Healthcare is a vast area for research and is one of the trending domains in simulation modelling for decades. Almost all methodologies of simulation have been applied individually, or as a blend, for healthcare system improvements. In this chapter, I am going to share my personal experiences on healthcare simulation modelling projects and particularly focus on the transition between problem definition phase and model development. Scoping, framing, and formulating a problem in healthcare is essential for increasing the usefulness of a simulation model. Although we have options for simulation methods, choosing the right method is a determinant of success. This chapter also includes some tips to make simulation models valid and credible in healthcare domain.

Forty years ago, in 1983, Lee Schruben proposed the Event Graph formalism and modeling language, subsequently defining the paradigm of Event-Based Simulation, in a precise way, which had been pioneered 20 years before by SIMSCRIPT. The purpose of this panel is for a group of Event Graph researchers both from Operations Research and Computer Science, including the inventor of Event Graphs and one of his former PhD students who has made essential contributions to their theory, to discuss their views on the history and potential of Event Graph modeling and simulation. In particular, the adoption of Event Graphs as a discrete process modeling language in Discrete Event Simulation and in Computer Science, and their potential as a foundation for the entire field of Discrete Event Simulation and for the fields of process modeling and AI in Computer Science is debated.

This tutorial aims to introduce Event Graphs (EGs), invented 40 years ago by Lee Schruben to allow eventbased modeling of discrete dynamic systems. Their simplicity and naturalness in causality modelling and simulation modelling made EGs popular in research and practice. In a simulation, an event causes state changes in a system as well as other events to happen in the future. EGs provide a parsimonious diagram representation for the Event Scheduling paradigm of Discrete Event Simulation. We first introduce their visual syntax and informal semantics, and then present a recent extension by adding objects to EGs. Our tutorial also includes an introduction to the formal semantics of EGs and a Python implementation for executing EGs.

The difficulty that hospital management has been experiencing over the past decade in balancing demand and capacity needs is unprecedented in the United Kingdom. Due to a shortage of capacity, hospitals cannot treat all patients. We developed a whole hospital-level decision support system to assess and respond to the needs of local populations. We integrated a comparative forecasting approach and discrete event simulation modelling using Hospital Episode Statistics and local datasets. It is clear from the literature that this level of whole hospital simulation model has never been developed before (an innovative decision support system). First, the demands of all hospital specialties were forecasted, and the forecasts were embedded into the simulation model as input. Secondly, a simulation model was developed to capture the patient pathway of all specialties. The model integrates every component of a hospital to aid with efficient and effective use of scarce resources (e.g., staff and beds). As a result, the hospital can meet the increasing demand with its current resources. According to the scenario analysis, the hospital bed occupancy rate will reach the national target (i.e., 85%), and the total hospital revenue will increase by approximately 13%, with a 10% increase in A&E and outpatient and a 20% increase in inpatient demand. In conclusion, the hospital-level simulation model can become a crucial instrument for decision-makers to provide an efficient service for hospitals in England and other parts of the world.

The increasing pressures on the healthcare system in the UK are well documented. The solution lies in making best use of existing resources (e.g. beds), as additional funding is not available. Increasing demand and capacity shortages are experienced across all specialties and services in hospitals. Modelling at this level of detail is a necessity, as all the services are interconnected, and cannot be assumed to be independent of each other. Our review of the literature revealed two facts; First an entire hospital model is rare, and second, use of multiple OR techniques are applied more frequently in recent years. Hybrid models which combine forecasting, simulation and optimization are becoming more popular. We developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of, (1) forecasting demand for all the specialties, (2) capturing all the uncertainties of patient pathway within a hospital setting using discrete event simulation, and (3) developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from simulation). These results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans, and highlight the benefits of hybrid models.

