The purpose of the article is to create a predictive analytics simulation model to help managers anticipate manufacturing issues. It integrates specifically the involvement of human resources in the manufacturing systems. The predictive analytics simulation model also includes the main existing interactions between the operators and the manufacturing system.
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.
Pallets are returnable transport items and of great importance for supply chains. They ensure efficient storage, transport, and handling processes. The pallet cycle, however, is associated with a substantial effort. In addition to administrative costs, extra trips and detours must often be taken by forwarders to retrieve pallets or buy new pallets. In this paper, a fictitious cross-actor pallet exchange platform is analyzed by building a supply chain model.
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.
This paper focuses on optimizing the production of a real-world pre-assembly facility in a high-volume and high-mix semiconductor wafer fab. In simulation software, the researchers built an in-depth, deterministic discrete-event model. With it, they tested release policies in two stages, using real production data and demands.
In Ireland, cheese is one of the most exported products. Due to Brexit, it will be most impacted by delays at ports caused by the potential reintroduction of customs or border controls. To predict forthcoming changes and analyze their impact on the cheese supply chain in Ireland, a simulation model was built. To develop the model, the research team used a combination of the agent-based and discrete event modeling approaches along with geographic information system (GIS) included in AnyLogic simulation software.
This paper presents a comprehensive production scheduling approach that combines optimization and simulation to cope with parameter uncertainty.
The approach allows for identifying and including demand fluctuations and scrap rates. Furthermore, the researchers adapt seven optimization algorithms for two-stage hybrid flow shops with unrelated machines, machine qualifications, and skipping stages with the objective to minimize the makespan.
The combination of methods is validated on a real production case of the automobile industry.
Overcrowding in the Emergency Department (ED) is one of the most important issues in healthcare systems. The lack of downstream beds can affect the quality of care for patients who need hospitalization after an ED visit.
This research proposes a generic simulation model as one of the ED overcrowding solutions to analyze patient pathways from the ED to hospital discharge. The model is adaptable for all pathologies and can include several hospitals within a healthcare network. To identify relevant pathways the research team conducts pathway analysis using Process Mining.