Incomplete Input Data Research Articles

Comparative Analysis of Human and Artificial Intelligence Planning in Production Processes

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.

Towards end-to-end structure determination from x-ray diffraction data using deep learning

Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) patterns. Since many materials are readily available as crystalline powder, powder crystallography is of growing usefulness to many fields. However, powder crystallography does not have an analytically known solution, and therefore the structural inference typically involves a laborious process of iterative design, structural refinement, and domain knowledge of skilled experts. A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning, while capturing the ambiguities around molecule orientation, symmetries, and reconstruction resolution. Here we present an ML approach for structure determination from powder diffraction data. It works by estimating the electron density in a unit cell using a variational coordinate-based deep neural network. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity (as measured by structural similarity index) with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples, paving the way to reconstruction of yet unresolved nanostructures.

Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset

This article introduces an approach to diagnose heart diseases utilizing the K-Nearest Neighbor algorithm and diverse correlation filters for selecting the most pertinent attributes. Results high- light that meticulous filter selection enhances survival predictions in patients with heart diseases. Employing K = 5 and correlation filter CF = 0.1, key attributes for classification were identified as anemia, high blood pressure, serum creatinine, and sex. Omitting the 'time' attribute led to information loss but was crucial to prevent biases and generalize predictions across various clinical scenarios. Utilizing these classification parameters, we designed an Android mobile application called “Heart Info System”, functioning as an artificial intelligence service. It employs the K-Nearest Neighbor algorithm with optimal parameters to evaluate the probability of survival in the progression of heart disease. The main activity of the application retrieves data from a Firebase database. While the study results show promise, the accuracy of the application may be influenced by inaccurate or incomplete input data. Nevertheless, this application has the potential to improve the early detection of heart diseases, paving the way for life-saving interventions.

Evaluation of the Suitability of Using Artificial Neural Networks in Assessing the Effectiveness of Greywater Heat Exchangers

The use of greywater heat exchangers (GHEs) is an effective way to reduce energy consumption for heating domestic water. However, the available characteristics of this type of device are often insufficient and consider only a few selected parameters of water and greywater, which results in the need to look for tools enabling the determination of the effectiveness of GHEs in various operating conditions with incomplete input data. The aim of this paper was to determine the usefulness of artificial neural networks (ANNs). For this purpose, comprehensive experimental tests were carried out on the effectiveness of the horizontal heat exchanger, taking into account a wide range of water and greywater flow rates and temperatures of these media, as well as the linear bottom slope of the unit, which allowed for the creation of a database of 32,175 results. Then, the feasibility of implementing the full research plan was assessed using ANNs. The analysis showed that the impact of the media temperatures on the heat exchanger effectiveness values obtained using ANNs is limited, which makes it possible to significantly reduce the number of necessary experiments. Adopting only three temperature values of at least one medium allowed the generation of ANN models with coefficient values R2 = 0.748–0.999 and RMSE = 0.077–1.872. In the case of the tested GHE, the slope and the flow rate of the mixed water are of key importance. However, even in the case of parameters of significant importance, it is possible to reduce the research plan without compromising the final results. Assuming five different values for each of the four input parameters (a total of 625 combinations) made it possible to generate an ANN model (R2 = 0.993 and RMSE = 0.311) with high generalization ability on the full research plan covering 32,175 cases. Therefore, the conducted analysis confirmed the usefulness of ANNs in assessing the effectiveness of GHEs in various operating conditions. The approach described in this paper is important for both environmental and economic reasons, as it allows for reducing the consumption of water and energy, which are necessary to carry out such scientific research.

