Inquiry (E-Journal - Faculty of Business and Administration, International University of Sarajevo)
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Analysis of Shoe Manufacturing Factory By Simulation of Production Processes
Full text linkIn this study, shoe manufacturing is analyzed and specific production policy is developed for the men’s shoe making company. The main characteristic of the shoe manufacturing is that the daily production rate and processing times are highly volatile and subject to high variances depending on the model and rapidly changing trends. The aim of this study is to determine the optimum production policy over the combinations of the models which will be produced in daily working schedule. A simulation study was developed to see at what degree the variations of the models effect the throughput rate
A Stochastic Programming Approach for Multi-Period Portfolio Optimization
Full text linkpresented in this paper. The basic model involves Multi-Period decisions (portfolio optimization) and deals with the usual uncertainty of investment returns and future liabilities. Therefore, is it well suited to a stochastic programming approach. We consider the problem of rebalancing policy to accomplish some investment’s criteria. Transaction costs have also been a subject of concern in this paper. In particular, a large amount of transactions usually make asset price move in an unfavorable direction. Therefore, the first problem neglects transactions cost while the second does not
Fuzzy Multiple Objective Models For Facility Location Problems Fuzzy Multiple Objective Models For Facility Location Problems
No full textThere are a variety of efficient approaches to solve crisp multiple objective decision making problems. However in the real life the input data may not be precisely determined because of the incomplete information. This paper deals with a multi objective facility location problem using the algorithm developed by Drezner and Wesolowski
A Stochastic Programming Approach For Multi-Period Portfolio Optimization With Transaction Costs
Full text linkThis paper uses stochastic programming to solve multi-period investment problems. We combine the feature of asset return predictability with practically relevant constraints arising in a multi-period investment context. The objective is to maximize the expected utility of the returns the periods to balance the liabilities. Asset returns and state variables follow a first-order vector auto-regression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in multi-period problems, we consider short-term decisions, and incorporate a solution for the long, subsequent steady-state period to account for end effects
Preemptive Fuzzy Goal Programming in Fuzzy Environments
Full text linkThere are a variety of efficient approaches to solve crisp multiple objective decision making problems. However in the real life the input data may not be precisely determined because of the incomplete information. This paper deals with a method which can be applied to solve fuzzy multi objective production marketing problems
Distinction of The Authors of Texts Using Multilayered Feedforward Neural Networks
Full text linkThis paper proposes a means of using a multilayered feedforward neural network to identify the author of a text. The network has to be trained where multilayer feedforward neural network as a powerful scheme for learning complex input-output mapping have been used in learning of the average number of words and average characters of words in a paragraphs of an author. The resulting training information we get will be used to identify the texts written by authors. The computational complexity is solved by dividing it into a number of computationally simple tasks where the input space is divided into a set of subspaces and then combining the solutions to those tasks. By this, we have been able to successfully distinguish the books authored by Leo Tolstoy, from the ones authored by George Orwell and Boris Pasternak
Principal Component Analysis and Neural Networks for Authorship Attribution
Full text linkA common problem in statistical pattern recognition is that of feature selection or feature extraction. Feature selection refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. In this paper the data collected by counting selected syntactic characteristics in around a thousand paragraphs of each of the sample books underwent a principal component analysis performed using neural networks. Then, first of the principal components are used to distinguish authors of the texts by the use of multilayer preceptor type artificial neural networks
Principal Component Analysis for Authorship Attribution
Full text linkA common problem in statistical pattern recognition is that of feature selection or feature extraction. Feature selection refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. In this paper the data collected by counting words and characters in around a thousand paragraphs of each sample book underwent a principal component analysis performed using neural networks. Then first of the principal components is used to distinguished the books authored by a certain author
Optimization of Transport Problems with Fuzzy Coefficients
Full text linkIn this paper, we concentrate on three kinds of fuzzy linear programming problems: linear programming problems with only fuzzy technological coefficients, linear programming problems with fuzzy right-hand sides and linear programming problems in which both the right-hand side and the technological coefficients are fuzzy numbers. We consider here only the case of fuzzy numbers with linear membership functions. The symmetric method of Bellman and Zadeh [2] is used for a defuzzification of these problems. The crisp problems obtained after the defuzzification are non-linear and even non-convex in general. Finally, we give illustrative examples and their numerical solutions