Tillal Eldabi

Simulation in manufacturing and business: A review

This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems.

Simulation modelling in healthcare: reviewing legacies and investigating futures

This article proposes considered futures for the use of simulation as a problem solving technique within healthcare settings. Using a synthesis of trends identified by a selection of experts in the field, academics and industrialists, critical analysis was applied to find the differences between what exists and what could be created based on outlining some major themes. The survey data reveals that most respondents agree on following whole system approaches with more joined up modelling or mixed methods to tackle problems rather than single-solution-based practices. The article then presents options for how simulation could be used within the healthcare domain. Such options or futures could assist in identifying the critical barriers towards having a successful strategy and provide the basis for debate that will be necessary to attain it.

Quantitative and qualitative decision‐making methods in simulation modelling

Discrete event simulation (DES) is widely known to be a quantitative research tool. A simulation modelling process is mainly based on feeding quantitative data into a model to produce quantitative results in a structured sequential process. Qualitative approaches to research take a less structured approach with more of an inclination towards judgmental and expert knowledge rather than hard data. In this paper the authors suggest that DES can be employed as both a qualitative and quantitative research tool. The paper demonstrates how simulation may represent both stances either separately or combined. This is based on the fact that the basic objectives of simulation are either for understanding – which needs a qualitative perspective – or performance measurement – which a needs quantitative perspective. Traditional quantitative and qualitative methods are discussed showing how DES might cope with the weaknesses of both stances. A structure for using DES as a combined research methodology is proposed.

Applicability of hybrid simulation to different modes of governance in UK healthcare

Healthcare organizations exhibit both detailed and dynamic complexity. Effective and sustainable decision-making in healthcare requires tools that can comprehend this complexity. Discrete event simulation (DES) due to its ability to capture detail complexity is widely used for operational decision making. However at the strategic level, System Dynamics (SD) with its focus on a holistic perspective and its ability to comprehend dynamic complexity has advantages over DES. Appreciating the complexity of healthcare, the authors have proposed the use of hybrid simulation in healthcare. As argued previously, effective decision making require tools which are capable of comprehending both detail and dynamic interactions of healthcare. The interactions in the organizations are governed by the governance design. In appreciation of that argument the authors have described the applicability of a hybrid approach to various modes of governance in UK healthcare.

A Simulation Modelling Approach to Evaluating Alternative Policies for the Management of the Waiting List for Liver Transplantation

A shortage of donor liver grafts unfortunately results in approximately 10% of patients dying whilst listed for a liver transplant in Europe and the United States. Thus it is imperative that all available organs are used as efficiently as possible. This paper reports upon the application of a simulation modelling approach to assess the impact of several alternative allocation policies upon the cost effectiveness of this technology at one liver transplant centre in the UK. The impact of changes in allocation criteria on the estimated net life expectancy, average net costs and overall cost effectiveness of the transplantation programme were evaluated. The incremental cost effectiveness ratio (ICER) for the base case allocation policy, based upon the time spent on the waiting list (i.e., longest wait first) was £11,557 at 1999 prices. The ICERs associated with an allocation policy based upon age (lowest age first), and an allocation policy based upon the severity of the pre-transplant condition of the patient (with most severely ill patients given a lower priority) were lower than the base case at £10,424 and £9077, respectively. The results of this modelling study suggest that the overall cost effectiveness of the liver transplantation programme could be improved if the current allocation policy were modified to give more weight to the age of the patient and the reduced chances of success of the most severely ill patients.

Simulation in health-care: lessons from other sectors

This paper compares the results of broad-range literature reviews over the period 1997–2006 on the application of simulation in three sectors: commerce, defence, and health-care. The main purpose of these reviews is to see whether there are lessons to learn from the first two sectors to be applied in the health-care sector. Results of our reviews show that, simulation has been commonly used in the three sectors; however, there is clear evidence of lack of consistency in the level of implementation. More specifically, healthcare lags behind the other two sectors, particularly in terms of stakeholder engagement and, consequently, in terms of implementation of simulation outcomes. This paper provides key insights into some challenges in applying simulation methods in healthcare, whilst presenting an up-to-date “big picture” of simulation in three main sectors from an academic point of view.

A proposed approach for modelling health‐care systems for understanding

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Simulation in healthcare management: a soft approach (MAPIU)

Abstract Simulation application in healthcare is not as widely perceived for problem solving as it is in other application areas. This might be due to the way simulation is applied in healthcare modelling, as it follows a traditionally based on engineering approach. This may not be a problem in itself; however, healthcare systems are often complex in that they involve multiple decision-makers and thus understanding and communication between the various stakeholders is potentially problematic. Problem understanding and efficient communication means can widely contribute to the solution; consequently a modelling approach is proposed in this paper to enhance such understanding and communication. The approach is based on participation of stakeholders; it is also iterative. An example is given aiming to show how this approach has been used to successfully facilitate the modelling process, concluding that involving stakeholders throughout enables them to fully appreciate the findings. This approach thus serves to enrich the communication process.

Hybrid simulation for modelling large systems: an example of integrated care model

Developing models for large systems is not a trivial task. Using only Discrete Event Simulation (DES) as a modelling technique may mean that the complexity of the underlying model will increase exponentially with the size of the model. An alternative to this is the use of System Dynamics (SD) for modeling large systems using the positive and negative feedback loops. However, for modelling a human-centric system like healthcare, DES is important as it provides individuality analysis; similarly, SD is important as it facilitates the whole systems approach. The combined application of OR/Simulation methods enable the symbiotic realization of the strengths of individual techniques, while reducing their limitations; in this paper it is suggested that a combined SD-DES approach (also referred to as hybrid technique) can be effectively used for modelling large systems. The example being used in this context is the modelling of an Integrated Care (IC) system in healthcare.

Simulation modelling is 50! Do we need a reality check?

Simulation modelling is a fascinating research field. The techniques and tools of simulation modelling have been used to research and investigate the behaviour of various systems in a wide range of areas such as commerce, computer networks, defence, health, manufacturing and transportation. Indeed, the study of the use of these techniques and tools, and the development of new forms of these, are a rich source of research in their own right. Simulation modelling is about to reach the 50th anniversary of the development of GSP (General Simulation Program), the first simulation modelling language (Tocher and Owen, 1960). There have been several historical accounts of simulation modelling research. To complement these, we have performed a review of the recent history of simulation modelling. This study targeted three leading journals dedicated to this field. These are the ACM Transactions of Modeling and Computer Simulation, Simulation: Transactions of The Society for Modeling and Simulation International and Simulation Modelling Practice and Theory (formerly Simulation Practice and Theory). The study covered the first 6 years of this century (2000–2005) and included 576 papers. The key observation of this work was the relative lack of ‘real world’ involvement in simulation modelling research and an even greater lack of evidence of ‘real world’ benefit, arguably very alarming outcomes for an applied field. To further investigate this observation two additional surveys were carried out, one to study if real world papers appeared in the more widely known OR/MS literature (837 papers in 12 journals) and one to study if such papers appeared in Manufacturing and Logistics, an application area closely associated with simulation modelling (1077 papers in 10 journals). The results of these surveys confirmed our observations. We ask if this is the natural evolution of a field that has existed for half a century or an indication of a worrying problem? This paper reports on our findings and discusses whether or not simulation modelling research urgently needs to face a ‘reality check.’