Gradient-Guided Modality Decoupling for Missing-Modality Robustness

Multimodal learning with incomplete input data (missing modality) is very practical and challenging. In this work, we conduct an in-depth analysis of this challenge and find that modality dominance has a significant negative impact on the model training, greatly degrading the missing modality performance. Motivated by Grad-CAM, we introduce a novel indicator, gradients, to monitor and reduce modality dominance which widely exists in the missing-modality scenario. In aid of this indicator, we present a novel Gradient-guided Modality Decoupling (GMD) method to decouple the dependency on dominating modalities. Specifically, GMD removes the conflicted gradient components from different modalities to achieve this decoupling, significantly improving the performance. In addition, to flexibly handle modal-incomplete data, we design a parameter-efficient Dynamic Sharing (DS) framework which can adaptively switch on/off the network parameters based on whether one modality is available. We conduct extensive experiments on three popular multimodal benchmarks, including BraTS 2018 for medical segmentation, CMU-MOSI, and CMU-MOSEI for sentiment analysis. The results show that our method can significantly outperform the competitors, showing the effectiveness of the proposed solutions. Our code is released here: https://github.com/HaoWang420/Gradient-guided-Modality-Decoupling.

Amplidiff: an optimized amplicon sequencing approach to estimating lineage abundances in viral metagenomes

BackgroundMetagenomic profiling algorithms commonly rely on genomic differences between lineages, strains, or species to infer the relative abundances of sequences present in a sample. This observation plays an important role in the analysis of diverse microbial communities, where targeted sequencing of 16S and 18S rRNA, both well-known hypervariable genomic regions, have led to insights into microbial diversity and the discovery of novel organisms. However, the variable nature of discriminatory regions can also act as a double-edged sword, as the sought-after variability can make it difficult to design primers for their amplification through PCR. Moreover, the most variable regions are not necessarily the most informative regions for the purpose of differentiation; one should focus on regions that maximize the number of lineages that can be distinguished.ResultsHere we present AmpliDiff, a computational tool that simultaneously finds highly discriminatory genomic regions in viral genomes of a single species, as well as primers allowing for the amplification of these regions. We show that regions and primers found by AmpliDiff can be used to accurately estimate relative abundances of SARS-CoV-2 lineages, for example in wastewater sequencing data. We obtain errors that are comparable with using whole genome information to estimate relative abundances. Furthermore, our results show that AmpliDiff is robust against incomplete input data and that primers designed by AmpliDiff also bind to genomes sampled months after the primers were selected.ConclusionsWith AmpliDiff we provide an effective, cost-efficient alternative to whole genome sequencing for estimating lineage abundances in viral metagenomes.

Relevance vector machine with hybrid kernel-based soft sensor via data augmentation for incomplete output data in sintering process

A ratio of CO and CO2 (CO/CO2) is a key indicator of sintering carbon consumption, which is difficult to be determined in real-time. Therefore, the establishment of its soft sensing model is of great practical significance. This paper proposes a novel CO/CO2 soft sensing model with incomplete output data based on relevance vector machine with hybrid kernel via data augmentation. First, a least absolute shrinkage and selection operator is employed for determining key input variables of the model, and an automatic fuzzy clustering framework is used to automatically identify multiple operating modes. Then, a relevance vector machine with hybrid kernel method is presented to model each operating mode separately. Meanwhile, considering the problem of incomplete input and output data, data augmentation is applied in modeling to enhance the model performance. Finally, the soft sensing model of CO/CO2 is formed. Experimental results and analyses using actual production data coming from the sintering production process demonstrate that the prediction performance and accuracy of the proposed model outperform some existing algorithms.

Data-based bipartite formation control for multi-agent systems with communication constraints.

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.

Multi-Modal Brain Tumor Data Completion Based on Reconstruction Consistency Loss.

