Operations Research and Simulation in Healthcare

✍ Scribed by Malek Masmoudi (editor), Bassem Jarboui (editor), Patrick Siarry (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 241
• Category: Library

✦ Synopsis

This book presents work on healthcare management and engineering using optimization and simulation methods and techniques. Specific topics covered in the contributed chapters include discrete-event simulation, patient admission scheduling, simulation-based emergency department control systems, patient transportation, cost function networks, hospital bed management, and operating theater scheduling.

The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.

✦ Table of Contents

Preface
Need for a Book on the Proposed Topics
Organization of This Book
Audience
Contents
Contributors
Acronyms
1 A Two-Dimensional Categorization Scheme for Simulation/Optimization-Based Decision Support in Hospitals Applied to Overall Bed Management in Interdependent Wards Under Flexibility
1.1 Introduction
1.2 Decision Support in Hospitals with Respect to OverallPlanning
1.2.1 Processes in Hospital Decision Support
1.2.2 Types of Interdependencies in Hospital Processes and Decisions
1.3 Abstraction Levels of Decision Support in Hospitals
1.3.1 First Abstraction: Patient Pathway and Scopeof Planning
1.3.2 Second Abstraction: Resources and Detail Level of Planning
1.4 Two-Dimensional Categorization Scheme
1.4.1 Two-Dimensional Categorization Scheme for Decision Support in Hospitals
1.4.2 Anticipation Issues of Decision Support Problems Within the Two-Dimensional Scheme
1.4.3 Classifying Problem Areas Based on the Categorization Scheme
1.5 Case Studies in Hospital Decision Support: Flexibility Strategies Coping with Interdependencies
1.5.1 Simulation Case Study
1.5.2 Optimization Case Study
1.6 Flexibility Strategies in Hospital-Wide Bed Management
1.6.1 Definition of Flexibility in the Context of Bed Management
1.6.2 Current State of Flexibility
1.6.3 Further Research
1.7 Conclusion
References
2 Heuristics-based on the Hungarian Method for the Patient Admission Scheduling Problem
2.1 Introduction
2.2 PAS Problem Description and Formulation
2.3 Technical Solutions
2.3.1 Hungarian Method to PAS Problem
2.3.2 Heuristic1 for Relaxed PAS Problem
2.3.3 Heuristic2 for PAS Problem
2.4 Experimental Results and Discussions
2.4.1 Benchmark Instances and Setting
2.4.2 Experimental Results
2.4.2.1 Experimental Results on Relaxed PAS Problem
2.4.2.2 Experimental Results on PAS Problem
2.5 Conclusions
Appendix
References
3 A Bi-objective Algorithm for Robust Operating TheatreScheduling
3.1 Introduction
3.2 Uncertainty Modeling
3.2.1 Stochastic and Probabilistic Modeling
3.2.2 Fuzzy Modeling
3.2.3 Scenario Modeling
3.3 Literature Review
3.4 A Bi-objective Algorithm for Robust Scheduling
3.4.1 Problem Definition
3.4.2 MOGA for Robust Operating Theatre Scheduling
3.4.2.1 Uncertainty Modeling
3.4.2.2 Robustness Criteria
3.4.2.3 Description of Our MOGA for Robust Operating Theatre Scheduling
3.5 Computational Experiments
3.5.1 The Test Problem
3.5.2 Computational Results
3.6 Conclusion
References
4 Cost Function Networks to Solve Large Computational Protein Design Problems
4.1 Introduction
4.2 The Computational Protein Design Approach
4.2.1 Exact CPD Methods
4.3 From CPD to CFN
4.3.1 Local Consistency in CFN
4.3.2 Maintaining Dead-End Elimination
4.3.3 Exploiting Tree Decomposition in a Hybrid Best-First Branch-and-Bound Method
