This article will answer the following questions. What are the Differences in Production Scheduling Between PP/DS and Simio? What is Each System Essentially Designed to Model? What Types of Manufacturing Environments Work for Each? What are the Available Views in Each Application? Where does Each Application Stand in Terms of Reporting? How Does Each Application Deal with Constraint Based Planning? How are the Applications Setup? How do the Applications Compare in Terms of Integration to ERP Systems?
A simulation model built in Simio was used to study the current schedule of the procedures for an Outpatient Surgery (OPS) Suite. Simio was used to build an open 1-year calendar as the schedule of the OPS. The calendar is represented by a table in Simio where the number of rows represents the days in the year and the number of columns represents the number of time slots in the day based on the shortest appointment. There are 3 rooms in the OPS, hence, 1 calendar was built for each room. The calendar (table) cells are filled with zero if the time slot is available or one if the time slot has been already reserved or blocked. According to the annual demand achieved from the historical data [made and scheduled dates], the simulation is run to book appointments in the available slots based on specific rules. The simulation model is used to study different scheduling modules like blocking certain rooms at specific times for some procedures. This white paper mainly discusses how Simio made this complicated exercise an easy and enjoyable technique to implement.
The solution, modeled from land transportation optimization systems, consists of three primary tools—one for real time status of the vessels, another for up-to-date demand requests, and finally a scheduling system. Due to the variability of weather, permissible delivery times, loading and unloading times, vessel traffic, and changing geographical locations of floating rigs, it became clear that the scheduling tool should be built on a spatially aware, discrete event simulator and be capable of assessing the risks of a given schedule.
This study was requested by manufacturing technology groups within Boeing to evaluate the feasibility and capability of AFP for use on a specific part for an airplane program in development. The requestors were interested in estimating how the current state of the art would perform on a given part in a proposed production system. Furthermore, they were interested in developing a set of parameters and minimum allowable values for use in a Request for Proposal (RFP) document. The customer provided a set of decision variables, KPIs, and system properties as detailed in Table 1. They also provided high-level part geometry and production rate requirements.
A new gate plan at MEL was being considered which decreased Virgin’s utilization of the existing common user terminal and increased utilization of the dedicated Virgin terminal. While all scheduled aircraft arrivals and departures could be scheduled with the decreased gate capacity, there was some concern on potential impacts to OTP due to off schedule arrivals and departures and lack of flexibility to make changes during day of operations. In addition to OTP impacts, the potential negative guest experience due to increased aircraft queuing for gate on arrival and increased utilization of the gate without an aerobridge was of concern.
The proposed SIMIO step integrates a simulation software to a computational agent in order to perform high computational operation like optimization. Several applications are presented to illustrate the potential of the proposed CallMatlab step instance in order to implement IOS modeling. However, this step is not limited to perform optimization and could be utilized to execute any type of calculation whichever user desires. We believe this addition, adds a new dimension to simulation modeling approach. This would enable experts to enjoy the modeling simulation while implementing their own logics and decision making tools within the simulation run.
The heavily traveled commuter corridor carries over 30,000 vehicles per day with average annual traffic increases of between nine and ten percent. The project corridor includes seven signalized intersections and three non-signalized intersections, including an non-signalized entrance and exit to a large grocery store. The current system experiences many traffic accidents as travelers are attempting to access and depart the store. Traffic analysis was conducted to determine traffic flow patterns by time of day in order to determine the number of signal timing plans is needed for the duration of a day. Analysis was conducted using SIMIO modeling software. The model was compared and calibrated to observed conditions to validate the model before analyzing scenarios of optimized coordinated signal timing plans along the corridor
In spite of what you might have heard, doing simulation projects well is not easy. There are many ways that even an experienced simulationist can fail. In this paper we have discussed some common traps and ways to avoid them. While following these suggestions will not guarantee a bull’s eye, it will certainly improve your chance of hitting the target.
A midstream petroleum company was designing and developing improvements at an existing facility to increase their crude-by-rail terminalling and transloading business, accomplished by expanding and reconfiguring their rail / truck infrastructure to create a new interface point between pipeline and rail transport. The company recognized the need to apply modeling and simulation technology to represent the new crude loading system in a dynamic environment, therein incorporating inherent variability, to validate the design and make informed decisions. There was the specific need to verify the process design throughput of the loading facility, in the holistic context of the anticipated logistics and business/market environment.
Simulation was used to help design engineers better understand the operating dynamics of a unique, not-yet-built theme park ride to gain insight into whether or not the ride is likely to function as designed while keeping within safety parameters. The analysis also assessed different methods of configuring ride operations to maintain maximum rider throughput and avoid interruptions to the rider experience resulting from delays in the load/unload station.