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1.
A decision-making model for controlling logistics costs
Sebastjan Škerlič, Robert Muha, Klavdij Logožar, 2016, izvirni znanstveni članek

Opis: A lack of understanding of logistics costs, particularly in terms of their integrity and interdependence, allows for the interests of individual departments within a company, which are not always in line with the common interests of the business process of the company, to take precedence over necessary logistics cost planning. In order to address this problem, we have developed a model that takes into account all the logistical costs within a company and represents an important decision-making tool that can be used by employees at different levels of the organization, covering different business functions. We have tested the model on a specific product in a company that operates in the automotive industry and determined that the model enables us to have greater control over logistics costs and improves the quality of information on logistics costs.
Ključne besede: decision making, knowledge transfer, logistics cost, logistics cost models
Objavljeno v DKUM: 11.07.2017; Ogledov: 1394; Prenosov: 406
.pdf Celotno besedilo (622,13 KB)
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2.
Environmental management of business processes
Vesna Čančer, 2000, izvirni znanstveni članek

Opis: Since the decision-makers in enterprises will accept the goals of environmental management only if they are motivated enough, comprehensible and useful tools should be generated to support environmentally oriented business decision- making. For that reason, a general optimisation model of the multiphase business process is presented in this paper. This model includes the possibilities for an integrated approach to environmental protection so that it can be applied as a scenario by the business process simulation for the evaluation of environmentally oriented business decisions on business performance. Furthermore, development and application possibilities of the presented model are introduced. Some measures of resource efficiency are developed using the presented optimisation model.
Ključne besede: management, environment, company, decision making, environmental protection, business, company business, optimization, business process, models
Objavljeno v DKUM: 04.07.2017; Ogledov: 1199; Prenosov: 129
.pdf Celotno besedilo (647,00 KB)
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Comprehensive decision tree models in bioinformatics
Gregor Štiglic, Simon Kocbek, Igor Pernek, Peter Kokol, 2012, izvirni znanstveni članek

Opis: Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did notexpected significant differences in classification performance, the resultsdemonstrate a significant increase of accuracy in less complex visuallytuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumptionthat the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes anda high number of possibly redundant attributes that are very common in bioinformatics.
Ključne besede: decision tree models, machine learning technique, visual tuning, bioinformatics
Objavljeno v DKUM: 05.06.2012; Ogledov: 2170; Prenosov: 337
.pdf Celotno besedilo (524,39 KB)
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An alternative sorting procedure for interactive group decision support based on the pseudo-criterion concept
Andrej Bregar, József Györkös, Ivan Rozman, 2003, izvirni znanstveni članek

Opis: An original interactive procedure is proposed, which aims at overcoming some of the major weaknesses of existent pseudocriterion based methods for group decision analysis. It refers to absolute judgements of feasible alternatives and is focused on complementary activities of opinion elicitation and robustness analysis. As a foundation, four interdependent principles are introduced - problem localization, interactivity on the basis of aggregation/disaggregation approach, automatic derivation of criteria weights according to selective effects of veto tresholds, and group consensus seeking. The principles are grounded and realized by appropriate methodological solutions.
Ključne besede: decision analysis, pseudo-criterion concept, decision models, group consensus, alternative sorting, group work, fuzzy relation
Objavljeno v DKUM: 01.06.2012; Ogledov: 1750; Prenosov: 92
.pdf Celotno besedilo (216,38 KB)
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