Multi-modal brain magnetic resonance imaging (MRI) data has been widely applied in vison-based brain tumor segmentation methods due to its complementary diagnostic information from different modalities. Since the multi-modal image data is likely to be corrupted by noise or artifacts during the practical scanning process, making it difficult to build a universal model for the subsequent segmentation and diagnosis with incomplete input data, image completion has become one of the most attractive fields in the medical image pre-processing. It can not only assist clinicians to observe the patient's lesion area more intuitively and comprehensively, but also realize the desire to save costs for patients and reduce the psychological pressure of patients during tedious pathological examinations. Recently, many deep learning-based methods have been proposed to complement the multi-modal image data and provided good performance. However, current methods cannot fully reflect the continuous semantic information between the adjacent slices and the structural information of the intra-slice features, resulting in limited complementation effects and efficiencies. To solve these problems, in this work, we propose a novel generative adversarial network (GAN) framework, named as random generative adversarial network (RAGAN), to complete the missing T1, T1ce, and FLAIR data from the given T2 modal data in real brain MRI, which consists of the following parts: (1) For the generator, we use T2 modal images and multi-modal classification labels from the same sample for cyclically supervised training of image generation, so as to realize the restoration of arbitrary modal images. (2) For the discriminator, a multi-branch network is proposed where the primary branch is designed to judge whether the certain generated modal image is similar to the target modal image, while the auxiliary branch is to judge whether its essential visual features are similar to those of the target modal image. We conduct qualitative and quantitative experimental validations on the BraTs2018 dataset, generating 10,686 MRI data in each missing modality. Real brain tumor morphology images were compared with synthetic brain tumor morphology images using PSNR and SSIM as evaluation metrics. Experiments demonstrate that the brightness, resolution, location, and morphology of brain tissue under different modalities are well reconstructed. Meanwhile, we also use the segmentation network as a further validation experiment. Blend synthetic and real images into a segmentation network. Our segmentation network adopts the classic segmentation network UNet. The segmentation result is 77.58%. In order to prove the value of our proposed method, we use the better segmentation network RES_UNet with depth supervision as the segmentation model, and the segmentation accuracy rate is 88.76%. Although our method does not significantly outperform other algorithms, the DICE value is 2% higher than the current state-of-the-art data completion algorithm TC-MGAN.

Machine-learning to predict utility of circulating tumor DNA (ctDNA) for somatic genotyping.

232 Background: ctDNA genotyping is increasingly used to evaluate patient eligibility for genomics-driven treatments ( e.g., PARP inhibitors) in metastatic castration-resistant prostate cancer (mCRPC), but low ctDNA-fraction (ctDNA%) causes false negatives and is unpredictable. We investigated whether a machine-learning model exploiting routine clinical prognostic markers can predict if a mCRPC patient will have sufficient ctDNA% for informative ctDNA testing. Methods: We analysed plasma cell-free DNA (cfDNA) collected at baseline prior to first-line therapy from 463 consecutive mCRPC patients. ctDNA% was quantified using somatic allele frequencies and genome-wide copy number profiles via deep targeted sequencing. We built an XGBoost model (70:30 train-test split) using 18 clinical factors to predict synchronously measured ctDNA% (binary classification >2% or ≤2%, i.e., the conventional ctDNA% limit of detection for mutations employed by commercial tests), with performance evaluated using area under the curve (AUC) of receiver operating characteristics. Results: Median age was 73 (range: 45-98) and 86% of patients had ECOG 0-1. Median ctDNA% was 5% (range: 0-89%) and correlated with serum and radiographic metrics of disease burden, including total cfDNA concentration (ng/mL of plasma, reflecting cfDNA released by both normal and tumor cells; Spearman ρ=0.55), alkaline phosphatase (ALP) per upper limit of normal (ULN) ( ρ=0.46), lactate dehydrogenase (LDH) per ULN ( ρ=0.41), PSA ( ρ=0.3), presence of liver metastases, and ≥10 bone lesions. We trained an XGBoost model incorporating these and 13 additional clinical factors achieving an AUC for ctDNA >2% of 0.83 (F1 score: 0.79). SHAP interpretability scores indicated that cfDNA concentration most strongly informed prediction of ctDNA >2%, followed by ALP/ULN and PSA—consistent with our prior bivariate rank correlations—whereas features associated with initial prostate cancer diagnosis (Gleason Grade Group, de novo versus metachronous M1 disease) were less informative. Recognizing that comprehensive and standardised clinical annotation is not always available in real-world settings, we built a secondary parsimonious ctDNA%-prediction tool restricted to 8 highly informative and clinically practical factors (cfDNA concentration, ALP/ULN, LDH/ULN, PSA, albumin, ECOG, liver metastases, lung metastases) and flexible to incomplete input data, achieving a comparable AUC for ctDNA>2% of 0.76. Conclusions: Our results demonstrate the feasibility of a machine learning framework to estimate ctDNA% in patients with mCRPC. This point-of-care tool would enable prioritisation of mCRPC patients for ctDNA somatic genotyping with a predicted ctDNA >2%, versus tissue or germline-only testing in patients with a predicted ctDNA ≤2%.

