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The purpose of this paper is to consider a number of issues around the poor adoption of healthcare simulation models and reflect whether there has been a broad failure of academic healthcare simulation modellers to build models that reflect real healthcare problems as acknowledged by healthcare stakeholders. This paper will also review the role of healthcare planners within the health sector and propose that they are well suited to act as change agents to improve the adoption of simulation within the sector.

This paper reviewed academic evidence around poor adoption of simulation modelling in healthcare, including differences to other sectors, its size and complexity, stakeholder issues and current and future challenges to improve operational efficiency. This paper also reviewed the role of healthcare planning and its valuable links with health stakeholders, suggesting that these links could be exploited to increase simulation modelling within the healthcare sector to improve operational efficiency.

This paper highlights the strong links between healthcare planning and the healthcare stakeholders and proposes that healthcare planning can play a key role in adoption of healthcare simulation modelling to achieve operational efficiency improvements.

This paper illustrates the potential link between healthcare planning and healthcare stakeholders to achieve operational improvements within the health sector.

One of the most challenging problems facing industrial engineers concerns the design and operational planning of today′s sophisticated production systems. The need for a detailed quantitative analysis is far more apparent than ever before. The application of discrete‐event simulation has been growing rapidly in the analysis of production systems. This is because no other quantitative methods can provide the flexibility, realism and predictive accuracy offered by the simulation technique. Although the important role that simulation can play in analysing production systems has now been generally realised, its use is not necessarily straightforward. The successful implementation of simulation projects usually depends on several factors which include, inter alia, the availability of simulation expertise and the ability of the available simulation software to model readily and accurately the environment under consideration. The areas of production systems where simulation can be applied are outlined. The essential considerations which must be studied when applying simulation are also discussed. An overview of simulation modelling environments that are currently used is then taken. Recommendations for future work of importance from the system analysis viewpoint are highlighted.

Many authors have suggested that disruption and associated costs result from poor quality performance in manufacturing. The purpose of this paper is to define and quantify the disruption costs associated with a simple manufacturing scenario using a simulation approach.

A manufacturing cell incorporating inspection and rework was simulated, and a validation exercise carried out. Using results from the simulation study, the authors then formulate the concept of a cost category for disruption cost, which is compatible with the traditional prevention‐appraisal‐failure (PAF) model for quality costs.

Comparative graphs of disruption costs and PAF costs elements are presented. The simulated disruption cost is compared with these traditional costs categories, and found to represent a significant additional cost at higher levels of non‐conformance.

The results presented in this paper are derived from a discrete‐event simulation exercise, using a model of a simplified generic manufacturing cell. They are believed to be indicative of costs that would occur in practical situations, but are not validated with empirical data. Further work would include such validation.

This is a theoretical paper, which attempts to extend a useful and well established cost model that has been widely accepted in industry.

The originality of this paper lies in the definition of the concept of disruption cost, as a separate category of quality cost. The simulation work indicates the potential size and behaviour of the disruption cost, compared with the traditional PAF costs categories.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.

Describes how to develop a visual interactive simulation model for flexible manufacturing systems. SIMPLE_1, an integrated modelling environment, is selected as the simulation language due to several of its innovative features. A physical simulator for a flexible manufacturing system is also chosen as the target to demonstrate the modelling process of the visual interactive simulation. The results of this study may provide industrial designers with a design and planning tool.

The current economic crisis increased the demand on management to improve process efficiency. The purpose of this paper is to identify and resolve inefficiencies within the car assembly system utilizing discrete simulation modeling and analysis in order to improve productivity at one of the original equipment manufacturers (OEM) body shops in North America.

This research was driven by a manager’s recommendation from one of the Big Three (GM, Ford, Chrysler LLC) companies in order to improve operational performance. The data utilized in creating the simulation model was obtained from one of the assembly facilities that produce three different vehicles over a period of one year. All model simulation, inputs and outputs were discussed and agreed upon by facility management.

The established base model was verified and validated to mimic the actual facility outputs indicating all process bottlenecks. Two model scenarios were considered: the first scenario focussed on the top bottleneck processes flexibility with a ROI of 497 percent, while the second considered changing the model mix percentage leading to a cost improvement of $1.6 million/annually.

The model only considered management decision on buffer sizes, batch size and the top bottleneck station alternatives to make improvements. Simulating improvements in labor efficiency, robots uptime, scrap root cause, and maintenance response to downtime where not considered.

This paper indicated the importance of discrete simulation modeling in providing alternatives for improving process efficiency under certain financial limitations. Given the similarity of the automotive manufacturing processes among the various companies, the findings for this particular facility remain valid for other facilities.

