Planning model types

Material flow and resource allocation can be defined by time, duration, type and quantity in the planning model. They describe a definite (by best current knowledge) change of the planning model in the future. In addition to this there are a number of fuzzy information data that have to be included in the planning model but are only weak assumptions about the future planning situation. These include, e.g., planned orders and planned independent requirements. 

These simple examples can only show the opportunity to further extend the value chain planning model usage for decision support integrated in simulation-based optimization architecture. There is an opportunity for further industry-oriented research to better understand production-price dynamics in different types of value chain networks. 

Resource Type - ATP allocation models must potentially deal with three resource types material resources, factory resources, and distribution resources. Figure 11.3 illustrates the variety of combinations that could exist in different applications. These directly impact the nature of models required. For example, in some cases the factory resource must be modeled in detail and the ATP problem can be modeled as a type of factory scheduling problem, while in other cases, its distribution planning models are appropriate. 

In some optimization problems, the model cannot be fiiUy specified because it depends on quantities that are unknown at the time of formulation. This characteristic is shared by many economic and financial planning models, which may depend for example on fumre interest rates, future demands for a product, or future commodity prices, but uncertainty can arise naturally in almost any type of application. 

The amount of detail input, and the type of simulation model depend upon the issues to be investigated, and the amount of data available. At the exploration and appraisal stage it would be unusual to create a simulation model, since the lack of data make simpler methods cheaper and as reliable. Simulation models are typically constructed at the field development planning stage of a field life, and are continually updated and increased in detail as more information becomes available. 

To estimate tlie potential iiupaet on tlie publie or tlie environment of aeeidents of different types, the likely emergeney zone must be studied. For example, a liazardous gas leak, fire, or explosion may eause a toxie cloud to spread over a great distance. The minimum atmospheric dispersion model. Vtirious models can be used tlie more difficult models produce more realistic results, but tlie simpler and faster models may provide adequate data for planning purposes. A more tliorough discussion of atmospheric dispersion is presented in Part 111 - Healtli Risk Assessment. 

Statistical and algebraic methods, too, can be classed as either rugged or not they are rugged when algorithms are chosen that on repetition of the experiment do not get derailed by the random analytical error inherent in every measurement,i° 433 is, when similar coefficients are found for the mathematical model, and equivalent conclusions are drawn. Obviously, the choice of the fitted model plays a pivotal role. If a model is to be fitted by means of an iterative algorithm, the initial guess for the coefficients should not be too critical. In a simple calculation a combination of numbers and truncation errors might lead to a division by zero and crash the computer. If the data evaluation scheme is such that errors of this type could occur, the validation plan must make provisions to test this aspect. 

---