Model Identification with Incomplete Input Data in Type 1 Diabetes*

A major challenge in fitting models to glucose metabolism in people with type 1 diabetes is incomplete data as its collection partially relies on self-reporting and does not include all relevant events. We develop a method for identifying optimal input corrections to reestablish a correct input-output relationship in the data while jointly identifying personalized model parameters. The unreported or misreported parts in the data are reconciled by adding sparse corrections via mixed-integer quadratic programming leading to an improved identification of the model parameters. We conduct numerical experiments with incomplete in-silico training data and show that models obtained from our method are able to provide more accurate predictions on test data than models obtained from standard methods. The performance of our methodology is similar to that attained with the standard method when trained on data with complete information.

From Front to Rear: 3D Semantic Scene Completion Through Planar Convolution and Attention-Based Network

Semantic Scene Completion (SSC) aims to reconstruct complete 3D scenes with precise voxel-wise semantics from the single-view incomplete input data, a crucial but highly challenging problem for scene understanding. Although SSC has seen significant progress due to the introduction of 2D semantic priors in recent years, the occluded parts, especially the rear-view of the scenes, are still poorly completed and segmented. To ameliorate this issue, we propose a novel deep learning framework for 3D SSC, named Planar Convolution and Attention-based Network (PCANet), to effectively extend high-precision predictions of the front-view surface to the rear-view occluded areas. Specifically, we decompose the traditional convolutional layer into three successive planar convolutions to form a Planar Convolution Residual (PCR) block, which maintains the planar features of the 3D scene. Afterward, the Planar Attention Module (PAM) is proposed to capture three different planar attentions and harvest the global context from the front surface to the rear occluded areas to improve the overall accuracy. Extensive experiments on the real NYU and NYUCAD datasets and the synthetic SUNCG-RGBD dataset demonstrate that our proposed framework can generate high-quality SSC results in both front and rear views and outperforms the state-of-the-art approaches trained in an end-to-end manner without additional data.

Estimating a panel MSK dataset for comparative analyses of national absorptive capacity systems, economic growth, and development in low and middle income countries.

Within the national innovation system literature, the low- and middle-income countries (LMICs) eligible for the World Bank's International Development Association (IDA) support, are rarely part of empirical discourses on growth, development, and innovation. One major issue hindering empirical analyses in LMICs is the lack of complete data availability. This work offers a new full panel dataset with no missing values for IDA-eligible LMICs. I use a standard, widely respected multiple imputation method (specifically, Predictive Mean Matching) developed by Rubin in the 1980s, which conforms to the structure of multivariate continuous panel data at the country level. The incomplete input data consisting of many variables come from publicly available established sources. These variables, in turn, capture six crucial country-level capacities: technological capacity, financial capacity, human capital capacity, infrastructural capacity, public policy capacity, and social capacity. Such capacities are part and parcel of the National Absorptive Capacity Systems (NACS). The dataset (MSK dataset) thus produced contains data on 47 variables for 82 LMICs between 2005 and 2019. The dataset has passed a quality and reliability check and can therefore be used for comparative analyses of national absorptive capacities and development, transition, and convergence among LMICs.

Performance measurement of construction suppliers under localization, agility, and digitalization criteria: Fuzzy Ordinal Priority Approach.

