There have been two basic computer-based approaches to addressing the scheduling problem. The first is Constraint-based Schedule Optimization (CSO) where the scheduling problem is formulated as a set of constraints that must be satisfied along with an objective (e.g. minimize number of late jobs, or maximize throughput). The mathematical formulation is then "solved" using a Constraint Programming (CP) heuristic algorithm (sometimes called a CP solver). The CP solver uses heuristic rules to search for candidate solutions that satisfy the constraints and improve the objective. An example of this approach is scheduling solutions based on the IBM ILOG CPLEX CP Optimizer or the SAP APO-PP/DS module. Although this approach may work well for some select applications, it is limited by the size and complexity of the problem that it can handle. The solver may also require a long time to generate a good solution; often the problem must be simplified by assuming away details in order to keep the size and complexity manageable for the CP solver.
The second approach is Simulation-based Schedule Optimization (SSO) where we use dynamic decision rules and simulate the movement of jobs through a model of the facility to construct a feasible schedule. There are a number of user-selectable heuristic optimizing rules that are used within the simulation to create a good schedule. The heuristic optimizing rules typically focus on either maximizing throughput, or minimizing the number of late jobs. The advantage of this approach is that the SSO scheduling system is typically substantially easier to implement, can more flexibly capture the facility constraints, and can generate a schedule much faster. The Simio RPS solution also provides a flexible framework based on Microsoft's .NET technology for incorporating custom optimizing rules into the simulation model.
Speed of schedule generation is a critical feature for any scheduling tool. When unexpected things happen in the system (machines break, material arrives late, etc.) it's important to be able to quickly generate a new schedule that reflects the change. This is an area where the SSO tools have a significant advantage over CSO tools because of their speed of execution. Simio Enterprise Edition, incorporating RPS, is built on the highly efficient Simio simulation engine that can generate a large schedule in less than a minute. Simio also provides a unique capability to employ multiple processors to quickly generate a corresponding risk analysis based on multiple replications of the model.
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How Risk-based Planning and Scheduling to help you drive more revenue while reducing risk and costs.
Drive more revenue at reduced cost by generating schedules that account for risk and uncertainty.
Expose hidden and unnecessary cost and time by accurately modeling critical constraints.
Quickly react to changes and update schedules in response to unpanned events.
View schedules using interactive Gantt charts with companion tools to expose the root cause for non-value-added time.
Mitigate schedule risk early while avoiding cost with unique insights provided by 3D animation.
Use Simio's rapid modeling environment and flexible data interfaces to quickly implement a cost-effective solution.