Modern Decision Support Systems Research Articles

Precise fertilization in greenhouse crops using modern decision support systems to save fertilisers and mitigate pollution of water resources

Precise fertilization in greenhouse crops using modern decision support systems to save fertilisers and mitigate pollution of water resources

Адресная реабилитация сельских населённых пунктов юго-запада Брянской области в отдалённый период после аварии на Чернобыльской АЭС

This study investigates the most efficient methods for mitigating radiation exposure to residents of rural settlements in five south-west districts of Bryansk Oblast, Russia. A database has been created utilizing modern decision support systems (ReSCA and GISDSS). It contains characteristics of 71 rural settlements, agricultural ecosystems, and forests in the most radioactively contaminated territories of Bryansk Oblast. The authors conducted an assessment to determine the radiological and economic effectiveness of seven technologies aimed at reducing both internal and external exposure for residents who were exposed to a total dose exceeding 1 mSv/year. A list of the most effective measures for each settlement was determined, taking into consideration radiological indicators, cost, and social acceptability of technologies. The application of ferrocene to cattle and the surface improvement of pasture lands in the settlement area have been identified as the most effective measures. The efficiency of radical improvement of hayfields and pastures has significantly decreased since the early 2000s due to the high implementation costs. It has been observed that in ten areas, implementing all efficient remediation technologies will not guarantee a decrease in the overall radiation exposure for the public to a level below 1 mSv/year. Simultaneously, the reduction factor for the total radiation dose experienced by residents in the most vulnerable settlements will be increased by 1.7-2.2 times. The results obtained are crucial for prioritizing further remediation of settlements in south-west Bryansk Oblast and restoring hem to normal living conditions in relation to radiation exposure.

Tackling the Accuracy-Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal

Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows extending the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this article, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning, which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.

A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors

Accurate time series prediction techniques are becoming fundamental to modern decision support systems. As massive data processing develops in its practicality, machine learning (ML) techniques applied to time series can automate and improve prediction models. The radical novelty of this paper is the development of a hybrid model that combines a new approach to the classical Kalman filter with machine learning techniques, i.e., support vector regression (SVR) and nonlinear autoregressive (NAR) neural networks, to improve the performance of existing predictive models. The proposed hybrid model uses, on the one hand, an improved Kalman filter method that eliminates the convergence problems of time series data with large error variance and, on the other hand, an ML algorithm as a correction factor to predict the model error. The results reveal that our hybrid models obtain accurate predictions, substantially reducing the root mean square and absolute mean errors compared to the classical and alternative Kalman filter models and achieving a goodness of fit greater than 0.95. Furthermore, the generalization of this algorithm was confirmed by its validation in two different scenarios.

Rating and Evaluation of Ethical Behaviors of Education Managers with AHP Analysis Technique, one of the Modern Decision Support Systems

This study aims to create a model for rating and evaluation the ethical behaviors that education administrators will use in decision-making processes by using the Analytical Hierarchy Process (AHP), which is one of the modern decision support systems. The research used the Analytical Hierarchy Process (AHP) method, one of the modern decision support systems developed by Saaty (1980), which allows the joint analysis of quantitative and qualitative data. The data collection instrument for the study is the Ethical Leadership Scale developed by Ylmaz (2006). Research data were collected from 15 school administrators and teachers working in Kartal, Pendik, Maltepe, and Sultanbeyli districts on the Anatolian side of Istanbul province during. The purposive sampling method was used to determine the study group. The ethical behaviors identified using the sub-dimensions of the scale were compared in pairs and ranked according to their importance. The results revealed that the most important ethical behaviors managers should have in all their values and behavioral ethics are truthfulness and honesty. In the dimension of environmental ethics, managers are expected to create the freest environments, while managers are expected to be the fairest in the dimension of communication ethics. Managers are expected to have these values and apply them in their lives. When managers make decisions in the management process, they should act rationally and consider the ethical dimensions of the decisions.