The suppliers’ performance plays a vital role, with a domino effect, in project success, organizational competitiveness, protecting supply chain and construction industry from disruptions and PESTEL risks (political, economic, social, technological, environmental, and legal). Therefore, measuring the performance of the construction suppliers has become the primary focus of project-oriented organizations and the core of business decision-making, especially during global megatrends. The question that may arise here is, “How can the performance of the construction suppliers be determined under uncertainties considering the post-COVID-19 era?” Organizations need eligible suppliers for the rapid recovery of the supply chain and construction sector at this critical stage. Given the importance of the issue, this study aims to propose a novel approach for measuring the performance of construction suppliers using the fuzzy ordinal priority approach (OPA-F). OPA-F is a recent development in multiple criteria decision-making (MCDM) that can determine the criteria weights for performance measurement using fuzzy linguistic variables. We do not always have access to a complete data set in real-world situations and business environments. Nevertheless, OPA-F can handle this dilemma, even with incomplete input data. This research intends to consider three main aspects of the construction suppliers, known as (L-A-D) capabilities, including localization, agility, and digitalization. In this regard, we bring up a case study from the construction industry to demonstrate the application of the proposed framework. The findings show that the most critical criterion is “digitalization” for the case study. This criterion covers “supply chain automation” and “virtualization and dematerialization” of services/products. The proposed approach is practical and straightforward, particularly for academicians and decision-makers; it can also incorporate uncertainties.

Simulated Hough Transform Model Optimized for Straight-Line Recognition Using Frontier FPGA Devices

The use of the Hough transforms to identify shapes or images has been extensively studied in the past using software for artificial intelligence applications. In this article, we present a generalization of the goal of shape recognition using the Hough transform, applied to a broader range of real problems. A software simulator was developed to generate input patterns (straight-lines) and test the ability of a generic low-latency system to identify these lines: first in a clean environment with no other inputs and then looking for the same lines as ambient background noise increases. In particular, the paper presents a study to optimize the implementation of the Hough transform algorithm in programmable digital devices, such as FPGAs. We investigated the ability of the Hough transform to discriminate straight-lines within a vast bundle of random lines, emulating a noisy environment. In more detail, the study follows an extensive investigation we recently conducted to recognize tracks of ionizing particles in high-energy physics. In this field, the lines can represent the trajectories of particles that must be immediately recognized as they are created in a particle detector. The main advantage of using FPGAs over any other component is their speed and low latency to investigate pattern recognition problems in a noisy environment. In fact, FPGAs guarantee a latency that increases linearly with the incoming data, while other solutions increase latency times more quickly. Furthermore, HT inherently adapts to incomplete input data sets, especially if resolutions are limited. Hence, an FPGA system that implements HT is inefficient for small sets of input data but becomes more cost-effective as the size of the input data increases. The document first presents an example that uses a large Accumulator consisting of 1100 × 600 Bins and several sets of input data to validate the Hough transform algorithm as random noise increases to 80% of input data. Then, a more specifically dedicated input set was chosen to emulate a real situation where a Xilinx UltraScale+ was to be used as the final target device. Thus, we have reduced the Accumulator to 280 × 280 Bins using a clock signal at 250 MHz and a few tens input points. Under these conditions, the behavior of the firmware matched the software simulations, confirming the feasibility of the HT implementation on FPGA.

Path Selection for the Inspection Robot by m-Generalized q-Neutrosophic PROMETHEE Approach

Path planning can be considered the most vital task of the autonomous robot. In this task, selecting an optimal route from the starting to the target position becomes an important problem that must be addressed when multiple competing optimization priorities are considered. Thus, a novel route assessment strategy based on a multi-criteria decision-making approach is proposed. The m-generalized q-neutrosophic PROMETHEE (PROMETHEE-mGqNS) method is applied to aggregate the competing route assessment requirements and choose an optimal route. A case study is investigated to explain the proposed strategy for path planning in a typical environment and indicates the method stability when incomplete input data characteristics are present.

Identification of BDS Satellite Clock Periodic Signals Based on Lomb-Scargle Power Spectrum and Continuous Wavelet Transform

Onboard satellite clocks are the basis of Global Navigation Satellite Systems (GNSS) operation, and their revolution periods are at the level of 2 per day (about 12 h) in the case of the Medium Earth Orbit (MEO) satellites. In this work, the authors analysed the entire BeiDou Navigation Satellite System (BDS) space segment (BDS-2 and BDS-3) in terms of the occurrence of periodic, repetitive signals in the clock products, and checked if they coincide with the orbital periods or their multiples. The Lomb-Scargle (L-S) power spectrum was used as a tool to determine the periods present in the BDS clock products, allowing for analyses based on incomplete input data; in this case, the incomplete data were the phase data with jumps and outliers removed. In addition, continuous wavelet transform (CWT) was used to produce a time−frequency representation showing the more complex behaviour of the satellite clock products. As shown in the case of geostationary and geosynchronous inclined orbit satellites, the main period was 23.935 h, while for the Medium Earth Orbit it was 12.887 h, with the BDS satellite orbital period being 12 h 53 m (12.883 h). Some effects connected with reference clock swapping are also visible in the power spectrum. The conducted analyses showed that the BDS-2 satellite clocks have much higher noise than the BDS-3 satellite clocks, meaning that the number of designated periods is greater, but their reliability is significantly lower. BDS-3 satellites have only been in operation for a very short time, thus this is the first analysis to include this type of data. Moreover, such a wide and complex analysis has not been carried out to date.

