Resource Optimization Technique examples

The SNP optimizer is based on (mixed-integer) linear programming (MILP) techniques. For a general introduction into MILP we refer to [11], An SAP APO user has no access to the mathematical MILP model. Instead, the modeling is done in notions of master data of example products, recipes, resources and transportation lanes. Each master data object corresponds to a set of constraints in the mathematical model used in the optimizer. For example, the definition of a location-product in combination with the bucket definition is translated into inventory balance constraints for describing the development of the stock level over time. Additional location-product properties have further influence on the mathematical model, e.g., whether there is a maximum stock-level for a product or whether it has a finite shelf-life. For further information on the master data expressiveness of SAP SCM we refer to [9],... 

Virtual screening applications based on superposition or docking usually contain difficult-to-solve optimization problems with a mixed combinatorial and numerical flavor. The combinatorial aspect results from discrete models of conformational flexibility and molecular interactions. The numerical aspect results from describing the relative orientation of two objects, either two superimposed molecules or a ligand with respect to a protein in docking calculations. Problems of this kind are in most cases hard to solve optimally with reasonable compute resources. Sometimes, the combinatorial and the numerical part of such a problem can be separated and independently solved. For example, several virtual screening tools enumerate the conformational space of a molecule in order to address a major combinatorial part of the problem independently (see for example [199]). Alternatively, heuristic search techniques are used to tackle the problem as a whole. Some of them will be covered in this section. 

The blending of different polymers is a frequently used technique in industrial polymer production to optimize the material s properties. The biodegradable polymer poly(3-hydroxybutyrate) (PHB) [45, 46], for example, which can be produced by bacteria from renewable resources, has the disadvantage of being stiff and brittle. The mechanical properties of PHB, however, can be readily enhanced by blending with another biopolymer, poly(lactic acid) (PLA) [47]. In order to prepare the optimum blend, it must be noted that the miscibility of different polymers depends on their concentration, the temperature, and their structural characteristics [48]. 

In many analytical techniques the response of the measurement system depends on a variety of experimental factors under the control of the operator. For example, enzyme assays involve the direct or indirect measurement of reaction rates. In a given experiment the reaction rate will depend on factors such as the temperature, the pH, ionic strength, and chemical composition of the buffer solution, the enzyme concentration, and so on. For a particular application it will be important to set the levels of these factors to ensure that (for example) the reaction rate is as high as possible. The process of finding these optimum factor levels is known as optimization. Several methods of optimization are discussed in detail in subsequent sections. But before an optimization process can begin we must determine which factors, and which interactions between them, are important in affecting the response it is also valuable to know which factors have little or no effect, so that time and resources are not wasted on unnecessary experiments. 

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