Bus Fleet Size Dimensioning In an International Airport Using Discrete Event Simulation

The ore handling systems represent one of the largest expenses in cave mining, while the productivity and cost of a given production system can significantly impact the value of mining projects and operations. Moreover, in recent years the increase in mineral production using mass mining methods has led to significant progress in extraction techniques as well as equipment and technologies. Simulations are used to mimic real situations of production systems to aid in the design of new concepts and make engineered decisions. Discrete event simulation (DES) techniques can be applied to a wide range of analyses in cave mining, from refinement of cost and feasibility of plans or schedules to understanding the system behaviour for better decisions before expenses. This paper discusses the foundations to build a simulation model, while presenting the development of a discrete event simulation code using the R-Simmer package for the R language, to estimate productivity and capacity of an underground mining production system. Models using open source tools are intended to be an alternative to commercial simulation software, which can be onerous and restricted to specific settings. The outcome of this work constitutes a novel contribution to the simulation field, with the benefit to help design and assess cave mining ore handling systems but also create the basis of a future fully open-source simulation tool for cave mining.

Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

MO3 COMPARING METHODS OF DATA SYNTHESIS: UPDATING PARAMETERS OF AN EXISTING PROBABILISTIC COST-EFFECTIVENESS MODEL

MO1 ADVANTAGES AND DISADVANTAGES OF MARKOV MODELS VERSUS DISCRETE EVENT MODELS FOR SIMULATING PATIENT FLOWS

CHAPTER X - DISCRETE EVENT SIMULATION PROGRAMMING

Delays beyond recommended wait times, especially for specialist services, are associated with adverse health outcomes. The Alberta Surgical Initiative aims to improve the referral wait time-the time between a referral is received at the central intake to the time a specialist sees the patient. Using the discrete event simulation modelling approach, we evaluated and compared the impact of four referral distribution policies in a central intake system on three system performance measures (number of consultations, referral wait time and surgeon utilization). The model was co-designed with clinicians and clinic staff to represent the flow of patients through the system. We used data from the Facilitated Access to Surgical Treatment (FAST) centralized intake referral program for General Surgery to parameterize the model. Four distribution policies were evaluated - next-available-surgeon, sequential, "blackjack," and "kanban." A sequential distribution of referrals for surgical consultation among the surgeons resulted in the worst performance in terms of the number of consultations, referral wait time and surgeon utilization. The three other distribution policies are comparable in performance. The "next available surgeon" model provided the most efficient and robust model, with approximately 1,000 more consultations, 100days shorter referral time and a 14% increase in surgeon utilization. Discrete event simulation (DES) modelling can be an effective tool to illustrate and communicate the impact of the referral distribution policy on system performance in terms of the number of consultations, referral wait time and surgeon utilization.

This paper describes the significant positive impact that the Ada® programming language can have on the development and application of simulation models. The Ada language has specific features which are shown to be ideally suited for implementing the unique requirements of discrete-event simulation, including such simulation functions as list processing, dynamic memory management, and event calendar management. Experiences and conclusions are described based upon the development of a general purpose discrete-event simulation package. The central issues of discrete-event simulation are addressed by describing the implementation of key simulation requirements in the package and indicating how Ada language capabilities drove the design of these features. Examples of design alternatives of general interest to both simulation modelers and Ada developers are presented. In addition, issues related to very large simulation models such as long model running times and verification of complex models are specifically addressed.

