This paper provides an overview of revenue management characteristics and methods in the semiconductor industry in order to enable greater customer satisfaction and supply chain flexibility while increasing revenue.
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.
This article focuses on the simulation model that was developed for Sibanye-Stillwater’s underground platinum mining operations in Nye, MT. The model was designed to help the mining company understand how bottlenecks move through their operations, to help identify which resources are constraining underground mining production increases, and to understand where capital investments are needed in backfill operations.
The Predictive Maintenance technique offers a possibility to improve productivity in semiconductor manufacturing. Current research on Predictive Maintenance mainly focuses on its technical implementation. By applying discrete-event simulation, the research team provide results on how maintenance strategies can help optimize machine operations, and how the technique contributes to an overall improvement of productivity in wafer fabrication.
In this work, the researchers undertake a root-cause enabling Vendor Managed Inventory performance measurement approach to assign responsibilities for poor performance. Additionally, the work proposes a solution methodology based on reinforcement learning for determining optimal replenishment policy in a VMI setting. Using a simulation model as a training environment, different demand scenarios are generated based on real data from Infineon Technologies AG and compared based on key performance indicators.
This paper proposes a simulation-based decentralized planning and scheduling approach to improve the performances of a job-shop production system, compliant with a semi-heterarchical Industry 4.0 architecture. To this extent, to face the increasing complexity of such a scenario, a parametric simulation model able to represent a wide number of job-shop systems is introduced.
Especially in complex manufacturing systems and uncertain conditions, sequencing operations in a machines’ queue can pose a difficult problem. To solve the problem, intelligent, adaptable, and autonomous systems have been developed using machine learning and simulation to sequence operations under uncertainty within large manufacturing systems.
This work aims at optimizing a public transportation network and its maintenance. In particular, it focuses on determining routes for replacement services and adjusting headways in case of scheduled shutdowns of subway lines. A simulation-based two-layer optimization approach is proposed to solve the problem.
Factors including hospital space layout, patient behavior, patient flow, and medical procedures interact and relate to each other, and ultimately affect efficiency and performance of healthcare facilities. And hospital layout planning can’t ignore such interdependencies.
This research integrates discrete event simulation (DES) and agent-based simulation (ABS) to help managers examine, plan, and compare different spatial design schemes through the modeling of patient behavior, patient flow, and the establishment of evaluation indexes.