List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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Evolutionary algorithms for solving multi-objective problems

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

BACKGROUND Hospitals have difficulty justifying the expense of maintaining trauma centers without strong evidence of their effectiveness. To address this gap, we examined differences in mortality between level 1 trauma centers and hospitals without a trauma center (non-trauma centers). METHODS Mortality outcomes were compared among patients treated in 18 hospitals with a level 1 trauma center and 51 hospitals non-trauma centers located in 14 states. Patients 18 to 84 years old with a moderate-to-severe injury were eligible. Complete data were obtained for 1104 patients who died in the hospital and 4087 patients who were discharged alive. We used propensity-score weighting to adjust for observable differences between patients treated at trauma centers and those treated at non-trauma centers. RESULTS After adjustment for differences in the case mix, the in-hospital mortality rate was significantly lower at trauma centers than at non-trauma centers (7.6 percent vs. 9.5 percent; relative risk, 0.80; 95 percent confidence interval, 0.66 to 0.98), as was the one-year mortality rate (10.4 percent vs. 13.8 percent; relative risk, 0.75; 95 percent confidence interval, 0.60 to 0.95). The effects of treatment at a trauma center varied according to the severity of injury, with evidence to suggest that differences in mortality rates were primarily confined to patients with more severe injuries. CONCLUSIONS Our findings show that the risk of death is significantly lower when care is provided in a trauma center than in a non-trauma center and argue for continued efforts at regionalization.

https://www.nejm.org/doi/pdf/10.1056/NEJMsa052049

A National Evaluation of the Effect of Trauma-Center Care on Mortality

The belief that mathematical location modeling can identify "optimal" location patterns rests on the basis that some realistic objective can be identified and by some measure quantified. For example, in the area of private facilities location analysis, a reasonably accurate statement of the objective of locating warehouses is to minimize the costs of manufacturing and distribution. Since most cost elements included in the objectives of private facility location can be reasonably estimated, the models can picture with some degree of accuracy the real location problem they are designed to solve. Unlike private facility location analysis, the objectives of public facility location analysis are more difficult to embrace and to quantify. The difficulty in defining direct measures of public objectives has resulted in a search for some surrogate measure with which the decision maker may be comfortable. Two different surrogate measures which have received attention in location models are: (1) total weighted distance or time for travel to the facilities, and (2) the distance or time that the user most distant from a facility would have to travel to reach that facility, that is, the maximal service distance.* The use of a maximal service distance as a measure of the value of a given locational configuration has been discussed at length by Toregas and ReVelle 1 who show that it is an important surrogate measurement for the value of a given locational configuration. For a given location solution, the maximum distance which any user would have to travel to reach a facility would reflect the worst possible performance of the system. In the regional location of emergency facilities such as fire stations or ambulance dispatching stations, the concept of the maximal service distance is well established. ~,3 The maximal service distance concept appears in the Location Set Covering

The maximal covering location problem

Variable neighborhood search: Principles and applications

This paper views the location of emergency facilities as a set covering problem with equal costs in the objective The sets are composed of the potential facility points within a specified time or 

The location of emergency service facilities

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1970.tb00142.x

Central Facilities Location

Location analysis: A synthesis and survey

ContextPrevious studies have reported that the number and distribution of trauma
centers are uneven across states, suggesting large differences in access to
trauma center care.ObjectiveTo estimate the proportion of US residents having access to trauma centers
within 45 and 60 minutes.Design and SettingCross-sectional study using data from 2 national databases as part of
the Trauma Resource Allocation Model for Ambulances and Hospitals (TRAMAH)
project. Trauma centers, base helipads, and block group population were counted
for all 50 states and the District of Columbia as of January 2005.Main Outcome MeasuresPercentages of national, regional, and state populations having access
to all 703 level I, II, and III trauma centers in the United States by either
ground ambulance or helicopter within 45 and 60 minutes.ResultsAn estimated 69.2% and 84.1% of all US residents had access to a level
I or II trauma center within 45 and 60 minutes, respectively. The 46.7 million
Americans who had no access within an hour lived mostly in rural areas, whereas
the 42.8 million Americans who had access to 20 or more level I or II trauma
centers within an hour lived mostly in urban areas. Within 45 and 60 minutes,
respectively, 26.7% and 27.7% of US residents had access to level I or II
trauma centers by helicopter only and 1.9% and 3.1% of US residents had access
to level I or II centers only from trauma centers or base helipads outside
their home states.ConclusionSelecting trauma centers based on geographic need, appropriately locating
medical helicopter bases, and establishing formal agreements for sharing trauma
care resources across states should be considered to improve access to trauma
care in the United States.

https://www.lifeflightmaine.org/Documents/EMS-and-Hospitals/Research/Access-to-Care/Access-to-Trauma-Centers-06_2005.aspx

Access to trauma centers in the United States.

Backup coverage, the second coverage of a demand node, is suggested as a decision criterion in modelling the location of emergency services on a network. The efficient handling of stochastic demand by vehicles which can respond to only one call at a time may require backup coverage in areas of high demand as a means to maintain a more uniform level of service. This new criterion is applied in the context of the classic covering models, the Location Set Covering Problem and the Maximal Covering Location Problem. First coverage as defined in these models is traded off against backup coverage in the present work. Other efforts which incorporate additional levels of coverage are reviewed. We also show how to extend these models to third as well as subsequent coverage. Based on an example problem we show that significant levels of backup coverage may possibly be provided within a system without substantial loss of first coverage.

Charles ReVelle

Papers

The location of emergency service facilities

Central Facilities Location

Location analysis: A synthesis and survey

Access to trauma centers in the United States.

Concepts and applications of backup coverage