4.3.4 A Parallel Variable Neighborhood Search Method Guided by Tree Decomposition
4.4 Integer Linear Programming for the CPD
4.5 Computational Protein Design Instances
4.6 Experimental Results
4.7 Conclusions
References
5 Modeling and Simulation in Dialysis Center of Hedi Chaker Hospital
5.1 Introduction
5.2 Historical Perspectives
5.3 Characteristics of the Renal Unit in HCH
5.4 Simulation Model Development
5.4.1 Description of the Processes Considered to Build the DES Model
5.4.2 DES Model
5.4.3 Software Selection
5.4.4 Model Verification and Validation
5.4.5 Run and Set-Up Parameters
5.5 Results and Analysis
5.6 Conclusion
References
6 Toward a Proactive and Reactive Simulation-Based Emergency Department Control System to Cope with Strain Situations
6.1 Introduction
6.2 Strain Situations in Emergency Department (ED)
6.3 Strain Situations in Emergency Department (ED)
6.3.1 Literature Review on Decision Support Systems in Emergency Department
6.3.2 Decision-Making Process Under Strain Situations
6.4 Proactive/Reactive Control System
6.4.1 Proactive/Reactive Operating Process
6.4.2 Proactive/Reactive Emergency Department Control System
6.5 Functions of Proactive/Reactive ED-CS
6.5.1 F1: Identification of the ED State
6.5.2 F2: Detection of Strain Situation
6.5.3 F3: Forecasting of Strain Situations
6.5.4 F4: Simulation of the ED Behaviors
6.5.5 F5: Data and Knowledge Storage Function
6.5.6 F6: Evaluation and Validation Function
6.6 Case Study
6.7 Experiments and Results
6.7.1 Proactive Control Simulation Results
6.7.2 Results in the Case of Reactive Control
6.7.3 Discussion
6.8 Conclusion and Perspectives
References
7 A Decentralized Approach to the Home Healthcare Problem
7.1 Introduction
7.2 Problematic and Aims
7.3 Literature Review
7.4 Proposed Approach
7.4.1 Mathematical Model
7.4.2 Decentralized Approach
7.4.2.1 Conflicts Management Rule
7.4.2.2 Negotiation Policies
7.4.2.3 POL1 <>
7.4.2.4 POL2 <>
7.4.3 Agents' Types
7.4.3.1 Agent Home Health Care Center
7.4.3.2 Caregiver Agent
7.4.3.3 Patient Agent
7.4.3.4 Agents' Communications
7.5 Computational Results
7.5.1 First Comparison
7.5.2 Second Comparison
7.5.3 Discussions
7.5.4 Example of Execution
7.6 Recommendations for Reuse
7.7 Conclusions and Future Works
References
8 Wounded Transportation and Assignment to Hospital DuringCrisis
8.1 Introduction
8.2 Literature Review
8.3 Problem Formulation
8.3.1 Mathematical Formulation
8.3.2 Equations
8.3.3 Figure
8.4 Problem Solution
8.4.1 Proof of NP-HARDNESS
8.4.2 Resolution Method
8.4.2.1 Greedy Randomized Adaptive Search Procedure (GRASP)
8.4.2.2 Tabu Search (TS)
8.4.2.3 Hybrid Algorithm
8.5 Conclusion
References
9 Carbon Footprints in Emergency Departments: A Simulation-Optimization Analysis
9.1 Introduction
9.2 Green Approaches in the Healthcare
9.3 Emergency Department
9.4 Case Study
9.5 Research Methodology and Simulation Model
9.5.1 Verification and Validation of Simulation Model
9.5.2 Carbon Footprint Calculation
9.5.3 Simulation Outputs of Hospital Performance
9.6 Simulation Optimization
9.6.1 OptQuest
9.6.2 Optimization Model
9.7 Conclusions and Future Insights
References
10 The Effect of Risks on Discrete Event Simulation in Healthcare Systems
10.1 Motivation
10.2 Background
10.3 Characterization, Uncertainty, and Simulation Process of DES Project in Healthcare
10.4 Risks on DES Project in Healthcare
10.4.1 Risk Identification
10.4.2 Risk Assessment
10.5 Case Study
10.6 Conclusion
References