Abstract Emergency departments (EDs) provide health care services to people in need of urgent care. Their role is remarkable when extraordinary events that affect the public, such as earthquakes, occur. In this paper, we present a hybrid framework to evaluate earthquake preparedness of EDs in cities. Our hybrid framework uses artificial neural networks (ANNs) to estimate number of casualties and discrete event simulation (DES) to analyse the effect of surge in patient demand in EDs, after an earthquake happens. At the core of our framework, Earthquake Time Emergency Department Network Simulation Model (ET-EDNETSIM) resides which can simulate patient movements in a network of multiple and coordinated EDs. With the design of simulation experiments, different resource levels and sharing rules between EDs can be evaluated. We demonstrated our framework in a network of five EDs located in a region of which is estimated to have the highest injury rate after an earthquake in Istanbul, Turkey. Results of our study contributed to the planning for expected earthquake in Istanbul. Simulating a network of EDs extends the individual ED studies in the literature and furthermore, our hybrid framework can help increase earthquake preparedness in cities around the world. On the methodological side, the use of ANN, which is a member of machine learning (ML) algorithms family, in our hybrid framework also shows the close links between ML and DES.

Simulation help achieve the better in the industry in many ways. It reduces the waste in time and resources and increase efficiencyEfficiency in manufacturing. It also helps increase productivityProductivity and the revenue. Simulation has also significant role in the design of products. Furthermore, as the complexity in technology increase, skilled workers required by the industry can be trained by using simulation. Additionally, work safety issues are more important than it was in the past with the emergence of autonomous machinesAutonomous machines in manufacturing. The data will help create smartness and intelligence in manufacturing and simulation help data analyticsAnalytics in comprehension and knowledge extraction. This chapter is the concluding chapter of this book and summarizes the role of simulation in Industry 4.0. There are explicit and implicit imposed roles of simulation which are summarized in terms of technologies composed of Cyber-Physical Systems (CPS)Cyber-Physical Systems (CPS) and smart factorySmart factory . In conclusion, as this book makes it clear with evidences, simulation is at the heart of Industry 4.0 and the main driver of the new industrial revolutionIndustrial revolution .

A man overboard is defined as a situation in which a person falls out at sea from a ship and is in need of rescue. In such cases, a quick and effective search and rescue (SAR) operation are crucial to increase the survival probability of the victim. If there is no visual contact with the victim, i.e. the exact time and location that the crew was overboard, and the drift direction and speed in the region are not certain, then the situation requires conducting a rescue maneuver in an uncertain area that is expanding over time. In this study, we compute the lower and upper bounds on the performance of a search operation in an expanding area for a man overboard situation. After deriving bounds on the cumulative detection probability of the victim, we test the validity of our analytic results by performing Monte Carlo simulations.

Maritime search and rescue (SAR) operations, conducted for rendering aid to the victims in need of help at sea, play a crucial role in dropping the number of causalities. Therefore, it is of high importance to organize SAR operations properly. In this paper, we compose a hybrid methodology which combines optimization and simulation to allocate SAR helicopters. First, we build an integer linear programming (ILP) model to provide an effective deployment plan and use it as an input to a simulation model which includes constraints that the ILP model cannot tackle. Next, using a rule-based algorithm, we generate alternative solutions and seek better plans that exist in the vicinity of the ILP model solution. We perform our methodology on the historical incident data in the Aegean Sea region. Results show that the hybrid methodology we adopted leads to a more effective utilization of resources than the optimization model alone.

Maritime transportation is one of the most significant components of the world’s economy and therefore safer, efficient, and sustainable transportation systems are essential. In the design of these systems, Operational Research/Management Science methods can help decision makers at operational and strategic levels. Simulation is one of the methods in the toolbox with its proven characteristics including scalability, flexibility, and accountability. Although simulation has been used for analyzing maritime transportation systems before, and there are examples in the literature, simulation model building process and models developed are not explicitly published. To fill this gap, and guide model builders in maritime transportation domain, this chapter presents a step by step development of a model which simulates maritime traffic in Bosporus, a narrow and busy strait in Istanbul, Turkey. The model utilizes two open source libraries in Java; OpenMap, a geographical information system, and SimKit, a discrete event simulation library. The model demonstrates the relationships between sea traffic rules, number of pilots, and waiting times. This chapter presents a simple and an extended version of the simulation model. The simple version includes one type of ship arrival, one pilot, and one radar. The extended version is a scaled-up version where these entities are multiplied; two types of ship arrivals, five pilots, and three radars. The models are fully customizable and can be tailored for various purposes. For illustration, the extended version is used to analyse the effects of change in number of pilots and mean of interarrival times to waiting times of ships.

