Exemples de projets

  • Simulation de la construction d’un tunnel à l’aide d’un tunnelier
    Le coût d’une heure d’arrêt d’un tunnelier (machine de forage de tunnel) est habituellement élevé et les gestionnaires de projet doivent s’efforcer d’éviter les retards dans la construction. Le but du projet de simulation, qui a été mené à l’université Bochum de la Ruhr en Allemagne, consistait à créer un modèle de simulation capable de déterminer les goulots d’étranglement dans les processus de construction du tunnel, afin de minimiser les pertes financières potentielles.
  • Planification de la production dans l’industrie maritime
    Les dirigeants de l’un des fabricants italiens les plus en vue recherchaient une nouvelle approche intelligente pour simplifier l’élaboration du planning. FD et DSE ont été approchés pour développer un outil de simulation du planning entièrement nouveau. L’objectif était de fournir au planificateur de la production réelle une mine d’informations pour le planning permettant de tester et d’ajuster le plan avant sa mise en œuvre. Le concept de l’outil s’organisait autour de l’aide décisionnelle, ce qui signifie que l’individu peut facilement affiner les idées et tester la faisabilité d’un plan dans de multiples situations avant de le déployer dans l’atelier.
  • Modélisation du système de Back Office de la Banca d'Italia
    La Banca d'Italia traite une certaine quantité de transferts de crédits manuels chaque année. Ces transferts ne peuvent être traités automatiquement et requièrent deux divisions d’employés dans le back office de la banque. La banque voulait déterminer si la fusion de ces deux divisions serait profitable.
  • Construction Simulation Model Tackling Increased Constraints on a Complex Earthmoving Project
    “Anylogic’s flexible and easy to use environment enabled CCT’s simulation engineers to rapidly model the newly added constraints and deliver a valuable simulation model leading to a highly successful claim process,” affirms Ramzi Roy Labban, Manager, Construction Systems and Simulation at CCC.
  • Improving Plane Maintenance Process with AnyLogic Agent-Based Modeling
    The military aircraft maintenance turnaround process (the in-between time when the aircraft touches down, is refueled, rearmed, and inspected, in order to be released) is complex and, being fairly time-consuming, includes multiple interactions and parallel workflows. Engineers from Lockheed Martin, one of the largest companies in the aerospace, defense, security, and technologies industry, used AnyLogic simulation modeling to improve decision making in the entire military airplane turnaround process and evaluate the impact of process changes on turnaround time.
  • Apparel Company Chose Location for New Distribution Center Using Simulation Modeling
    Fruit of the Loom (FOTL) is one of the largest US apparel manufacturers and marketers. The company was expanding, and the executives wanted to know if it would be beneficial, in terms of shipping costs, to add a new distribution center (DC) on the east/west coasts of the US, or to redistribute products to a pre-existing DC. The contractors decided to simulate the whole supply chain in order to visualize DC locations on a GIS map, and the supply network between them.
  • Business Processes Optimization Using Data Science and Simulation Modeling
    The world’s largest companies use data analytics to increase their revenue and keep up with the changing business world. But how does data science relate to simulation modeling, and what are the cases for the implementation of this interaction, primarily concerning value for the business? The United Services Automobile Association (USAA), a Fortune 500 group of companies, has answered these questions with real-life solutions.
  • Outpatient Appointment Scheduling Using Discrete Event Simulation Modeling
    Indiana University Health Arnett Hospital, consisting of a full-service acute care hospital and a multispecialty clinic, faced poor statistics because the number of no-show patients (those who don’t show up for their scheduled appointments) rose dramatically to 30%. This was primarily connected to the fact that clinic schedules were driven by individual preferences of the medical staff, which led to increased variations in scheduling rules. To eliminate the problem, the client wanted to develop a scheduling methodology that would benefit the clinic, doctors, and patients.