BackgroundThe World Health Organization (WHO) has called for the elimination of cervical cancer. Unfortunately, the implementation of cost-effective prevention and control strategies has faced significant barriers, such as insufficient guidance on best practices for resource and operations planning. Therefore, we demonstrate the value of discrete event simulation (DES) in implementation science research and practice, particularly to support the programmatic and operational planning for sustainable and resilient delivery of healthcare interventions. Our specific example shows how DES models can inform planning for scale-up and resilient operations of a new HPV-based screen and treat program in Iquitos, an Amazonian city of Peru.MethodsUsing data from a time and motion study and cervical cancer screening registry from Iquitos, Peru, we developed a DES model to conduct virtual experimentation with “what-if” scenarios that compare different workflow and processing strategies under resource constraints and disruptions to the screening system.ResultsOur simulations show how much the screening system’s capacity can be increased at current resource levels, how much variability in service times can be tolerated, and the extent of resilience to disruptions such as curtailed resources. The simulations also identify the resources that would be required to scale up for larger target populations or increased resilience to disruptions, illustrating the key tradeoff between resilience and efficiency. Thus, our results demonstrate how DES models can inform specific resourcing decisions but can also highlight important tradeoffs and suggest general “rules” for resource and operational planning.ConclusionsMultilevel planning and implementation challenges are not unique to sustainable adoption of cervical cancer screening programs but represent common barriers to the successful scale-up of many preventative health interventions worldwide. DES represents a broadly applicable tool to address complex implementation challenges identified at the national, regional, and local levels across settings and health interventions—how to make effective and efficient operational and resourcing decisions to support program adaptation to local constraints and demands so that they are resilient to changing demands and more likely to be maintained with fidelity over time.

The simulation modeling community consists of several frameworks or approaches that have been developed at different times to handle different problems, and persist in a state of relatively limited interaction. Various forms of hybrid modeling, combining aspects of two or more modeling approaches, have been proposed and used, but these still represent a relatively small part of the world of simulation modeling. In this paper, we will draw on parallels between the current debate around discrete event simulation and agent-based modeling, and the historic conflict between two schools of psychology: behaviorism (human thought considered a black box, focus restricted to observable behavior), and cognitive psychology (emphasis on conscious thought processes). Through a presentation of different perspectives on what happened in psychology, we will discuss views on the combination of different modeling approaches, and implications of similar perspectives on the future development of simulation modeling.

Automated storage and retrieval systems have become increasingly popular in modern supply chains due to their significant advantages over traditional warehousing systems. Due to the high complexity of these systems, simulation approaches can be used to generate accurate performance measures for a specific system configuration. Simulation models are also the cornerstone of digital twins, one of the latest technological innovations that can further improve warehouse operations. Therefore, the aim of this research is to describe an approach for the development of a discrete event simulation model of an automated storage and retrieval system with a perspective towards the implementation of a digital twin. To be consistent with the objectives of the digital twin, the proposed model represents both the physical system and the overarching information technology architecture, such as the warehouse management system and the warehouse control system. In addition, this paper describes a methodology to validate such a simulation model by setting up an experimental campaign based on the principles of design of experiment. The experiments conducted in a logistics laboratory were used to iteratively calibrate the model until its performance accurately reflected the functioning of the real system. The results obtained demonstrate the effectiveness of the proposed method. Finally, this work contributes to the literature on warehouse digital twins by highlighting new variables to be considered when defining travel time models and their stochastic nature.

Discrete event simulation model reduction: A causal approach

This research aims to develop a simulation theory using discrete event simulation as a convincing tool for building a digital twin models in the pharmaceutical industry calibration department as part of the supporting components enabler for industry 4.0. The Methodology used are define the Calibration Laboratory service model, then analyze the big data collected from the service performance parameters. The analyzed data will be used to continue the development of discrete event simulation models for calibration laboratory service systems using ProModel2016 software. The simulation output data will be verified with real event data to ensure similarity. The study finds that the Discrete Event Simulation approach can be used as a convincing tool to develop digital twin models as virtual replicas of Calibration Laboratory Services in the Pharmaceutical Industry so that improvement planning can be analyzed efficiently. There is a limitation of this research that the digital twin model can only be verified for the Pharmaceutical Industry Calibration Laboratory Services as a case study object. Further research needs to be carried out to expand the possibilities of using this discrete event simulation approach for Digital Twin Model in every aspect of industrial activities.

Discrete event simulation as an ergonomic tool to predict workload exposures during systems design

Using discrete event computer simulation to improve patient flow in a Ghanaian acute care hospital