Maritime Search and Rescue (SAR) operation is a critical process which aims to minimize the loss of life, injury, material damage by rendering aid to persons in distress or imminent danger at sea. Response time to incidents is the most crucial performance metric in SAR operations. Fatalities, injuries and damage can be reduced if incidents are responded as quickly as possible. However, uncertainty of location, time and severity of an incident make these operations difficult to conduct and complicated to plan. Furthermore, quick response relates to rational planning and allocation as well as the use of modern and fast SAR resources. In this paper, we combine optimization and simulation methodologies and study the problem of allocating SAR helicopters. Our methodology works in two stages; we first determine an optimal allocation scheme of helicopters with the objective of minimizing the average response time to incidents in the responsibility region. Next, we use a discrete event simulation model to test the performance of our analytical solution under stochastic demand. Using 2014 incident data of the Aegean Sea, we demonstrate a case study in the western sea region of Turkey.

Increasing demand for services in England with limited healthcare budget has put hospitals under immense pressure. Given that almost all National Health Service (NHS) hospitals have severe capacity constraints (beds and staff shortages), a decision support tool (DST) is developed for the management of a major NHS Trust in England. Acute activities are forecasted over a 5-year period broken down by age groups for 10 specialty areas. Our statistical models have produced forecast accuracies in the region of 90%. We then developed a discrete event simulation model capturing individual patient pathways until discharge (in accident and emergency, inpatient and outpatients), where arrivals are based on the forecasted activity outputting key performance metrics over a period of time, for example, future activity, bed occupancy rates, required bed capacity, theatre utilisations for electives and non-electives, clinic utilisations and diagnostic/treatment procedures. The DST allows Trusts to compare key performance metrics for thousands of different scenarios against their existing service (baseline). The power of DST is that hospital decision makers can make better decisions using the simulation model with plausible assumptions that are supported by statistically validated data.

Multistatic sonar networks (MSNs) utilize non-co-located sources and receivers. Although there are many advantages of MSNs, the complex and unusual geometry of their detection regions brings additional analytic challenges, especially in measuring the performance of a multistatic search operation. Furthermore, the challenge becomes harder when mobile sources are included in the network. Previous work has determined a closed form analytic expression for the equivalent sweep width of a MSN that includes a mobile source and stationary receivers, as well as the coverage achieved by parallel sweeps conducted by mobile sources in a field of stationary receivers. These formulae were derived using particular assumptions that are not always met in practice. In this paper, we conduct Monte Carlo simulations to investigate the accuracy of these analytic results under more realistic circumstances.

Military logistics may be defined as the discipline of the planning and subsequent implementation of the movement of military supplies (and forces). Military logistics is a broad spectrum term that is associated with many aspects of military operations. The focus of this paper will be upon medical logistics in the military. In this paper, an analysis of the supply of medical supplies (including pharmaceuticals, equipment and medical personnel) to a field hospital during the time of a military operation has been carried out. The rate at which the supplies arrive at the facility and the rate at which they are used up, thereafter, has been measured and recorded, thereby enabling a model for the existing system to be established. Bottlenecks associated with the system have been identified in the instances when there arose a shortage of these said supplies. Having modelled the existing system, its shortcomings have been identified, thus making it possible proceed with the optimization of the system via the use of a simulation tool that runs on a Cloud environment. The supply rate of medical equipment (namely pharmaceuticals and medical apparatus) and the schedule of the medical personnel deployed in the military base have been optimized.

Maritime piracy has been a major issue for the international community in the last decade and affected the global economy. As a reaction to this issue, international organizations have deployed naval forces to protect maritime transportation in the affected regions. In this study, we present a simulation-based analysis tool to evaluate the effectiveness of operations to prevent piracy. In our model, we consider three stakeholders’ views (pirates, maritime transporters, and naval forces) and modelled their behaviours separately, using Discrete Event Simulation (DES) and Agent-Based Simulation (ABS) approaches. Our hybrid DES and ABS model is used to simulate hypothetical scenarios on the Gulf of Aden for understanding the cause and effect relationship between naval resource allocation and piracy prevention. Our experimental results showed that, first, helicopters are most valuable in prevention and, second, either patrolling or escorting naval ships must carry a helicopter for effective prevention.