Automatic model generation, the consequential reduction of problem solving cycles and the need for a higher degree of data integration have long been characterized as significant challenges in the field of simulation of manufacturing systems. Especially operationally used manufacturing simulation models require a high degree of modeling detail and thus depend on a significant amount of input data. In many cases, the time and effort required to manually build such a detailed model and keeping it up-to-date are prohibitive. This paper describes a practical case in which entire simulation models of a complex and large scale automotive flow shop production were automatically created from an automotive company’s SAP and MES systems in order to support operational planning purposes and reduce operational logistical risks, such as production disruptions caused by stock-out situations at the manufacturing line.
In fleet management, aircraft undergo phase inspection to maximize aircraft availability. An aircraft is grounded after reaching a maximum threshold of flight hours accrued since its last phase inspection. To manage this process, planners use a time distributed index to track the phase cycle of individual aircraft and keep the planes respectively in-phase. As planes break and maintenance lines become backed-up, the avail-ability of aircraft diminish; the desired effect for the mission is lost, and the constant use of spare planes invite future scheduling hazards. In this example, planners are constantly faced with determining schedules with several random factors and risk. The model presented here via Simio is a risk-based planning and scheduling simulation to identify risk and account for randomness in phase cycles. The result of this model provides the planners the opportunity to input an actual schedule into the system, assess fleet health, and conduct what-if analysis.
One of the most pressing issues in the current 401(k) retirement system is the problem of employees cashing out their accounts when they leave a job. This is especially true for accounts less than $5,000. After leaving a job, approximately 60% of these individuals will cash out within a year and approximately 90% will cash out within 7 years. Retirement Clearinghouse, LLC (RCH) has proposed changes to the retirement system where a clearinghouse will find an employee’s new 401(k) through records matching technology and automatically merge the previous 401(k) with the new 401(k). The name for this new process is autoportability. This simulation evaluated the impact of autoportability on the retirement market and it demonstrates that on a cumulative basis over the 40-year time horizon, cash outs decline from $320 billion to $164 billion, while Roll-Ins increase from $15 billion to nearly $130 billion, helping millions to preserve their retirement.
A multi-year research project focused on a global aerospace company’s design-to-production transition, and in particular how to answer production-related questions much earlier in a program’s design cycle than is possible today. A fundamental difficulty is that the time and expertise required to formulate appropriate analysis models prevents their routine use, especially in new program development. The project’s goal was to reduce these requirements, and by late 2014 a methodology had been developed for on-demand analysis generation to answer routine questions about production systems. A pilot project was conducted in 2015 to demonstrate efficacy, that an implementation of the methodology could in fact reduce by at least an order of magnitude the time required to answer a frequently-asked question, in a repeatable way while specification of the products, their process plans, planned facilities, and available resources were frequently changing. This paper summarizes the methodology, its pilot project implementation, and preliminary results.
Classical planning approaches of storage allocation decisions are often conducted iteratively with significant manual effort. Warehouse layouts are generated on the basis of planners’ experiences with the target to reduce the operators’ travel distances and thereby to increase productivity. By combining optimization and simulation in a software-based planning tool, a multitude of mathematically optimized storage allocation scenarios can be generated and analyzed to improve traditional planning approaches. This paper describes a practical case of a German automotive manufacturer’s warehouse allocation problem that is approached using an evolutionary meta-heuristic. The best solutions of the optimization are loaded into a large scale, automatically generated simulation model and evaluated using the company’s real-life data.
A variety of challenges are inherent to the provision and management of administrative services in a federal agency, including the Office of Research Services (ORS) at the National Institutes of Health (NIH). Many administrative functions are both regulatory and policy driven, and requirements are constantly changing. As the NIH research mission requirements change and evolve, the demand and nature of administrative support evolves as well. Resources need to be planned for and the proper tools are required to be in place in this dynamic environment in order to achieve success in providing the required administrative services, in a timely manner, with quality outcomes. The output of these processes in most, if not all, cases is ‘intangible’ and process visibility is limited. Computer simulation techniques will be utilized to develop a more in-depth understanding of these administrative functions, develop recommendations for improved resource allocation, productivity and quality improvement, and enhance communication and visibility of these processes among customers and stakeholders.
Traditionally, architects rely on average utilization benchmarks to determine appropriate department sizes when planning a new facility. While these averages might adequately predict space for the design of an office building or parking lot, they sometimes fall short of accurately determining the amount of space needed for healthcare facilities. A community hospital in a costal Mid-Atlantic state is experiencing significant emergency department (ED) holds due to a lack of inpatient capacity. Analysis of patient arrival and unit assignment data led the team to believe that treating observation patients in inpatient units is causing the capacity problem. A discrete event simulation (DES) model helped determine the appropriate size of an observation unit needed to reduce ED holds and relieve current inpatient pressures.
This study analyzes the handling operations performance at an Empty Container Depot that serves different shipping lines operating with the port of Valparaíso, Chile. With the aid of a discrete event simulation model built in Simio that interacts with an SQL Server database, we seek to improve container stacking policies and to redesign the depot’s layout such that truck turn-around times decrease.
Amsterdam’s Schiphol capacity is limited to 500,000 air traffic movements per year and currently is reaching the limit. For that reason, Schiphol Group decided to divert the non-hub related traffic to the regional airport in Lelystad. This airport will be upgraded to handle commercial traffic, mainly low cost carriers. We used a divide and conquer approach in SIMIO modules in which we included the main elements in the system namely airspace, runway, taxiways and airport stands for analyzing the future performance and potential operative problems of the airport. An analysis of the different operative areas of the system was performed and we could identify problems due to the emergent dynamics once the different subsystems interacted between them.
We developed a simulation model in SIMIO representing the system elements of the pre-archival process taking place at the largest archival services company in Israel. The pre-archival process usually involves a data entry operator manually registering retrieval information of boxes and files arriving on a roller-conveyor before they are assigned a space at the storing facility. The operators sit around the conveyor and pick a barcoded box. Using the simulation model we explored the behavior of the original system and identified opportunities for efficiency improvement. Initial changes in the system have shown an improvement in the system’s capacity of up to 15% over several months. The following sections provide the system descriptions and features of the modelling components.
This extended abstract provides an overview of the development of a simulation model to be used in the assistance of triaging patients into the Hospital Internal Medicine (HIM) Department at The Mayo Clinic in Rochester, MN in an effort to balance workload among the department services. The main contribution of this work is the development of a score that measures provider workload more accurately. Delphi surveys, conjoint analysis, and optimization methods were used in the creation of this score and it is believed to better represent provider workload. Preliminary results were based on the proportion of time of a month that each service was at or above “maximum utilization”, which is how workload is currently viewed at an instance. A simulation model built in SIMIO 8 yielded a 12.1% decrease in the proportion of time that a service was at or above their “max utilization” on average, while also seeing a decrease in the average difference among these proportions by 8.3% (better balance among all services).
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.