Effective Methods and Transportation Processes Management Models at the Railway Transport. Textbook. Vadim Shmal
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СКАЧАТЬ Far from it, it can help, and very significantly. Firstly, with the help of this device, it is possible to successfully solve direct problems of operations research and establish what advantages and disadvantages each of the solutions has according to different criteria. The mathematical model gives us the opportunity to calculate not only the value of the main performance indicator, but also all additional ones, and the complexity of the calculation increases little. Comparison of the results of solving a set of such direct problems provides the decision maker with a certain «accumulated scientific experience». Knowing what he wins and what he sacrifices, a person can evaluate each of the decisions and choose the most acceptable for himself.

      A perplexing question may arise: what about the mathematical methods of optimization, about which he heard a lot and which he hoped so much? The trouble is that each of these methods makes it possible to find only an optimal solution for a single, scalar criterion W. Evaluate by the vector criterion (W1, W2,…) Modern mathematics does not yet know how. Indeed, not every «better» or «worse» is directly related to «more» or «less», and mathematical methods so far speak only the language of «more-less». Of all the devices known to us, so far only a person is able to make reasonable decisions not according to the scalar, but according to the vector criterion. How he does this is not clear. Maybe each time he reduces the vector to a scalar, forming some function (linear, nonlinear) from its components? Possibly, but not plausible. Most likely, when choosing a solution, he thinks not formally, but generally, instinctively assessing the situation as a whole, discarding insignificant details, subconsciously using all the experience he has, if not literally such, but similar situations. At the same time, the (informal) choice of a compromise solution can significantly help a person with a mathematical apparatus. In any case, it helps to discard in advance obviously unsuccessful solutions, which are not worth thinking about.

      Let’s demonstrate one of these methods of preliminary «rejection» of unsuccessful decisions. Let us have to make a choice between several solutions: X1, X2,…, Xn (each option is a vector, the components of which are the elements of the solution). The effectiveness of the operation is evaluated by two indicators: the productivity of P and the cost of S. The first indicator is desirable to maximize, and the second to minimize.

      Similarly, unsuitable options are discarded in the case when there are not two, but more. (With more than three of them, the geometric interpretation loses clarity, but the essence of the matter remains the same: the number of competitive solutions decreases sharply.) As for the final choice of the solution, it still remains the prerogative of man – this unsurpassed «master of compromise».

      However, the procedure for choosing the final solution, being repeated repeatedly, in different situations, can serve as the basis for which convenient formalization. We are talking about the construction of so-called «heuristic methods» of decision-making. Such methods are widely used in attempts to automate the solution of some informal tasks. For example, in order to force the automaton to solve difficult-to-formalize tasks (for example, reading handwritten text, recognizing images or sounds of live speech), so-called training automata are created. The program according to which such a machine works is not laid down in it in advance, but is formed gradually, in the process of familiarization with an increasingly wide range of situations. The initial model for the machine is an experienced person who knows how to perform an informal task, say, to make a decision according to a vector criterion. Subsequently, there may be further improvement of the program (already in the order of «self-study»).

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