Investment cost and process improvement are currently the two biggest challenges facing operations managers in the manufacturing industry. This study allows managers to gain a broader perspective on discrete simulation ability to simulate complicated systems and present different process improvement alternatives.

The purpose of this paper is to provide simulation modelling for bulk arrival bulk service queueing system involved in a textile industry and analyze the performance metrics.

This paper describes the simulation modelling of a bulk queueing system with limited number of admissions and multiple vacations. The model is developed for the proposed queueing system using Flexsim 2017, and it is explained through an application observed in a textile industry involving the process of cone winding.

In this paper, the simulation model has been developed to study the behaviour of queues at different resources in a production system. Various performance measures such as average components, average waiting time, total number of inputs and outputs, processing time and idle time involved in a textile industry are evaluated using simulation and justified through numerical illustration.

The proposed simulation model may be used in various scenarios wherever a real time situation exists related to bulk queueing system. The results produced in this paper can be used by the manufacturing industries to enhance the need-based accuracy. It is worth pointing out that the findings are of direct practical relevance and can be successfully used for a number of industrial applications.

The approach suggested in this paper attempts to deal with the queueing system involved in a textile industry and provides numerical results in less time with less computer resources. It provides a reasonably good approximation for simple and complex queueing models where it is difficult to find closed form of theoretical results.

The purpose of this paper is to develop a generic framework for hybrid (integrated deployment of system dynamics and discrete event simulation) simulation which can be applied in the healthcare domain.

As hybrid simulation in an organisational context is a new topic with limited available data on deployment of hybrid simulation in organisational context, an inductive approach has been applied. On the basis of knowledge induced from literature, a generic conceptual framework for hybrid simulation has been developed. The proposed framework is demonstrated using an explanatory case study comprising an accident and emergency (A&E) department.

The framework provided detailed guidance for the development of a hybrid model of an A&E case study. Findings of this case study suggest that the hybrid model was more efficient in capturing behavioural impact on operational performances.

The framework is limited to only SD and DES; as agent‐based is another simulation method which is emerging as a promising tool for analysing problems such as spread of infectious diseases in healthcare context, inclusion of this into the framework will enhance the utility of the framework.

This framework will aid in the development of hybrid models capable of comprehending both detail as well as dynamic complexity, which will contribute towards a deeper understanding of the problems, resulting in more effective decision making.

It is expected that this research will encourage those engaged in simulation (e.g. researchers, practitioners, decision makers) to realise the potential of cross‐fertilisation of the two simulation paradigms.

Currently, there is no conceptual framework which provides guidance for developing hybrid models. In order to address this gap, this paper contributes by proposing a conceptual framework for hybrid simulation for the healthcare domain.

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

Both system dynamics (SD) and agent-based modeling (ABM) have been used in simulation-based group dynamics research. To combine the advantages of both simulation approaches, the concept of SD-ABM hybrid simulation has been proposed. However, research efforts to compare the effectiveness of modeling approaches between the hybrid and non-hybrid models in the context of group dynamics study are rare. Against this background, this study aims to propose an agent-embedded SD (aeSD) modeling approach and demonstrate its advantages when compared to pure SD or ABM modeling approaches, based on a research case on construction workers’ social absenteeism.

The authors introduce an aeSD modeling approach to incorporate individual attributes and interactions among individuals in an SD model. An aeSD model is developed to replicate the behavior of an agent-based model previously developed by the authors to study construction workers’ group behavior regarding absenteeism. Then, the characteristics of the aeSD model in comparison with a pure ABM or SD model are demonstrated through various simulation experiments.

It is demonstrated that an aeSD model can capture the diversity of individuals and simulate emergent system behaviors arising from interactions among heterogeneous agents while holding the strengths of an SD model in identifying causal feedback loops and policy testing. Specifically, the effectiveness of the aeSD approach in policy testing is demonstrated through examples of simulation experiments designed to test various group-level and individual-level interventions to control social absence behavior of workers (e.g. changing work groupings, influencing workgroup networks and communication channels) under the consideration of the context of construction projects.

The proposed aeSD modeling method is a novel approach to how individual attributes of agents can be modeled into an SD model. Such an embedding-based approach is distinguished from the previous communication-based hybrid simulation approaches. The demonstration example presented in the paper shows that the aeSD modeling approach has advantages in studying group dynamic behavior, especially when the modeling of the interactions and networks between individuals is needed within an SD structure. The simulation experiments conducted in this study demonstrate the characteristics of the aeSD approach distinguishable from both ABM and SD. Based on the results, it is argued that the aeSD modeling approach would be useful in studying construction workers’ social behavior and investigating worker policies through computer simulation.