Decision level

One approach reUes heavily on heuristics but allows the engineer to interact during the synthesis procedure through a framework of hierarchical decision levels (58,59). 

A hierarchical design procedure for process synthesis can be used in conjunction with a flow-sheeting program to analyze, evaluate, and optimize the options (60). The emphasis is on starting with the simplest possible models that will give answers to a particular question quickly so that the questions to be asked at the next decision level can be formulated. At each stage, it is necessary to ensure that the level of detail in the model is sufficient to give rehable information. 

Another type of time-weighted average exposure limit is the decision level (DL), which is expressed as a fraction of the OEL. In general, it is based on judgment, and it is greater than a dose of 50% and usually corresponds to one-fourth of the dose. For special substances, such as carcinogens, it should... 

The initial aim of the procedure is to generate a reasonable base case design that can be used for preliminary economic evaluation of the process. This can subsequently be optimized and/or compared with any process alternatives that are identified. The complete process is always considered at each decision level, but additional fine detail is added to the structure of the flowsheet at any stage. Established heuristics and equipment selection procedures are used together with new process synthesis insights to guide each flowsheet decision. 

A new tumor marker is evaluated using the same criteria used for many diagnostic tests (i.e., sensitivity, specificity, and accuracy). The diagnostic sensitivity and specificity are best represented by a receiver operating characteristic (ROC) curve. The ROC curve is constructed with the true-positive rate versus false-positive rate at various decision levels. As a test improves in its diagnostic performance, it shifts upward and to the left as the true-positive rate increases and the false-positive rate decreases. 

The comparison of the effectiveness of different tumor markers (old versus new) for a given malignancy should be carried out using split samples from both patients and healthy control subjects (Table 4). Several different decision levels could be used to establish an interpretive scale instead of using a single point. How to establish the decision levels is described in the following ... 

The decision level calculated on the basis of the marker levels found in patients with benign disease—any level above this value should indicate the presence or probability of disease that is not necessarily the target malignancy. 

These decision levels calculated on the basis of marker levels found in patients with confirmed advanced malignancies should indicate a very high probability of the presence of malignancy. 

It finally may be stated that the use of discrete-event simulation on different decision levels even though state-of-the-art is still slightly underrepresented in the process industry. However, since the technology has proven itself in an... 

The top-down approach, which defines appropriate hierarchical coordination mechanisms between the different decision levels and decision structures at each level. These structures force constraints on lower operating levels and require heuristic decision rules for each task. Although this approach reduces the size and complexity of scheduling problems, it potentially introduces coordination problems. 

A note concerning terminology Lp (Ref. 2) and Sp have been used interchangeably to denote the detection limit for the net signal (y-B) xp is used here to denote the analyte detection limit (concentration or amount). Lq (or Sc or xq) denotes the decision level it is also called the critical point or level, test level, or threshold by various authors. The directly observed gross signal (y) is here referred to as the response.]... 

In the text which follows we shall examine in numerical detail the decision levels and detection limits for the Fenval-erate calibration data set ( set-B ) provided by D. Kurtz (17). In order to calculate said detection limits it was necessary to assign and fit models both to the variance as a function of concentration and the response (i.e., calibration curve) as a function of concentration. No simple model (2, 3 parameter) was found that was consistent with the empirical calibration curve and the replication error, so several alternative simple functions were used to illustrate the approach for calibration curve detection limits. A more appropriate treatment would require a new design including real blanks and Fenvalerate standards spanning the region from zero to a few times the detection limit. Detailed calculations are given in the Appendix and summarized in Table V. 

Decision levels and detection limits are relatively easy to define and evaluate for simple" (zero dimensional) measurements. The transition to higher dimensions and multiple components introduces a number of complications and added assumptions related to the number and identity of components, shapes and parameters of calibration functions and spectra, and distributional consequences of non-linear estimation. 

As with Case-e, the response decision level is pre-fixed ... 

Two main decision levels, the level of plants and the level of customers, are considered in the Provimi Pet Food supply chain problem. Customers have certain demands for products which can be produced in the plants. The production process is modeled as being consisted of two consecutive steps. Semi-products are produced in production lines. These semi-products are subsequently mixed and packaged in packaging lines. This packaged product is the final product of the plants, and is transported to the customers. The semi-products can be transported between plants, i.e. a packaging line can package semi-products produced in another plant. 

Integrating strategic, tactical and operational supply chain decision levels in a model predictive control framework... 

In this work an MILP model which achieves the integration of all three Supply Chain (SC) decision levels is developed. Then, the stochastic version of this integrated model is applied as the predictive model in a Model Predictive Control (MPC) framework in order to incorporate and tackle unforeseen events in the SC planning problem in chemical process industries. Afterwards, the validation of the proposed approach is justified and the resulting potential benefits are highlighted through a case study. The results obtained of this particular case study are analyzed and criticized towards future work. 

Keywords supply chain optimization, decision levels, MILP, model predictive control. 

Many factors play a role at this decision level. Type of kinetics, operational stability of the biocatalyst, form of biocatalyst, type of bioreactor, bioreactor operating costs, necessity of process control, compatibility with down-stream processing, tonnage or scale of operation, existing facilities and experience, feedstock, type of product, GMP, and others, are all involved in an intricate... 

Note that the molar concentration values are relevant to decision levels where accurate measurement is crucial for medical decision. The category of synthetic-type reference material sera needs to be certified by means of primary methods. According to international regulations [6], the methods of certification of the clinical reference materials followed several steps regarding the preparation of the material, homogeneity testing, performance of interlaboratory analyses, assignment of the certified value and uncertainty. 

Analyte Decision Level, Xc Acceptable Performance, CLIA 88 Precision Goals (Maximum SD) Xc X CLIA/4 Fraser Fixed-Limit Goals (Maximum Total Error) CLIA 88... 

We consider here decision levels of 3 and 6mmol/L and suppose in the present example that the SDa is 0.09mmol/L, which corresponds to a CVa of 2% at the mean (4.5 mmol/L) of the considered range. In the present example we set the random bias component to zero. Thus the systematic difference that should be detected is ... 

This corresponds to a requirement for detecting oCq equal to 0.35mmol/L> if the systematic difference is ascribed to an intercept deviation. Relating the systematic difference to a slope deviation results in a demand of detecting P=1.12 (3.35/3) or 0.88. Similarly, at the upper decision level of Xl argetc = 6 mmol/L, we have again the limits 0,35 mmol/L for detection of tto, but now the demand for detecting a slope deviation has been sharpened to p = 1.06 (6.35/6) or 0.94. 

Other decision levels might also be considered (e.g., the nonfasting plasma glucose concentration limit of 2000mg/L). In this proportional error model, the standardized slope deviation to be detected is the same at aU decision levels, but the requirement for intercept detection varies with the concentration, so that the most demanding situations occur at low concentrations. 

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