The use of environmental decision support systems for modeling of atmospheric pollution following the chemical accidents

We studied the possibility of the combined application of screening models to assess the characteristics of sources in accidents at storage facilities for hazardous substances with complex models of atmospheric transport as part of modern decision support systems to calculate air pollution in a wide range of spatial and temporal scales. The evaporation time following an emergency spill, estimated by screening models, is used to set the emission intensity and calculate the atmospheric transport by the RODOS nuclear emergency response system. For the accident in Chernihiv on March 23, 2022, it was estimated that the maximum permissible concentration of ammonia 0.2 mg/m3 was exceeded at distances up to 75 km from the source. The dependence of the calculated maximum concentrations on time is close to asymptote cmax ~ t-4.5 up to 15 h after emission, which is consistent with the asymptote σ ~ t3/2 for the time dependence of the sizes of puffs following turbulent dispersion of instantaneous releases.

Validating a smart nutrient solution replenishment strategy to save water and nutrients in hydroponic crops

Sustainable use of water and nutrients is fundamental in modern Mediterranean greenhouses and the use of modern decision support systems (DSS) can contribute to this direction. This work focuses on implementing new elements in water and nutrient recycling in cropping systems under saline conditions. Thus, two nutrient solution (NS) control strategies were applied to tomato crops grown in a recirculated NS under low- and high-salinity (NaCl) conditions. According to the first strategy, (i.e., Standard-STD) NS was replenished with water and nutrients based on predetermined “uptake concentrations” (UCs), i.e., nutrient to water uptake ratios, following standard commercial practices. According to the second strategy, NS was replenished by applying recalculated UCs of all nutrients after chemical analysis of a drainage solution sample using the software NUTRISENSE (NTS). Compared with the STD strategy, tomato plants treated with NTS retained drainage nutrient concentrations closer to the optimal values irrespectively of the salinity treatment. This eventually accounted for a smaller deviation from the mean threshold salinity value in the root zone at higher salinity levels, thus plants retained water consumption and crop yield potential at higher stomatal conductance minimizing physiological impacts of salinity on plants. Nutritional control strategies did not impose substantial difference in the UCs for most of the nutrients; however, this was not the case under salinity. Nutrient UCs determined and NTS strategy can be successfully used in DSS to optimize nutrient supply and prolong recirculation, thereby saving precious water and nutrients, in Mediterranean hydroponic crops with particular emphasis on saline environments.

Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming

Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency.

An Approach to Building Decision Support Systems Based on an Ontology Service

Modern decision support systems (DSSs) need components for storing knowledge. Moreover, DSSs must support fuzzy inference to work with uncertainty. Ontologies are designed to represent knowledge of complex structures and to perform inference tasks. Developers must use the OWLAPI and SWRL API libraries to use ontology features. They are impossible to use in DSSs written in programming languages not for Java Virtual Machines. The FuzzyOWL library and the FuzzyDL inference engine are required to work with fuzzy ontologies. The FuzzyOWL library is currently unmaintained and does not have a public Git repository. Thus, it is necessary to develop the ontology service. The ontology service must allow working with ontologies and making fuzzy inferences. The article presents ontology models for decision support, fuzzy inference, and the fuzzy inference algorithm. The article considers examples of DSSs for balancing production capacities and image analysis. The article also describes the architecture of the ontology service. The proposed novel ontology models for decision support make it possible to reduce the time of a knowledge base formation. The ontology service can integrate with external systems with HTTP protocol.

MANAGEMENT AND ORGANIZATIONAL AND ECONOMIC CONDITIONS OF STRENGTHENING THE MARKETING ACTIVITY OF THE ENTERPRISE AND MAINTAINING EFFICIENT AGRO BUSINESS

