Discrete event simulation model reduction: A causal approach

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

CHAPTER X - DISCRETE EVENT SIMULATION PROGRAMMING

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

A discrete-event simulation (DES) model was developed to enhance the reinforcing bar (rebar) fabrication efficiency for multiple simultaneous projects at different sites. The production volume and procedure of the actual rebar fabrication plant were compared to the simulation model to ensure its accuracy. By determining the loss rate and necessary processing time, the fabrication plan was then optimized. The rebar type and machine features, which influence the loss rate and time required for rebar fabrication, were configured as the parameters in a discrete-event simulation model. The model considers a situation in which a rebar fabrication plant simultaneously delivers rebars to multiple sites. In this manner, the model can quantify the loss rate and time required in the fabrication process. The determination of the loss rate according to the import ratio of raw steel, site combination, and length can help optimize the rebar fabrication plan and increase work efficiency. In the considered scenario, a two-site combination and import ratio of raw steel of 2:1 (8 m:10 m) was noted to corresponded to the maximum decrease in the loss rate and required time. By extending the proposed approach to the complete rebar process (processing–transportation–construction), the plant member production process can be optimized.

Although Thrombolysis has been licensed in the UK since 2003, it is still administered only to a small percentage of eligible patients. We consider the impact of investing the impact of thrombolysis on important acute stroke services, and the effect on quality of life. The concept is illustrated using data from the Northern Ireland Stroke Service. Retrospective study. We first present results of survival analysis utilizing length of stay (LOS) for discharge destinations, based on data from the Belfast City Hospital (BCH). None of these patients actually received thrombolysis but from those who would have been eligible, we created two initial groups, the first representing a scenario where they received thrombolysis and the second comprising those who do not receive thrombolysis. On the basis of the survival analysis, we created several subgroups based on discharge destination. We then developed a discrete event simulation (DES) model, where each group is a patient pathway within the simulation. Coxian phase type distributions were used to model the group LOS. Various scenarios were explored focusing on cost-effectiveness across hospital, community and social services had thrombolysis been administered to these patients, and the possible improvement in quality of life, should the proportion of patients who are administered thrombolysis be increased. Our aim in simulating various scenarios for this historical group of patients is to assess what the cost-effectiveness of thrombolysis would have been under different scenarios; from this we can infer the likely cost-effectiveness of future policies. The cost of thrombolysis is offset by reduction in hospital, community rehabilitation and institutional care costs, with a corresponding improvement in quality of life. Our model suggests that provision of thrombolysis would produce moderate overall improvement to the service assuming current levels of funding.

Geographic Service Delivery for Endovascular Clot Retrieval: Using Discrete Event Simulation to Optimize Resources

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.

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.

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.

In manufacturing plants, part of assembly is often performed by human workers and therefore its performance largely depends on humans (or human performances) rather than machines. However, most studies on human centred assembly systems using the modelling simulation methods do not consider or incorporate the effects of human performance that may also impact the overall system performance of such as an assembly line. The prediction of human performance or behaviour in a manufacturing system evaluation is often overlooked by researchers or system designers partially due to a lack of proper versions of existing simulation tools that can incorporate human attributes into an established simulation model. This paper presents a study by incorporating human attributes of learning and ageing into a discrete event simulation (DES) model based on a human centred assembly system. The effects of worker performance due to aging and learning were investigated and identified through a literature review [1]. The simulation result demonstrates that the worker performance may approach his/her full capacity at the age of 38 years old and this may decrease by an average of 1% from 38 to 40 years old, and 6% at the age of 45 years old. After the age of 45 years old, the decline rate of worker performance may slow down as it can be offset by accumulating worker experience through learning.

Improvement in patient waiting time in dispensing pharmacies is an important element for patient and pharmacists. The One-Dose Package (ODP) of medicines was implemented in Japan to support medicine adherence among elderly patients; however, it also contributed to increase in patient waiting times. Given the projected increase in ODP patients in the near future owing to rapid population aging, development of improved strategies is a key imperative. We conducted a cross-sectional survey at a single dispensing pharmacy to clarify the impact of ODP on patient waiting time. Further, we propose an improvement strategy developed with use of a discrete event simulation (DES) model. A total of 673 patients received pharmacy services during the study period. A two-fold difference in mean waiting time was observed between ODP and non-ODP patients (22.6 and 11.2 min, respectively). The DES model was constructed with input parameters estimated from observed data. Introduction of fully automated ODP (A-ODP) system was projected to reduce the waiting time for ODP patient by 0.5 times (from 23.1 to 11.5 min). Furthermore, assuming that 40% of non-ODP patients would transfer to ODP, the waiting time was predicted to increase to 56.8 min; however, introduction of the A-ODP system decreased the waiting time to 20.4 min. Our findings indicate that ODP is one of the elements that increases the waiting time and that it might become longer in the future. Introduction of the A-ODP system may be an effective strategy to improve waiting time.

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

A simulation optimization approach to investigate resource planning and coordination mechanisms in emergency systems