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1

Information management in passenger traffic supporting system design as a multi-criteria discrete optimization task

Galuszka A., Krystek J., Swierniak A., Lungoci C., Grzejszczak T.

Archives of Control Sciences

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2017

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Vol. 27, no. 2

229--238

EN

This paper presents a concept of an Integrated System of Supporting Information Management in Passenger Traffic (ISSIMPT). The novelty of the system is an integration of six modules: video monitoring, counting passenger flows, dynamic information for passengers, the central processing unit, surveillance center and vehicle diagnostics into one coherent solution. Basing on expert evaluations, we propose to present configuration design problem of the system as a multi-objectives discrete static optimization problem. Then, hybrid method joining properties of weighted sum and ε-constraint methods is applied to solve the problem. Solution selections based on hybrid method, using set of exemplary cases, are shown.

2

Polioptymalizacja rozmyta w MATLAB'ie

Tarnowski W.

Pomiary Automatyka Kontrola

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2011

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R. 57, nr 9

1059-1062

PL

Skutecznym sposobem wdrożenia optymalizacji do praktyki może być wykorzystanie logiki rozmytej do tworzenia modeli matematycznych zadania optymalizacji, jeśli jest dostępna wiedza ekspercka dotycząca obiektu optymalizacji. Dzięki temu: a) unika się żmudnego procesu budowania modelu analitycznego i b) umożliwia się uwzględnienie w modelu rozmytych kryteriów i ograniczeń. W pracy pokazano procedurę wspomagania optymalizacji za pomocą pakietu MATLAB na przykładzie z dwoma i z trzema kryteriami optymalizacji, w ujęciu polioptymalizacyjnym. Wyciągnięto wnioski metodologiczne.

EN

As for now, optimization techniques are not very popular in real engineering, though they are taught at universities and are recognized as very useful in design and management. Probably the main reason is that mathematical models of objects are difficult to device and, what more criteria may not be precisely defined. A way to enhance an effective implementation of optimisation and MADM techniques to a real practice is a fuzzy logic approach to modelling an optimisation problem, if only the expert knowledge is at hand. It yields: a) avoiding the laborious and often not possible process of building an analytical model, b) makes it possible to use fuzzy and imprecise notions and aspects. In the paper there is proposed a procedure how to device and handle such models in the MATLAB environment to get a Pareto set solutions, in poly-optimisation. The methodology is illustrated on an example of a chemical reactor. The poly-optimal control is to be found. First, having stated criteria, as fuzzy and/or non-fuzzy notions (product quality and effectiveness, in the example), an expert arbitrarily defines decision variables (process temperature, time and mixing velocity), and their membership functions, then a mathematical model is established as a set of rules if - then (Fig. 1). This model may be intuitively verified and corrected by graphical presentations, as in Fig. 2. Then, a poly-optimisation is completed: either by survey of all possible solutions (Fig. 3) and reducing the set to the Pareto solutions (Figs. 4 - 6), or by mathematical optimization algorithms. In the example a first approach is adopted. The example is extended: a third non-fuzzy criterion is introduced (cost). (Figs. 7 - 9).Methodological conclusions are formulated, too.

3

Advances in cone-based preference modeling for decision making with multiple criteria

Wiecek M. M.

Decision Making in Manufacturing and Services

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2007

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Vol. 1, no. 1-2

153-173

EN

Decision making with multiple criteria requires preferences elicited from the decision maker to determine a solution set. Models of preferences, that follow upon the concept of nondominated solutions introduced by Yu (1974), are presented and compared within a unified framework of cones. Polyhedral and nonpolyhedral, convex and nonconvex, translated, and variable cones are used to model different types of preferences. Common mathematical properties of the preferences are discussed. The impact of using these preferences in decision making is emphasized.

4

On Teaching of Fundamentals of Optimization in Designing

Pawłowski J. J.

Machine Dynamics Problems

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2004

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Vol. 28, No. 4

77-82

EN

The report on the lectured subject Fundamentals of Optimization in Designing contains the description of the methods of teaching, computer optimization programs and examples of exercises. Lectures are to convince students that optimization methods can be used at every stage of a process of designing.