Abstract. The analysis of the activity of agricultural enterprises indicated its inherent high degree of risk. Unlike other industries, a significant share of integrated risk for agriculture is weather risk. It is this risk that necessitates the diversification of agricultural marketing into three forms: with a deficit of the main commodity product of the agricultural enterprise in the market; with the balance of sales and supply and with the market surplus of marketable products. It is pointed out that in order to reduce the risk, a significant number of medium-sized producers rely on multi-product production and management has to simultaneously make use of all the intended forms of marketing. It has been established that the marketing activities of agricultural enterprises for a number of reasons: established tradition, lack of financial resources, etc. are often sporadic, fragmented, not a systematic, purposeful activity with defined goals, well considered tools, adequate funding and resources. Therefore, a number of specific steps have been proposed for the implementation of effective agribusiness: changing the traditional way of thinking; formation of a holistic systematic approach to marketing policy as a single coordinated complex of management and marketing; thorough study of the market of products manufactured by the agricultural enterprise, and the formation of the forecast for the next season. To strengthen marketing activities and conduct effective agribusiness, algorithms have been developed to gain a competitive advantage. It is shown that agricultural marketing today is becoming a broader field of activity than providing pure production and marketing using the main levers — price, product, sales and communication. It must best meet the needs and requirements of consumers, in particular, by shifting the main emphasis from price and sales aspects to communication. This is one of the main ways to ensure the function of agribusiness efficiency. It is proposed to use the experience of developed countries and more widely employ modern information technologies of management and marketing, in particular, decision support systems and analysis of risks and forecasts for the next period. The following ways are proposed for the use of modern decision support systems, risk analysis and forecasts in Ukrainian agricultural management and marketing: cooperation, use of these information systems on the basis of lease or, less relevant, expert risk assessment. A mathematical method of taking into account the degree of risk in the business processes of agricultural enterprises is proposed. Keywords: marketing, management, management, manager, agricultural management, agribusiness, information technology marketing. JEL Classification M11, M31, Q13 Formulas: 1; fig.: 3; tabl.: 0; bibl.: 22.

FIViz: Forensics Investigation through Visualization for Malware in Internet of Things

Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim’s device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.

Decision Support Systems for Managing Innovation Through Project Selection in Public Sector R&D Environments

One of the primary components of innovation management in research and development (R&D) organizations is project selection. In public sector R&D environments, it is essential that improvements are made to the selection processes. These improvements should address the unique challenges faced by these organizations in order to increase the quality of the decisions that are made. Modern decision support systems (DSS) have the potential to meet these needs. The implementation of effective DSS backed by solid data management practices and driven by artificial intelligence technologies has the potential to enhance the management of public sector R&D innovation.

Decision support systems for scheduling tasks in Biosystems Engineering

Modern decision support systems have many applications, including assistance in scheduling tasks. Biosystems engineering combines engineering sciences and physical sciences in order to understand and improve biological systems in agriculture, food production, environment, etc. The work reviews the decision support systems in the aspect of scheduling tasks in the field of biosystems engineering. The analysis was based on documents (articles and proceedings paper) indexed in the Web of Science Core Collection (WoS-CC) database from 1945-2018. The search has been limited to the category of WoS-CC related to agriculture, water resources, food processing, horticulture and forestry. The main research topics, areas of application and methods used were determined. In the analyzed documents, task scheduling was mainly used in irrigation and harvest scheduling. Simple and advanced optimization tools were used.

Human–Machine Cloud Decision Support in Tourism

Modern decision support systems (DSSs) use a broad range of technologies based on data processing by both humans and computers. This article describes a new architectural approach to creating DSSs based on the human–machine cloud concept. This concept combines two different views of decision support in the tourism industry: the standpoint of a tourist and the position of administrative bodies of tourist regions. The article distinguishes standard tasks of decision support in tourism; these tasks are then projected onto a multilevel concept architecture of cloud services. The proposed approach is illustrated based on the example of three scenarios of interaction of services (resources) in solving practical tasks.

Integrating Remote Sensing Data in Decision Support Systems for Transportation Asset Management

The objective of the research summarized in this manuscript is two-fold: to develop and make available a set of novel analysis tools, fully integrated in ArcGIS, that leverages the rich information provided by satellite-based remote sensing data to detect and characterize geohazards of interest to the transportation community; and to provide a modern web-based decision support system (DSS) in which these novel analysis products can be seamlessly integrated with existing datasets. Specifically, interferometric synthetic aperture radar (InSAR) and its derivatives are employed in this work. Each tool is designed to target a specific asset of interest to the transportation community: sinkhole detection, bridge differential settlement detection, slope stability assessment, and pavement distress evaluation. Furthermore, to allow interested parties to test the introduction of remote sensing data into their decision-making process without requiring the full integration with existing workflows, a modern web-based DSS is introduced. Thanks to the large coverage and the high update frequencies offered by satellite-based remote sensing data, datasets augmented with these new products can offer a near real-time overview of the network status and lead to better support in decisions about resources allocation, existing infrastructure monitoring, new infrastructure planning, and post-construction assessment.

