Scheduling problems are typically large and complex and they are classified mathematically in a group of problems referred to as NP-Hard (non-deterministic polynomial-time hard). In non-mathematical terms these are the hardest of the hard computational problems for which no practical optimal algorithms exist. As a result, all scheduling solutions make use of heuristics and hence none produce an optimal solution (regardless of what vendors might suggest). The best that can be hoped for with this class of problems is a “good” solution that is better and easier to obtain than trying to manually generate a solution using Excel or a planning board. In this white paper we will briefly compare the optimization and simulation based approaches to the scheduling problem.
The convenience and economic benefits are driving the movement of many enterprise applications to the cloud. Simulation and Risk-based Planning and Scheduling share these same benefits, but also benefit from the ability to rapidly scale the number of compute nodes to run many simulation replications in parallel. The heavy computational demands of simulation and Risk-based Planning and Scheduling, along with the ability to execute experiments by spreading replications across processors, makes these ideal applications for the cloud.
Simio is the first Commercial-off-the-Shelf risk-based planning & scheduling (RPS) tool for interfacing an ERP system with flexible SIMULATION models that account for process variation, predict probability for hitting key targets and provide a platform for generating good alternatives. This gives you a fast answer to what it will cost you to buy down risk in your schedule. RPS is best suited in highly dynamic factory scheduling applications, where a fast response is required, and a detailed, realistic representation of complex constraints on equipment and operators must be modeled in order to generate a good schedule. Click here for the business benefits of Simio's Risk-based Planning and Scheduling (RPS).
The role that variation plays in creating congestion and delays in manufacturing is well documented in the literature, but is typically ignored in day-to-day planning and scheduling of production. Advanced Planning and Scheduling (APS) tools generate schedules by completely ignoring system variation -- producing unrealistic and optimistic schedules that promise more than can be delivered.
This paper discussed Advanced Planning and Scheduling (APS) and how Risk-based Planning and Scheduling (RPS) can be used to supplement the typically optimistic APS plan. RPS supplies not only an analysis of schedule risk, but also a means to determine the best way to mitigate those risks, creating a more robust and feasible plan.
What if you could use simulation models for risk-based planning and scheduling, and accurately forecast the probability of individual order deliveries – enabling corrective action before issues become problems? A new risk-based planning and scheduling (RPS) tool goes beyond the traditional use of simulation for assessing alternative designs. Instead, it directly supports the use of models within an operational setting to improve the odds of achieving everyday production, operational, and financial targets that are key drivers to the overall success of a manufacturing operation.
This paper deals with the use of a discrete simulation tool (SIMIO) in the production system design of a cement plant. This work is intended to be relevant in the specification of a proposal of an internal logistic and monitoring weighting system (Cachapuz - SLV Cement). Through this integrated approach (Simulation, Logistic and Weighting System) it is possible to test and validate system changes and their impact in the overall system performance.
Analytics exploits information to identify patterns, create possible change scenarios, make predictions about the future, and prescribe actions based on predicted results. Analytics is the key to successfully driving changes in people, processes, and business systems. This is done by using simulation to examine alternative outcomes and scenarios not only before but also during and after implementation and execution.
During difficult economic times, companies have few positive cost reducing options that simultaneously improve operational performance. This paper addresses how Deloitte Consulting partnered with Simio LLC to model multiple process improvement opportunities for a HVAC manufacturer in order to reduce the facility’s operating costs.
It’s no secret: Simulation can predict performance, reduce risk and let you see the impact of change. Those who use it know how simulation helps projects get off the ground days – if not weeks -- earlier than imagined.
What often goes unnoticed is the over $10,000-per-day benefit of using simulation to shave time off of a project’s start date.
Simio makes modeling dramatically easier and faster by providing a new object-based paradigm that radically changes the way objects are built and used. The purpose of this paper is to describe how the object-oriented modeling framework of Simio differs from other object-oriented modeling tools.
This paper introduces Simio to users new to simulation. It is intended for the manufacturing engineer, hospital quality engineer, logistics specialist, six sigma black belt, lean system manager, etc., who would like to see how Simio can improve system performance. We discuss the use and benefits of simulation, the basic concepts of modeling, and how to get started using Simio in your decision making.
Succeeding with a technology as powerful as simulation involves much more than the technical aspects you may have been trained in. The parts of a simulation study that are outside the realm of modeling and analysis can make or break the project. This paper explores the most common pitfalls in performing simulation studies and identifies approaches for avoiding these problems.
This paper describes a new modeling system that simplifies model building through the use of intelligent objects. The intelligent objects, built by modelers, may be reused in multiple modeling projects. Although this framework is focused on object-oriented modeling, it also supports discrete event, process, and agent-based modeling to provide high flexibility in a single tool.
This paper will help experienced Arena users transition to Simio. As an experienced Arena user you have many advantages over a new user in terms of modeling experience and project management. Although Simio is very simple and powerful - that power can best be leveraged by a different approach than you are accustomed to with Arena. Learn how to exploit the full power of Simio.
Simulation enables organizations to make better decisions by letting them see the impact of proposed changes before they are implemented. Modeling is traditionally done by individuals distant from the system to be modeled. This creates a chasm between the people that know and operate the system, and those that model the system. Simulation modeling is it's most powerful when it is done by the people that understand the system, issues, and opportunities within the system.