Neuro-fuzzy inference systems approach to decision support system for economic order quantity

Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neuro-fuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision sup- port system gives valid results. The proposed system is flexible and can be applied to various types of goods in SC

Implementation of information and analysis support of the industrial enterprise’s logistical management based on the tools of the fuzzy set theory

Management of industrial enterprises’ logistical systems is based on application of rather heterogeneous and not always certain information. Presence of different types of uncertainty in the complex hierarchical system of industrial enterprises’ logistical management gives grounds for analysis support of management solutions based on the fuzzy set theory. Use of the fuzzy set theory allows to link together and adequately consider all the necessary heterogeneous information. In this regard, information on functioning of the logistical system must be presented in a specific form as membership functions. It is justified in the article that the tools of the fuzzy set theory can be applied for description of parameters of the industrial enterprises’ logistical system and justification of decision-taking in the sphere of logistical management. Within the framework of the system of information and analysis support of industrial enterprises’ logistical management it is proposed to use tools of problem “determination of the fuzzy set image” and its variety – “definition of the sub direct fuzzy set image” in order to choose the best variant of combination of key efficiency indicators of logistical management complying with the present complex of criteria. Application of the fuzzy set theory also allows to determine fuzzy values of factors, as a result of which the enterprise’s logistical system has obtained the existing or objective set of features. For analysis of factors influencing the key efficiency indicators of the industrial enterprise’s logistical management it is proposed to use tools of problem “definition of the fuzzy set pre-image at a fuzzy binary relation”

Development of a Fuzzy Decision Support System for Irrigation Network Operation Under Water Scarcity Conditions

Inefficient irrigation and drainage networks lead to an increase in the gap between water supply and demand, especially under water scarcity conditions. Proper operation of irrigation networks plays an important role in ensuring water supply and demand management. This requires the implementation of a comprehensive approach to making the right decisions at the time of operation. The design of this approach is complex due to the existence of conflicts of interest, uncertainty, and the intrinsic complexity of irrigation network operation topics. In Iran, current practices in irrigation network operations rely on personal experiences and lack comprehensive decision-making tools. This study proposes a fuzzy decision support system to address this challenge. The fuzzy decision support system leverages a fuzzy conceptual model to capture the inherent complexity and uncertainty of irrigation networks. It utilizes a systems approach to identify problems, propose key decision options, and evaluate various solutions. The study emphasizes the effectiveness of multi-criteria decision-making methods for handling complex irrigation network issues. An example of the results of structuring the decision-making process, along with the development of a hierarchical analysis method that is combined with fuzzy set theory (FAHP), is presented in this paper. This shows how the fuzzy decision support system structure can be applied in a real-world irrigation network. Implementing such a system in irrigation management companies is expected to improve water distribution indicators by enabling data-driven, informed decision-making

Engineering Decision Support and Expert Systems - Colorado State University

The student is introduced to development of decision support systems (DSS) for application to complex engineering management and design problems under conflicting objectives and uncertainty. A number of techniques are introduced for aiding in the analysis of a wide range of complex multiobjective engineering problems. Several stochastic optimization methods are presented for including risk and reliability in engineering design. Basic concepts of expert systems (ES) are discussed to show an essential synergy between DSS and ES for development of decision support structures that allow inclusion of human domain knowledge, heuristics and fuzzy logic. Heuristic methods such as genetic algorithms and particle swarm optimization are offered as a means of solving complex engineering design and management problems that defy traditional techniques of mathematical programming and operations research. Machine learning methods using artificial neural networks are introduced for solving complex dynamic scheduling and control problems in engineering. Each student is required to present a final class project involving application of the tools and concepts presented in the class to a real-world engineering decision problem. Course taught at Colorado State University

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

The object of this study is fuzzy cognitive modeling as a means of studying semistructured socio-economic systems. The features of constructing cognitive maps, providing the ability to choose management decisions in complex semistructured socio-economic systems, are described. It is shown that further improvement of technologies necessary for developing decision support systems and their practical use is still relevant. This work aimed to improve the accuracy of cognitive modeling of semistructured systems based on a fuzzy cognitive map of structuring nonformalized situations (MSNS) with the evaluation of root-mean-square error (RMSE) and mean average squared error (MASE) coefficients. In order to achieve the goal, the following main methods were used: systems analysis methods, fuzzy logic and fuzzy sets theory postulates, theory of integral wavelet transform, correlation and autocorrelation analyses. As a result, a new methodology for constructing MSNS was proposed—a map of structuring nonformalized situations that combines the positive properties of previous fuzzy cognitive maps. The solution of modeling problems based on this methodology should increase the reliability and quality of analysis and modeling of semistructured systems and processes under uncertainty. The analysis using open datasets proved that compared to the classical ARIMA, SVR, MLP, and Fuzzy time series models, our proposed model provides better performance in terms of MASE and RMSE metrics, which confirms its advantage. Thus, it is advisable to use our proposed algorithm in the future as a mathematical basis for developing software tools for the analysis and modeling of problems in semistructured systems and processes. Doi: 10.28991/ESJ-2022-06-02-012 Full Text: PD