Distributed snapshot maintenance in wide-column NoSQL databases using partitioned incremental ETL pipelines

Wide-column NoSQL databases are an important class of NoSQL (Not only SQL) databases which scale horizontally and feature high access performance on sparse tables. With current trends towards big Data Warehouses (DWs), it is attractive to run existing business intelligence/data warehousing applications on higher volumes of data in wide-column NoSQL databases for low latency by mapping multidimensional models to wide-column NoSQL models or using additional SQL add-ons. For examples, applications like retail management can run over integrated data sets stored in big DWs or in the cloud to capture current item-selling trends. Many of these systems also employ Snapshot Isolation (SI) as a concurrency control mechanism to achieve high throughput for read-heavy workloads. SI works well in a DW environment, as analytical queries can now work on (consistent) snapshots and are not impacted by concurrent update jobs performed by online incremental Extract-Transform-Load (ETL) flows that refresh fact/dimension tables. However, the snapshot made available in the DW is often stale, since at the moment when an analytical query is issued, the source updates (e.g. in a remote retail store) may not have been extracted and processed by the ETL process in time due to high input data volume or slow processing speed. This staleness may cause incorrect results for time-critical decision support queries. To address this problem, snapshots which are supposed to be accessed by analytical queries need to be first maintained by corresponding ETL flows to reflect source updates based on given freshness needs. Snapshot maintenance in this work means maintaining the distributed data partitions that are required by a query. Since most NoSQL databases are not ACID compliant and do not provide full-fledged distributed transaction support, snapshot may be inconsistently derived when its data partitions are updated by different ETL maintenance jobs.This paper describes an extended version of HBelt system [1] which tightly integrates the wide-column NoSQL database HBase with a clustered & pipelined ETL engine. Our objective is to efficiently refresh HBase tables with remote source updates while a consistent snapshot is guaranteed across distributed partitions for each scan request in analytical queries. A consistency model is defined and implemented to address so-called distributed snapshot maintenance. To achieve this, ETL jobs and analytical queries are scheduled in a distributed processing environment. In addition, a partitioned, incremental ETL pipeline is introduced to increase the performance of ETL (update) jobs. We validate the efficiency gain in terms of data pipelining and data partitioning using the TPC-DS benchmark, which simulates a modern decision support system for a retail product supplier. Experimental results show that high query throughput can be achieved in HBelt when distributed, refreshed snapshots are demanded.

Processing Uncertainties in Modeling Nonstationary Time Series Using Decision Support Systems

Background . Forecasting of nonlinear nonstationary time series (NNTS) is important problem in economics, marketing, industry, ecology and many other branches of science and practical activities. Successful solution of the problem requires development of modern computer based decision support systems (DSS) capable to generate reliable estimates of forecasts in conditions of uncertainty of various type and origin. Objective . The purpose of the research is as follows: development of requirements to the modern DSS and their formal representation; analysis of uncertainty types characteristic for model building and forecasting; selection of techniques for taking into consideration of the uncertainties; and illustration of the system application to solving the problem of forecasts estimation for heteroscedastic NNTS using statistical data. Methods . To reach the objectives stated the following methods were used: systemic approach to statistical data analysis; statistical approach to identification and taking into consideration of possible uncertainties; Kalman filtering techniques; Bayesian programming approach and statistical criteria of model adequacy and quality of forecasts. Results . Formal description of the DSS is provided, and requirements to its development are given; the classes of mathematical methods necessary for DSS implementation are proposed; some approaches to formal taking into consideration of probabilistic, statistical and parametric uncertainties are discussed; and illustrating example of the DSS application is considered. Conclusions . Systemic approach to DSS constructing for solving the problem of nonlinear nonstationary time series forecasting turned out to be very fruitful. Using the system proposed it is possible to take into consideration various uncertainties of probabilistic, statistical and parametric type, and to compute high quality estimates of short and medium term forecasts for NNTS. The approach proposed has good perspectives for the future improvements and enhancement.

