Clustering problems in optimization models

In the article, the author considers the problems of complex algorithmization and systematization of approaches to optimizing the work plans of construction organizations (calendar plans) using various modern tools, including, for example, evolutionary algorithms for "conscious" enumeration of options for solving a target function from an array of possible constraints for a given nomenclature. Various typical schemes for modeling the processes of distribution of labor resources between objects of the production program are given, taking into account the array of source data. This data includes the possibility of using the material and technical supply base (delivery, storage, packaging) as a temporary container for placing the labor resource in case of released capacity, quantitative and qualification composition of the initial labor resource, the properties of the construction organization as a counterparty in the contract system with the customer of construction and installation works etc. A conceptual algorithm is formed that is the basis of the software package for operational harmonization of the production program ( work plans) in accordance with the loading of production units, the released capacities of labor resources and other conditions stipulated by the model. The application of the proposed algorithm is most convenient for a set of objects, which determines the relevance of its implementation in optimization models when planning production programs of building organizations that contain several objects distributed over a time scale.

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Inventory Optimization Models of Equipment Spare Parts Based on Computer Simulation

2018 3rd International Conference on Computer Science and Information Engineering (ICCSIE 2018) ◽

10.23977/iccsie.2018.1067 ◽

2018 ◽

Keyword(s):

Computer Simulation ◽

Spare Parts ◽

Optimization Models ◽

Inventory Optimization

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Combinatorial Optimization of Clustering Decisions: An Application to Refine Psychiatric Diagnoses

10.31234/osf.io/jpnmf ◽

2018 ◽

Author(s):

Jordan Stevens ◽

Douglas Steinley ◽

Cassandra L. Boness ◽

Timothy J Trull ◽

[email protected] ◽

...

Keyword(s):

Combinatorial Optimization ◽

Alcohol Use Disorder ◽

Point Of View ◽

Psychiatric Diagnoses ◽

Optimal Solutions ◽

Specific Function ◽

Significant Information ◽

Diagnosis Group ◽

Clustering Problems ◽

Unique Application

Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is permuted, optimization on some predefined, user-specific function can occur. We apply this technique to optimizing the diagnosis of Alcohol Use Disorder. This is a unique application, from a clustering point of view, in that the decision rule for clustering observations into the diagnosis group relies on both the set of items being considered and a predefined threshold on the number of items required to be endorsed for the diagnosis to occur. In optimizing diagnostic rules, criteria set sizes can be reduced without a loss of significant information when compared to current and proposed, alternative, diagnostic schemes.

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COMPARISON OF TWO OPTIMIZATION MODELS FOR CONSTRUCTING PATTERNS IN THE METHOD OF LOGICAL ANALYSIS OF DATA

СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ ◽

10.36622/vstu.2020.95.50.002 ◽

2020 ◽

pp. 10-13

Author(s):

Р.И. Кузьмич ◽

А.А. Ступина ◽

С.Н. Ежеманская ◽

А.П. Шугалей

Keyword(s):

Objective Function ◽

Optimization Model ◽

Logical Analysis ◽

Optimization Models ◽

Logical Analysis Of Data

Предлагаются две оптимизационные модели для построения информативных закономерностей. Приводится эмпирическое подтверждение целесообразности использования критерия бустинга в качестве целевой функции оптимизационной модели для получения информативных закономерностей. Информативность, закономерность, критерий бустинга, оптимизационная модель Comparison of two optimization models for constructing patterns in the method of logical analysis of data Two optimization models for constructing informative patterns are proposed. An empirical confirmation of the expediency of using the boosting criterion as an objective function of the optimization model for obtaining informative patterns is given.

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