patrolling the northern Norwegian coastline such that the risk of oil tanker drifting accidents is reduced. Currently, these operators do not use computer algorithms or mathematical models to solve this dynamic resource allocation problem but rely on their own knowledge and experience when faced with constantly changing weather and traffic conditions. We therefore propose a novel sustainable model called the receding horizon mixed integer programming (RHMIP) model for optimal dynamic allocation of patrol vessels to oil tankers. The model combines features from model predictive control and linear programming. Simulations run with real-world parameters highlight the performance and quality of our method. The developed RHMIP model can be implemented as an operational decision support tool to the NCA.

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“…Still, most of the studies have found convergence in practice after a few iterations. Acar et al [53] propose a ROSA framework close to SE methods. The authors define an analytical model with an optimistic objective function.…”

Section: Stochastic Programming Deterministic Equivalent (Spde)mentioning

confidence: 99%

Hybrid simulation–optimization methods: A taxonomy and discussion

Figueira¹,

Almada-Lobo²

2014

Simulation Modelling Practice and Theory

217

104

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The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields.

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Incorporating uncertainty in optimal decision making: Integrating mixed integer programming and simulation to solve combinatorial problems

Cited by 34 publications

References 25 publications

Layout Planning Problems in Health Care

Layout Planning Problems in Health Care

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Hybrid simulation–optimization methods: A taxonomy and discussion

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