Resource Optimization Technique

It s easier to hgure out where available resources can be employed to improve a system or solve a problem (also using Resource Optimization, Technique 12). 

Linear programming is one of the most common optimization techniques applied. LPs are commonly used on production scheduling and resourcing problems. A linear program is a class of optimization problems where the objective function and constraints are linear. The objective function and constraints of a linear program are convex therefore, a local optimum is the global optimum. In addition, LPs demonstrate the characteristic wherein the optimum solutions of LPs lie on a constraint... 

Use resource optimization when you need to come up with solution ideas that provide higher value than those in existence today—or when you need to refine and optimize a specific solution design. The key is to make sure you list as many resources as possible within and outside your immediate system or sphere of focus. After this, you can use any number of idea-generation techniques to figure out how your available resources can be applied to your inventive problem. 

Pressurized Water Extraction Resources and Techniques for Optimizing Analytical Applications... 

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],... 

Due to the campaign structure, the existing decomposition techniques in the SNP optimizer like time decomposition and product decomposition are not applicable. For problems with this structure it is possible to use the resource decomposition in case a good sequence of planning of the campaign resources can be derived. However, in our case, problem instances could be solved without decomposition on a Pentium IV with 2 GHz in one hour to a solution quality of which the objective value deviates at most one percent from the optimal objective function value. 

Like any businesses, bioanalytical laboratories perform operations that transform starting materials (samples and supplies) into products of higher value (quality reports continuing accurate sample concentration data). To maximize productivity and stay ahead of competition, bioanalytical scientists continuously invent, reinvent, and implement processes and techniques that generate more accurate and better quality reports with fewer resources (labor, time, capital, energy, and consumable goods). These continuous optimizations of laboratory operations drove the bioanalytical laboratories to begin... 

Each screening center has medicinal and synthetic chemistry expertise in order to optimize hits identified from HTS campaigns and develop them into chemical probes. Specific capabilities vary, however typical strategies employed include parallel synthesis, computational and informatics analysis, and analytical capabilities such as LC/MS techniques. The structures of novel compounds that are prepared, their synthetic protocols, analytical data and biological data are all available, and samples of final probes developed are deposited into the MLSMR. A Working Group comprised of chemists from each center meets regularly to share information, best practices, and insure optimal use of resources. 

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. 

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