Енергонезалежність України: досягнення та перспективи

The article provides a retrospective analysis of market transformations in Ukraine's energy sector in terms of efficiency of creating competitive commodity markets. The author identifies the basic factors causing structural changes of this country's energy balance during 1991-2016, among which the following ones would be emphasized: separation of the state's energy system from the centrally-planned single energy system; the uneven decline and increase in the output in individual sectors causing an uneven demand for energy resources; inconsistency of the reforms on individual energy markets and the consequent imbalances in the logistic pattern of the national energy supply (energy, financial and information flows). The analysis the structural and price disruptions in Ukraine's energy balance shows that, at present, the market mechanism does not ensure the optimum allocation of resources. This situation is the result of the different approaches to the formation of competitive structure of the energy markets and market pricing mechanism. The initial boost in the market reforms towards liberalization was eventually changed to the concentration of national assets that in the end retained the state-monopoly structure, even on the potentially competitive, by their nature, energy markets. Market deviations already at the stage of formation distort the objectives and effectiveness of their operation, which devalues the effect of the transfer of ownership. The privatization of energy assets has failed to create preconditions for the formation of a competitive structure of the market of natural energy monopolies and led to the consolidation of the market power of the energy monopolies. The author considers the structural changes in the energy balance and the dynamics of prices for fuel and energy resources (FER) and makes a comparative analysis of Ukraine with European countries by major energy efficiency indicators, showing the differences between Ukraine's structural and technological characteristics with the corresponding average rates. There is a dynamics of the basic indicators of this country's fuel and energy complex in the context of the use of the available resource potential. The author gives an assessment of the achieved level of energy security and sustainability of energy situation compared to other countries. By the results of the international ranking of energy sustainability, Ukraine occupies one of the lowest positions among the industrial countries. A factor analysis of energy efficiency policy urges for enhancing the improvement of the institutional mechanisms of the modernization of energy systems based on European experience. Energy efficiency is one of the key elements in the competitiveness of Ukrainian goods and services and an important factor of economic growth in Ukraine. The article deals with the key issues in Ukraine's European integration in the energy sector, in particular as to the requirements of the Second and Third EU Energy Packages. The verification of the process of Ukraine's European integration in the energy sector shows that our country shares and accepts the European principles, objectives and criteria for sustainable energy development, but does not apply European methods of task solution, in particular as regards the transparency of the policy measures, consensus of all stakeholders, mechanisms of planning and forecasting (modern decision support systems and risk management, energy foresight, and technology platforms), responsibility for delays in plan fulfilment and poor management, weak coordination between state authorities (duplication), social activation of the energy policy (public involvement) and others. The article outlines the prospects of achieving the maximum possible energy independence of Ukraine. Current challenges for this country's energy policy are derived both from the specific features in the transformation of the energy sector and branch structure of the economy, and from the global geopolitical and economic trends on the world energy market, which over the last decade have changed significantly. Ukraine's response the same as that of most industrialized countries consists in reviewing the national energy strategy, whose main provisions are currently under discussion among experts.

