Operational Research (OR) is the discipline that applies analytical methods, most of which are mathematical, statistical and computational in nature, to arrive at optimal or near-optimal solutions to complex decision-making problems. OR has strong ties with disciplines such as Mathematics, Business and Management, Industrial Engineering and Computer Science. The growth of the OR field has meant greater specialization and division into subfields (Miser, 2000) which sometime lack distinct boundaries. A topical example here is the emerging research area of Big Data, Business Intelligence and Analytics. They enable introspection of large corpus of data and provide both data-driven and predictive insights for organizational decision-making; this again is a fertile area of enquiry for OR research, as also for Computer Science (e.g., technologies associated with Big Data storage and retrieval), Information Systems (e.g., business intelligence dashboards) and Mathematics and Statistics (e.g., algorithms for data mining, predictive analytics). The fuzzy boundaries of the discipline are further evidenced by the fact that the terms ‘OR’ and ‘MS’ (Management Science) have not been absorbed as one but both exist conjoint and make the well-known OR/MS discipline. The lack of boundaries is a reflection of the interdisciplinary nature of the discipline.

How does one describe the sheer enormity of OR content and how can it be logically structured? One possible approach is through the identification of broad categories of OR techniques, their domain of application (application area) and application context. In most application areas OR can be applied to a multitude of problems with the objective of enabling the stakeholders to make better and more informed decisions; the context of application identifies the specific purpose of using OR techniques. Categorization of OR literature is important because it is arguable that the readers must not only build an understanding of the analytical methods that are discussed in this book, they should in addition possess domain knowledge which is specific to their area of work or study, and further, they should carefully consider the context of application since the choice of particular OR techniques can be largely dictated by specific problems that are to be solved! It therefore follows that this triple lens of technique-application-context will help readers to better appreciate the practical application of OR, and the 26 studies that have been presented in Volumes 1 and 2 of this book have been structured keeping this in mind (see Tables 1.1 and 1.2).

At the most basic level the modeling techniques can be classified as either, (a) Qualitative or ‘Soft OR’, and (b) Quantitative or ‘Hard OR’; (a) Soft OR includes approaches from systems thinking (e.g., Critical Systems Thinking and Theory of Constraints) and problem-structuring methods (e.g., Cognitive Mapping, Collaborative Planning, Qualitative System Dynamics, Soft Systems Methodology, Strategic Choice Approach and SODA), (b) Quantitative modeling techniques can be grouped under a far wider array of sub-categories, for example, Algorithms, Artificial Intelligence, Combinatorial Analysis, Complexity Theory, Data Envelopment Analysis, Forecasting, Fuzzy Logic, Inventory Theory, Multicriteria decision-making, Optimization, Probability, Simulation, Statistics. Specific OR techniques such as Constraint Programming, Bilevel Programming, Integer Programming, Approximate Dynamic Programming, Linear Programming, Simulated Annealing, Tabu Search, Minimum Spanning Trees can all be grouped under the sub-category Optimization. Similarly, Logistic Regression, Structural Equation Modeling, Ranking and Selection, Importance Sampling, Bayesian Statistics, Bootstrapping, Importance Sampling, etc. are specific methods that are related to Statistics. Only two sub-categories (Optimization and Statistics) have been discussed and there are already more than a dozen techniques! The book mostly focuses on the application of ‘Hard OR’ techniques like Mathematical Modeling, Dynamic Programming, Data Mining, Discrete-event Simulation and Quantitative System Dynamics, with only a couple of studies reporting the use of ‘Soft OR’ techniques like Soft Systems Methodology and Qualitative System Dynamics.

It is important to gain an understanding of specific problem scenarios to which OR techniques are being frequently applied. Following the categorization approach presented above, Mustafee and Katsaliaki (n.d) have identified the common application scenarios of OR to include Cost Analysis, Decision-Making, Emergency Response, Maintenance, Marketing, Operations Management, Quality Management, Reliability, Scheduling, Strategy, Facility Planning and Design, Inventory Management, Policy Analysis, Supply Chain Management, etc. These application categories can be further sub-divided into a hierarchy of sub-categories, with the subsequent levels representing a shift from the more general to the more specific! For example, Decision-Making could include the application of OR techniques for Decision Analysis, Cost-Benefit Analysis, Decision Support, Group Decision and Negotiations, Conflict Analysis, etc. The application of OR in the general area Economics could take the form of OR for Econometric Analysis, Actions/Bidding, Experimental Economics, etc. Similarly, OR techniques have been specifically applied for Disruption Management and Evacuation Planning (under Emergency Response) and in the context of Accidents, Epidemiology and Health Screening (under Healthcare Management). The book covers the application of OR for Emergency Planning and includes chapters that have covered specific aspects related to location and routing of emergency services, inventory management, healthcare supply chain management, multi-agency planning, healthcare policy assessment, public health education, etc.