Decision support tools for urban contingency policy: a scenario approach to risk management of the Vesuvio area in Naples, Italy

Contingency management, in particular the management of unanticipated events outside the control of an ordinary planning system, has in the last 50years become an important andfrequently debated issue in the scientific literature on complex systems management underrisk conditions. The urban system can be regarded as such an open complex system whereexternal events, not always foreseeable with a closed system's model, may strongly impact on the internal dynamics of an urban area.Conventionally, planning the future presupposes collecting information and analyzing itrationally in order to control for unexpected contingency events. But it is an importantquestion in the field of urban planning, how proper strategies can be developed to deal withexternal uncertainty and shocks that transcend the imagination of policy-makers. How should decision-makers respond to such unforeseen jumps in asystem?The aim of this paper is to present and apply a new scientific decision support method based on the future studies literature, with the aim to helpdecision-makers in the strategicmanagement of uncertainty and risk in order "to anticipate the extraordinary events correctlyin order to act more effectively" (Godet, 1987). In particular, we will deploy here the scenariomethodology in combination with multicriteria analysis and fuzzy set theory, as a usefu

Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs

[EN] Currently, the management of water networks is key to increase their sustainability. This fact implies that water managers have to develop tools that ease the decision-making process in order to improve the efficiency of irrigation networks, as well as their exploitation costs. The present research proposes a mathematical programming model to optimize the selection of the water sources and the volume over time in water networks, minimizing the operation costs as a function of the water demand and the reservoir capacity. The model, which is based on fuzzy methods, improves the evaluation performed by water managers when they have to decide about the acquisition of the water resources under uncertain costs. Different fuzzy solution approaches have been applied and assessed in terms of model complexity and computational efficiency, showing the solution accomplished for each one. A comparison between different methods was applied in a real water network, reaching a 20% total cost reduction for the best solution.Sanchis, R.; Díaz-Madroñero Boluda, FM.; López Jiménez, PA.; Pérez-Sánchez, M. (2019). Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs. Water. 11(12):1-22. https://doi.org/10.3390/w11122432S1221112Biswas, A. K. (2004). Integrated Water Resources Management: A Reassessment. Water International, 29(2), 248-256. doi:10.1080/02508060408691775Pahl-Wostl, C. (2006). Transitions towards adaptive management of water facing climate and global change. Water Resources Management, 21(1), 49-62. doi:10.1007/s11269-006-9040-4Wu, K., & Zhang, L. (2014). Progress in the Development of Environmental Risk Assessment as a Tool for the Decision-Making Process. 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Improving the quality of the industrial enterprise management based on the network-centric approach

The article examines the network-centric approach to the industrial enterprise management to improve the ef ciency and effectiveness in the implementation of production plans and maximize responsiveness to customers. A network-centric management means the decentralized enterprise group management. A group means a set of enterprise divisions, which should solve by joint efforts a certain case that occurs in the production process. The network-centric management involves more delegation of authority to the lower elements of the enterprise’s organizational structure. The industrial enterprise is considered as a large complex system (production system) functioning and controlled amidst various types of uncertainty: information support uncertainty and goal uncertainty or multicriteria uncertainty. The information support uncertainty occurs because the complex system functioning always takes place in the context of incomplete and fuzzy information. Goal uncertainty or multicriteria uncertainty caused by a great number of goalsestablished for the production system. The network-centric management task de nition by the production system is formulated. The authors offer a mathematical model for optimal planning of consumers’ orders production with the participation of the main enterprise divisions. The methods of formalization of various types of uncertainty in production planning tasks are considered on the basis of the application of the fuzzy sets theory. An enterprise command center is offered as an effective tool for making management decisions by divisions. The article demonstrates that decentralized group management methods can improve the ef ciency and effectiveness of the implementation of production plans through the self-organization mechanisms of enterprise divisions.The work has been prepared with the financial support from the Russian Ministry of Education and Science (Contract No. 02.G25.31.0068 of 23.05.2013 as part of the measure to implement Decision of the Russian Government No. 218)