Aetiology and Progression of Construction Disputes towards a Predictive Model

Construction projects are at risk of dispute. Dispute prevention is thus expected to avoid that cost. This paper aims to establish a model predicting the occurrence of disputes and identifying strategies regarding resource allocation in dispute avoidance. Factors in the model were identified by a literature review and conceptualised into aetiology and progression of dispute formation based on an analogy with epidemiological investigations. The model structure was established upon this analogy, validated against Bradford Hill criteria, and then quantified by conducting a Bayesian network analysis with samples returned from a questionnaire survey. The model shows high accuracy rates with both complete and incomplete input data. Finally, a sensitivity analysis was conducted to simulate the results of potential management measures available to be taken. This predictive model not only serves the prediction function but also traces back to factors causing disputes, and hence can be used to assist decision making before and during a construction process. At the centre of the model sits the causal mechanism investigation, but the existing methods only shed lights on correlational research without causal consideration. This paper devised an epidemiological investigation which covers from causal proposition to causal examination and contributes greatly to model establishment.

Monitoring and Management of Geoengineering Repositories of Radioactive Waste

Abstract The paper provides the approach to the management of the repositories of radioactive waste, which include monitoring of contaminants migration into environment in case of natural or technogenic accidents which may influence on soil, subsoil waters and repository’ installations structures. The aim of the research is the development of system of estimation of radioactive state of the environment in the area of radioactive repository, based on the cumulative data, the knowledge base, set of rules of production, logical deduction gear and conclusion building gear by means of uncertain and incomplete input data. It is proposed the use of several models for the spectral characteristics of radioactive pollutants migration. The evaluation of possible damages of repository containment is made. The heuristics for the radioactive waste repository expert system are developed. The proposed procedure of quality definition of decision making for the radioactive waste repositories management should take into account the reliability of information about the state of the radioactive repository.

QUALITY CONTROL OF PURCHASED GOODS, WORKS AND SERVICES FOR THE NATIONAL GUARD OF UKRAINE

The purpose of the article is to create methodological bases for increasing the competitive position of organizations for tender procurement to meet the needs of units of the National Guard of Ukraine. Research methods are the use of optimization economic and mathematical models and computer simulation. In the article, by involving representatives of the command of the National Guard of Ukraine, an expert assessment of the impact of various factors in the specific conditions of the procurement process for the needs of logistics of military units. The degree of importance of each of the factors became the basis for the creation of a methodology for interaction of the partner organization with the relevant military unit. In turn, the proposed technique became the theoretical basis for the development of Microsoft Visual Studio 2010 decision support system. This information system helps to support management decisions to improve the effectiveness of the organization's interaction with the military unit of the NMU. The created prototype of the decision support system should be used to solve in the mode of dialogue poorly structured tasks of interaction of the organization with the military unit, which are characterized by incomplete input data, lack of existing standard procedures, incomplete clarity of goals and limitations. In global conditions, improving the competitiveness of the organization in terms of tender procurement will save the defence budget, will be an effective means of preventing corruption in the field of defence procurement, will create favourable conditions for the development of a competitive environment in public procurement and fair competition. The theoretical result of the article is the method of ensuring the competitiveness of the organization in terms of tender procurement for the needs of military units of the National Guard of Ukraine. The practical result is to substantiate the ways to increase the efficiency of the organization on the basis of ensuring competitiveness in terms of tender procurement for the needs of military units of the National Guard of Ukraine.