OR has been widely applied in application domains/industries like Agriculture, Audit, Defence, Finance, Education, e-Commence, Manufacturing, Public Service, Sports and Recreation, Service Industries, Transporting, Utilities, Health Services, etc. Examples of specific industries that have benefited from OR include, for example, under the category Transportation – Airlines, Container Terminals, Railways, Shipping, Freight Transportations, etc.; under Utilities – Electricity, Power, Energy; under Finance – Bank/Banking, Financial Institutions, Insurance; under Health Services mainly Hospitals. The book consists of chapters that have applied OR techniques in relation to A&E departments, first responder and ambulance services, mass immunization clinics and emergency response pertaining to public health.

The application of OR methods in healthcare has been widely reported in literature (Brailsford et al., 2009; Jun et al., 1999; Katsaliaki and Mustafee, 2011; Mustafee et al., 2010, 2013; Mustafee and Katsaliaki, 2015; Rais and Viana, 2011). Several studies have used these methods for healthcare emergency planning. The book presents a collection of studies that have applied analytical methods for achieving heightened preparedness, better planning and faster response to A&E and public health emergencies. The book covers four broad themes that are relevant to healthcare emergency planning. These are A&E, ancillary services, outbreak of epidemic and public health emergency response. A&E is core to emergency planning, and the availability of adequate resources is crucial to saving lives. The second theme is that of ancillary services like ambulance service and healthcare supply chain. They support A&E and the wider healthcare operations. Planning for epidemics (the third theme) enables public health institutions to minimize the effect of outbreaks among the population. The final theme is on public health emergency response and it acknowledges the need for multi-agency planning using a qualitative approach to problem-solving. These themes intersect through the various parts and chapters of the book. The structure of Volumes 1 and 2 is discussed next.

Volume 1 consists of 13 studies which are on operational level planning in the context of emergency preparedness and response. These chapters are presented in three well-defined parts of the book, namely, OR for Locating Emergency Services (Part I; Chapters 2–4), OR for Operational Planning in Emergency Services (Part II; Chapters 5–10) and OR for Inventory Management in Emergency Services (Part III; Chapters 11–14). The chapters mainly focus on ‘Hard’ OR techniques. Table 1.1 presents an outline of Volume 1 using the triple lens of technique-domain-context.

Volume 2 of the book is mainly on strategic level planning made possible through the application of OR. It consists of 13 studies that are organized under OR for Assessment and Review of Emergency Services (Part IV; three chapters), OR for Policy Formulation in Emergency Services (Part V, two chapters), OR for Broader Engagement in Planning for Emergency Services (Part VI, four chapters) and Application of OR within the Wider Healthcare Context (Part VII, four chapters). The chapters included in Volume 2 are outlined in Table 1.2; for every chapter (except for Part VII which includes chapters on literature review) the table lists the OR technique, domain of application and context of use.

Following this introductory chapter, the book consists of 13 chapters that are organized into four parts based mainly on the context of application. Part I focuses on the use of OR for Locating Emergency Services. It focuses on location of resources, movement of resources from one place to the other (resource relocation) and resource routing; the specific studies relate to planning for emergency centres, dispatch of ambulances and first responder service. In Chapter 2, Silva and Serra present work on prioritization of calls relating to emergency services. Their objective is to locate emergency centres and to allocate users to these centres in order to maximize the covered population while also considering the varying waiting time limit associated with the different priorities of healthcare services. The authors approach this problem by using location models that incorporate queuing behaviour (queuing maximum coverage location allocation model) and connect this to priority queuing theory. Results from their study show that strategically located emergency centres can avoid congestion of higher priority services. Chapter 3 focuses on ambulance relocation; the objective is to better manage the demand for ambulances, which varies with time, by planning relocation of the existing fleet. Authors Andersson and Värbrand present new algorithms for the ambulance dispatch and the dynamic ambulance relocation problems. Computational tests using a simulation model show that the algorithms are beneficial in reducing the waiting periods for the patients. Chapter 4 presents the work of Henchey et al. which compares the performance of conventional shortest path model and a variance-constrained shortest path model for emergency responders. Comparisons are made under varying conditions of traffic and weather in a ‘smart’ environment that simulates an intelligent transportation system with real-time traffic feeds. The routing methodologies and data captured techniques that are presented in this chapter can help planning for the better and will consequently decrease the response time of the first responder service.