Demands for health care are becoming overwhelming for healthcare systems around the world regarding the availability of resources, particularly in emergency departments (EDs). This paper provides a case study of the Uppsala University Hospital, where a data-driven simulation model was designed to examine the current state of the patient flow and to investigate potential logistics solutions for improving that flow through a novel strategy.
This article presents a simulation model for feed supply networks consisting of a number of customers, retailers, and manufacturers which are agents and placed on Geographic Information System (GIS) map. The simulation model proposed a fuzzy-based decision strategy for customers to decide when to order specific products. The AnyLogic model described a possible decision strategy for retailers to optimize their transport routes by selecting viable manufacturers.
This paper considers a loading facility that uses Truck Appointment System (TAS) for slot management and faces incoming truck arrival uncertainty due to traffic congestion. The researchers developed an integer mathematical model to represent the adaptive behavior that determines the optimal reschedules by minimizing the average truck waiting time. The model was aimed to emulate the adaptive system. It was created in AnyLogic simulation software based on the conceptual model.
In this paper, researchers developed a discrete event simulation model of a Dutch phone and subscription retailer's queueing system. The goal of simulation modeling was to learn what improvements can be suggested to reduce the waiting time in shops.
Elevator traffic affects people who use elevators in high-rise buildings. This happens because elevators transport a number of passengers above its planned capacity. The next set of passengers still needs to wait approximately four minutes before they are serviced even if the elevators implement a static zoning division to reduce waiting time during peak hours. Therefore, there is a need to improve the current elevator system. And to better understand how the system works along with its pitfalls, the environment, and the passenger flow will be simulated using agent-based modeling. The simulation will be modeled using data gathered from ID scans and CCTV footage.
During recent years, Autonomous Vehicle Storage and Retrieval Systems (AVS/RS) have been widely applied in warehouse optimization to meet the increasing demand for rapid and flexible large-scale storage and retrieval tasks. This paper focuses on the operations control strategies with regard to the conveyor system, rack storage system, and pick-up system in order to maximize the system’s throughput capacity and minimize the storage/retrieval times of items. The study is based on a large-scale shoe manufacturer’s warehouse optimization and provides insights for system management.
Besides transportation costs the punctual delivery of the goods is a key factor for mode choice in intermodal transportation networks. However, only a limited number of studies have included stochastic transportation time in Service Network Design, which refers to decisions regarding transportation mode and services, so far.
The paper on hand combines a Sample Average Approximation approach with Discrete Event Simulation for transportation network optimization with stochastic transportation times. This includes the corresponding vehicle routing problem for road vehicles. The share of orders transported by intermodal road-rail vs. unimodal road transportation in dependence of costs and delays of the trains is evaluated for a generic transportation relation in Central Europe. The data is backed by empirical data for transportation orders and delay distributions.
A common challenge in field service planning is to evaluate different design choices, related to staffing decisions, technician scheduling strategies, and technological improvements in order to make the system more efficient. This work provides a simulation-based optimization tool to support decision makers in tackling this challenging problem. The proposed framework relies on an optimization engine for the generation of the daily plans.
The study uses AnyLogic as field service planning software to evaluate the applicability of such plans by taking into account the stochastic factors. Furthermore, an interface manages the communication between these two components and allows a feedback loop between the simulator and the optimizer to achieve more robust plans. The applicability of the framework is demonstrated through a business case to evaluate different staffing decisions.
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.
The paper first discusses the importance of discrete event simulation (DES) in the business school curriculum. It next notes how small Macintosh lap tops have become increasingly popular among business students. We next discuss what DES software is available on the Mac, first directly, then indirectly by running DES software for Windows in some way on the Mac. Noting that there is not much simple DES software on the Mac, but yet a great demand for such software from many business students, we turn to the transfer of one pedagogical software system, aGPSS, from Windows to the Mac. We here first give a brief historic background of aGPSS. Next we discuss some of the problems encountered when transferring aGPSS to the Mac. The paper ends with a brief discussion of some pedagogical aspects of using aGPSS on the Mac in the teaching of basic management science.