Academic articles

Strategic Supply Chain Design for an Austrian Winter Road Service Provider


Snowplow operations are critical for public safety and economic success in countries where difficult driving conditions occur in winter. Specifically, the salt supply ensuring good driving conditions is a crucial factor. In this paper, the strategic supply chain design of a winter service provider in Austria is investigated. Two research directions on the influence of bigger and fewer salt silos per depot and the logistic costs for a unique summer salt purchasing strategy are addressed applying two independent solution approaches. On the same data basis, a simulation model is developed and a mixed integer linear problem is applied to answer the respective research questions.

How Order Placement Influences Resource Allocation and Order Processing Times Inside a Multi-user Warehouse


This paper focuses on the influence of different order placement behavior of users on the allocation of common resources inside a multi-user warehouse. Furthermore, the interdependencies between one user’s resource usage on other users’ order processing time is investigated. For this objective, an agent-based simulation model has been developed, depicting a rectangular warehouse with two users and one order picker. Results show that different order placement behavior and resource usage of one user have a strong influence on order processing times of other users. Furthermore, by simulating uneven order placement by one user, it can be shown that peaks in order demand influence other user’s order processing times with a delay of up to two hours after the peak occurred. Thus, the results highlight the need for coordinated order placement of partners inside a multi-user warehouse.

Rail Fleet Maintenance Optimization at the UK Rail Network


This paper presents a joint simulation project in the area of railway fleet maintenance optimization. A simulation model was developed to understand and visualize the complex interaction in a railway system comprising rolling stock, depots, and maintenance guidelines. In many cases, requirements on such a system come from different sides, such as fleet operation (timetables and availability), maintenance engineering (maintenance regime), and depot management (depot restrictions). The paper describes the domain-specific challenge, the model for planned maintenance optimization, the implemented scheduling algorithm, and resulting insights. It, furthermore, describes how the model can be used for a variety of different use cases all along a railway project: from sizing to forecast and performance analysis and from initial tender theory check to operational risk analysis and preventive maintenance optimization. Two real-world use cases are presented: West Coast Main Line and TransPennine Express, both in the United Kingdom.

Simulation-based Evaluation of Urban Consolidation Centers Considering Urban Access Regulations


The negative effects of urban freight transports, such as air quality problems, road congestion, and noise emissions lead in many cities to major difficulties. A widely studied measure to reduce these negative effects are Urban Consolidation Centers (UCCs), which aim to bundle freight flows to reduce the number of urban freight transports. However, many projects showed that the additional costs of UCCs often made it unattractive for carriers to participate in such schemes. This paper presents an agent-based simulation to assess the impact of urban access regulations on the cost-attractiveness of UCCs for carriers. A case study inspired by the Frankfurt Rhine-Main area is presented to compare deliveries of a group of carriers with and without a Urban Consolidation Center under various urban access scenarios. The simulation shows that regulations increase the cost-attractiveness of UCCs for carriers to varying degrees while increasing the overall traffic volume.

Agent-based Modeling for Casualty Rate Assessment of Large Event Active Shooter Incidents


The 1999 Columbine attack changed police response to the active shooter incidents (ASI) by the public and first responder’s tactics and training. With FBI data suggesting ASI events increasing, this study offers an AnyLogic models to understand mitigation actions such as Run.Hide.Fight. Our model represents a general densely populated area, such as public transportation terminal or indoor arena. Model agents include civilians, police, and shooter agents interact with the following parameters: civilian evacuation time, the response of police, firearm discharge by the shooter and police. The casualty rates vary from 85 to 1 causalities when the shooter’s rate of discharge was 1 to 60 seconds, respectively. The model as developed was shown to provide a method to evaluate and compare actions such as adequacy of training, introduction of technology into public buildings and the general design of public spaces to reduce the impact of ASI events.