Achieving Well-Performance Optimization Through Work-Flow Automation

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173578, “Samarang Integrated Operations: Achieving Well-Performance Monitoring, Surveillance, and Optimization Through Data- and Model-Driven Work- Flow Automation,” by M. Zul Izzi Ahmad and Colinus Lajim Sayung, Petronas, and Muzahidin M. Salim, M. Kasim Som, Lee Hin Wong, Shripad Biniwale, Nur Erziyati, Kenneth Soh, Roland Hermann, Vo Tri Nghia, Lau Chong Ee, and Muhammad Firdaus Hassan, Schlumberger, prepared for the 2015 SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, USA, 3–5 March. The paper has not been peer reviewed. The Samarang field is located offshore Sabah, Malaysia. The field is undergoing a major redevelopment project with integrated operations (IO). In order to overcome a variety of challenges and to improve field awareness, several work flows were designed and deployed in order to achieve an early milestone of providing realtime well-performance monitoring, surveillance, and optimization. The paper discusses how these work flows were implemented in an integrated way to provide a modern decision-support system for the Samarang field. Introduction Samarang is an old oil field and thus exhibits the characteristics of many mature fields, including declining production. It has been in production for more than 35 years from multiple, now aging, platforms with insufficient metering and monitoring processes. Given this situation, well status and uptime is unknown for many days, causing delays in mitigation and rectification of production issues. Most of the instrument measurements have been carried out on an ad hoc basis; various facility readings were measured manually, which subsequently deferred field review, causing loss of production. The main technique implemented for production optimization has been artificial lift, with more than 80% of wells and strings being gas lifted. It was necessary to find a better way to manage this production flow stream efficiently with an improved asset-management strategy. Solution Overview For Samarang, the IO solution is designed in such a way that work flows process data into actionable information, improve decision processes by leveraging technology, and equip people with systems and tools enabling enhanced decision support. An IO field creates value through enhanced asset management by focusing on decisions to improve reservoir drainage, production, and operations. As part of Samarang’s overall IO development strategy, the wells and facilities were equipped with the required instrumentation to support approximately 25 work flows. Oil and gas asset activities and decisions span a range of time scales; therefore, the work flows are divided mainly into three main categories: fast-loop, medium-loop, and slowloop decision cycles. These are then sub divided into seven different subdomain categories covering flow-assurance, well- performance, facilities-monitoring, artificial-lift, production-planning, enhanced-oil- recovery (EOR) - surveillance, and optimization work flows.

About the methodology of system synthesis of decision-makings and its procedures automation

Purpose . The problem qualitative assessment of technologies and innovations, which cannot be solved without modern methods and decision support systems, is currently particularly relevant. The reason for this is the increasingly increasing amount of heterogeneous information, which should take into account the decision maker. Reduction of the allowable period of decision-making also has a signifi cant impact on this process. These reasons are refl ected in the problems of network-centric warfare, nuclear management, forecasting, and conceptual design. It actualizes the problem of the synthesis of new methods of decision-making that will effectively solve the problem of choice of alternatives in those problem areas where the application of existing methods of decision-making is unfounded or inadmissible. The complexity of the process of creation of new decision-making methods caused by the high degree of utilization of cognitive processes. In accordance with the system-wide development trends the designer in the process of synthesis of the decision-makings should take a system (or group of systems) which implements certain cognitive operations. The first step towards the creation of such a group of systems is the development of methodology of the system synthesis of decision-makings. Materials and methods. The scope of research are approaches to the synthesis of systems, and in particular – the synthesis of decision-makings. On the basis of multidimensional analysis and generalization of approach the methodology was developed, which consists of the main stages - goal setting, goal achievement and variations of the target system. Within each of the stages a wide variation of operations is possible to be used, which applied depending on the available data, obtained from the analysis of the subject area. With the use of the methodology obtained a number of decision-making methods, and their creation was carried out without the use of computer equipment, which require signifi cant time expenses on the development of appropriate solutions. Therefore, it was decided to develop an automated system to improve the effectiveness of the implementation of a number of methodology procedures. Results. During the study were identifi ed use cases of the system, in accordance with which were formed the conceptual and technical architecture of the system, highlights the key subsystems of the reference data, knowledge about the methods of decision-making and synthesis strategies, and identifi es their development tools. As the database used by the DBMS MS SQL Server, as the client side – Borland Delphi. Conclusion. Due to the high complexity of formalization of intellectual, creative, methodologies operations, the focus of automation is the support of conceptual analysis of the decisionmakings subject area and formation on its basis the knowledge base of intellectual operations together with their characteristic features, aimed to combine operations that make up the group of methods of synthesis strategies decision-making and implementation of the search function on the basis of a intellectual operations knowledge base.