Logistic problem analysis

This classification of bonds allowed the application of logistic regression analysis (LoRA), which proved of particular benefit for arriving at a function quantifying chemical reactivity. In this method, the binary classification (breakable or non-breakable, represented by 1/0, respectively) is taken as an initial probability P0, which is modelled by the following functional dependence (Eqs. 7 and 8) where f is a linear function, and x. are the parameters considered to be relevant to the problem. The coefficients c. are determined to maximize the fit of the calculated probability of breaking (P) as closely as possible to the initial classification (P0). 

This activity is not necessarily a trivial task and the availability of spare parts and tools must be co-ordinated with the failure rates of the equipment to which they apply. Space does not permit a detailed discussion of these problems, which are the topic of Logistic Support Analysis (LSA) further information can be found in MIL-HDBK-472 [1], in Lyonnet [13], and in Blanks [14]. 

A corollary to the mechanisms of cell-death execution is that a dying cell does not need to reach a stable apoptotic state to be dead. This creates a logistical problem for many standard dynamical-systems approaches (nullclines, bifurcation analysis, etc.) that are based on steady-state solutions to the governing equations. Aldridge and coworkers applied finite-time approaches (that do not require d/df = 0) to define the separation of cellular trajectories in a model of caspase activation (Aldridge et al. 2006). These types of approaches may be particularly relevant for other irreversible cell-fate choices, such as during the mitotic-spindle checkpoint. 

The descriptive, classificatory and comparative approach to analysis of structures also depends on computing. It is no longer feasible to pursue these studies with physical models. Beyond the obvious mechanical problems, there is the logistical catastrophe the amount of space and materiel increases linearly with the number of structures to be examined. In addition, there is no way to save and restore structures. It is therefore necessary to apply computer graphics to draw representations of structures. The importance of supercomputers in studies of this kind will lie not only in the feasibility of large calculations that are rot possible at present, but in the conversion of many tasks from batch mode to interactive. 

ABSTRACT Reliability analysis of complex systems is complicated by several factors. The possible unreliability of logistic support elements may lead to decrease of performance of the system being supported. As a result both systems must be considered in a single model. However, the simultaneous setting of all structural parameters (e.g. redundancy, repair shop capacity) and control variables (e.g. spare part inventory levels, maintenance policy parameters, repair job priorities, time redundancy) is mathematically a hard problem. That is why this paper describes FTTD modeling technique for handling these difficulties. Moreover, the application example of tram network performance is described. Fault tree with time dependencies model for the presented example and its analysis is discussed. 

Parlar, M. 1988. Game theoretic analysis of the substitutable product inventory problem with random demands. Naval Research Logistics, Vol.35,397-409. 

Parlar, M., Game Theoretic Analysis Of The Substitutable Product Inventory Problem With Random Demands, Naval Research Logistics, 35 (1988), 397-409. 

The case studies in this chapter have shown that algorithms based on the methods depicted in this chapter are able to solve variant problems from various states in the product lifecycle that could not be solved without these. As a consequence, powerful analyzing tools exist that completely and precisely verily complex product models with respect to a diversity of properties involving variants. Furthermore, we expect that models are able to express variant information from other states in the product life cycle and the relations between the states as shown in Fig. 17.9. Examples of other states in the product life cycle are the requirement and market analysis, design, construction, production, logistics, sales and after-sales. 

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