A Simulation Model to Assess the Impact of Insurance Expansion on Colorectal Cancer Screening at the Population Level


Recent US healthcare reform debates have triggered substantial discussion on how best to improve access to insurance. Colorectal cancer (CRC) is an example of a largely preventable condition, if access to and use of healthcare is increased. Early and ongoing screening and intervention can identify and remove polyps before they become cancerous. We present the development of an individual-based discrete-event simulation model to estimate the impact of insurance expansion scenarios on CRC screening, incidence, mortality, and costs. A national repeated cross-sectional survey was used to estimate which individuals obtained insurance in North Carolina (NC) after the Affordable Care Act (ACA). The potential impact of expanding the state’s Medicaid program is tested and compared to no insurance reform and the ACA without Medicaid expansion. The model integrates a census-based synthetic population, national data, claims based statistical models, and a natural history module in which simulated polyps and cancer progress.

Building a Flexible Simulation Model for Modeling Multiple Outpatient Orthopedic Clinics


This study is designed to demonstrate the benefit of using a single simulation model in order to analyze operations at two distinct, but related, pediatric orthopedic outpatient clinics in Massachusetts. A simulation model with a built-in dashboard is constructed for the clinics using AnyLogic. The constructed simulation model has been proven to be a useful tool in anticipating the effects of changes in system features such as patient volume, provider team mix, and exam room assignment policies. With the development of a flexible simulation model the ultimate goal is to assist clinic managers in their efforts to reduce patient waiting time and lengths of stay in the two distinct orthopedic clinics.

A Simulation and Online Optimization Approach for the Real-time Management of Ambulances


Emergency Medical Service (EMS) is one of the most important health care services as it plays a vital role in saving people’s lives and reducing the rate of mortality and co-morbidity. The importance and sensitivity of decision making in the EMS field have been recognized by researchers who studied many problems arising in the management of EMS systems since the 1960. Some authors of similar research present a review of the many simulation models that have been developed over the years: most of the available simulation approaches are based on a Discrete Event Simulation (DES) approach.

Improving Quality of Care in a Multidisciplinary Emergency Department by the Use of Simulation Optimization: Preliminary Results


Emergency department (ED) crowding is a worldwide challenge. It adversely affects quality of care, patient safety, and employee satisfaction. The magnitude of ED crowding can be measured by the quality metrics length-of-stay (LOS), the patient’s door-to-doctor-time (DTD), and the 4-hourstandard. This standard states that 95% of the patients stay less than four hours within the ED. In order to improve those metrics, healthcare processes have to be welldesigned and resource capacity has to match the ever increasing demand. We implemented a validated, detailed discrete-event simulation model of a multidisciplinary ED in Germany to provide decision support for ED managers. Our model incorporates several patient flows considering patients and resources of two different medical specialties. The introduced simulation model was parameterized according to real-world data. Leveraging OptQuest and AnyLogic, we combined optimization and simulation to find input staffing levels that minimize the avg. LOS of patients. Simulation experiments show that certain process modifications, nurse pooling, and optimized staffing levels lead to improvements in quality of care. With respect to that, both avoiding boarding of inpatients and implementing nurse pooling result in a decrease of more than 14% in avg. LOS and are particularly promising. We also identified that reallocating capacities from internists to nurses dedicated to internal medicine patients enhances the quality of care.

Dynamic Behavioural Modeling, Simulation and Analysis of Household Water Consumption in an Urban Area: a Hybrid Approach


Pakistan is rapidly becoming a water stressed country, thus affecting people’s well-being. Authorities are faced with making drastic water conservation policies toward achieving effective management of available water resources and efficient water supply delivery coupled with responsible demand side management. Due to the lack of modern water metering in Pakistan, water consumption is not being accurately monitored. To achieve this goal, we propose a hybrid modeling and simulation framework, consisting of Agent-Based Modeling (ABM) paradigm that deals with the behavior and characteristics of individuals and System Dynamics(SD) paradigm that accounts for water flow dynamics. Our approach provides dual-resolution expressiveness suitable for replicating real-world urban infrastructure scenarios. The key objective of the research is to assist authorities to understand and forecast short-term and long-term water consumption through examining varying patterns of water consumption in different climates and thus improving demand side water usage dynamically subject to water supply availability.