Through simulation modeling, this research highlighted the interactions of key system parameters in a disruption phase under different scenarios. A multi-period, multi-echelon serial supply chain was studied with agent-based and discrete-event simulation.
The semiconductor industry is facing pressure to reduce its extensive energy consumption, which requires transparency on the relationship between energy efficiency and original planning objectives. This paper aims to develop an extension to the existing Operating Curve concept by investigating the effect of utilization on energy efficiency. It uses the results of discrete-event simulation on a fab level to verify the novel concept.
As widely reported in the literature, the leather luxury accessories industry is characterized by a highly fragmented supply chain. Frequent changes in the production mix had to be managed, often requiring the re-optimization or even re-design of production flows. The objective of this paper was to propose a data-driven simulation model for production balancing and optimization in this sector.
The job-shop scheduling problem was considered with sequence-dependent setup times and preventive maintenance constraints. A hybrid model combining discrete-event simulation and an optimization algorithm in Python was applied to simulate the production process and solve the job-shop problem.
An animated, data-driven and data-generated simulation model was developed to support the development of both the design and configuration methodology and operational control strategy of Shuttle-based Storage and Retrieval Systems.
In this study, researchers investigated the process of orthopedic treatment in an Australian health service, from the point of referral to surgical intervention. In a number of cases, surgical intervention could be avoided through participation in community-based rehabilitation programs.
Blood supply chains are an essential element of healthcare systems. Simulation provides the means to test out digital innovations for blood supply chains regarding their operational efficiency and expected costs.
A discrete event simulation model was developed for a network of eight major intensive care units (ICUs) as well as high-acuity units (HAUs) in British Columbia, Canada. The simulation model will be used to develop strategies for managing the combined impacts of COVID-19 and seasonal influenza without the need for extensive public health interventions to limit transmission.
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 study investigates a different source of uncertainty, which is the waiting time for the next vessel that is scheduled on a specific route, connecting two international ports. The aim of this research was to determine the booking size for vessels in oversea delivery to minimize transportation costs. The simulation model and all the respective processes included in the oversea supply chain were developed in AnyLogic with a discrete-event paradigm.