Search results for: robust decision support
10979 Modelling Water Usage for Farming
Authors: Ozgu Turgut
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Water scarcity is a problem for many regions which requires immediate action, and solutions cannot be postponed for a long time. It is known that farming consumes a significant portion of usable water. Although in recent years, the efforts to make the transition to dripping or spring watering systems instead of using surface watering started to pay off. It is also known that this transition is not necessarily translated into an increase in the capacity dedicated to other water consumption channels such as city water or power usage. In order to control and allocate the water resource more purposefully, new watering systems have to be used with monitoring abilities that can limit the usage capacity for each farm. In this study, a decision support model which relies on a bi-objective stochastic linear optimization is proposed, which takes crop yield and price volatility into account. The model generates annual planting plans as well as water usage limits for each farmer in the region while taking the total value (i.e., profit) of the overall harvest. The mathematical model is solved using the L-shaped method optimally. The decision support model can be especially useful for regional administrations to plan next year's planting and water incomes and expenses. That is why not only a single optimum but also a set of representative solutions from the Pareto set is generated with the proposed approach.Keywords: decision support, farming, water, tactical planning, optimization, stochastic, pareto
Procedia PDF Downloads 6710978 Alternative Robust Estimators for the Shape Parameters of the Burr XII Distribution
Authors: Fatma Zehra Doğru, Olcay Arslan
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In this paper, we propose alternative robust estimators for the shape parameters of the Burr XII distribution. We provide a small simulation study and a real data example to illustrate the performance of the proposed estimators over the ML and the LS estimators.Keywords: burr xii distribution, robust estimator, m-estimator, least squares
Procedia PDF Downloads 42410977 A Framework for an Automated Decision Support System for Selecting Safety-Conscious Contractors
Authors: Rawan A. Abdelrazeq, Ahmed M. Khalafallah, Nabil A. Kartam
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Selection of competent contractors for construction projects is usually accomplished through competitive bidding or negotiated contracting in which the contract bid price is the basic criterion for selection. The evaluation of contractor’s safety performance is still not a typical criterion in the selection process, despite the existence of various safety prequalification procedures. There is a critical need for practical and automated systems that enable owners and decision makers to evaluate contractor safety performance, among other important contractor selection criteria. These systems should ultimately favor safety-conscious contractors to be selected by the virtue of their past good safety records and current safety programs. This paper presents an exploratory sequential mixed-methods approach to develop a framework for an automated decision support system that evaluates contractor safety performance based on a multitude of indicators and metrics that have been identified through a comprehensive review of construction safety research, and a survey distributed to domain experts. The framework is developed in three phases: (1) determining the indicators that depict contractor current and past safety performance; (2) soliciting input from construction safety experts regarding the identified indicators, their metrics, and relative significance; and (3) designing a decision support system using relational database models to integrate the identified indicators and metrics into a system that assesses and rates the safety performance of contractors. The proposed automated system is expected to hold several advantages including: (1) reducing the likelihood of selecting contractors with poor safety records; (2) enhancing the odds of completing the project safely; and (3) encouraging contractors to exert more efforts to improve their safety performance and practices in order to increase their bid winning opportunities which can lead to significant safety improvements in the construction industry. This should prove useful to decision makers and researchers, alike, and should help improve the safety record of the construction industry.Keywords: construction safety, contractor selection, decision support system, relational database
Procedia PDF Downloads 27310976 Optimization Model for Support Decision for Maximizing Production of Mixed Fruit Tree Farms
Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal
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We consider a linear programming model to help farmers to decide if it is convinient to choose among three kinds of export fruits for their future investment. We consider area, investment, water, productivitiy minimal unit, and harvest restrictions and a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability and initia investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market.Keywords: mixed integer problem, fruit production, support decision model, fruit tree farms
Procedia PDF Downloads 44710975 Vulnerability Analysis for Risk Zones Boundary Definition to Support a Decision Making Process at CBRNE Operations
Authors: Aliaksei Patsekha, Michael Hohenberger, Harald Raupenstrauch
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An effective emergency response to accidents with chemical, biological, radiological, nuclear, or explosive materials (CBRNE) that represent highly dynamic situations needs immediate actions within limited time, information and resources. The aim of the study is to provide the foundation for division of unsafe area into risk zones according to the impact of hazardous parameters (heat radiation, thermal dose, overpressure, chemical concentrations). A decision on the boundary values for three risk zones is based on the vulnerability analysis that covered a variety of accident scenarios containing the release of a toxic or flammable substance which either evaporates, ignites and/or explodes. Critical values are selected for the boundary definition of the Red, Orange and Yellow risk zones upon the examination of harmful effects that are likely to cause injuries of varying severity to people and different levels of damage to structures. The obtained results provide the basis for creating a comprehensive real-time risk map for a decision support at CBRNE operations.Keywords: boundary values, CBRNE threats, decision making process, hazardous effects, vulnerability analysis, risk zones
Procedia PDF Downloads 20510974 Context Specific E-Transformation Decision-Making Framework
Authors: A. Hol
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Nowadays, within quickly changing business environments, companies are often faced with specific problems where knowledge required to make timely decisions is often available however is not always readily accessible by the decision makers, in a required form. To identify if in any way via innovative system development companies could be assisted so that they can make quicker industry specific decisions in a given time and space, researchers conducted in depth case study investigation during which they studied company’s e-transformation recommendations, company’s current issues and problems as well as the nature of company’s pressing decisions. This study utilizes Scenario Based Analysis with the aim to help identify parameters crucial for the development of the system that could support decision making in a given time and space. Based on the findings, Context Specific e-transformation decision making framework is proposed.Keywords: e-transformation, business context, decision making, e-T Guide, ICT
Procedia PDF Downloads 44710973 Evaluation of a Hybrid Knowledge-Based System Using Fuzzy Approach
Authors: Kamalendu Pal
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This paper describes the main features of a knowledge-based system evaluation method. System evaluation is placed in the context of a hybrid legal decision-support system, Advisory Support for Home Settlement in Divorce (ASHSD). Legal knowledge for ASHSD is represented in two forms, as rules and previously decided cases. Besides distinguishing the two different forms of knowledge representation, the paper outlines the actual use of these forms in a computational framework that is designed to generate a plausible solution for a given case, by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an integrated environment. The nature of suitability assessment of a solution has been considered as a multiple criteria decision making process in ASHAD evaluation. The evaluation was performed by a combination of discussions and questionnaires with different user groups. The answers to questionnaires used in this evaluations method have been measured as a combination of linguistic variables, fuzzy numbers, and by using defuzzification process. The results show that the designed evaluation method creates suitable mechanism in order to improve the performance of the knowledge-based system.Keywords: case-based reasoning, fuzzy number, legal decision-support system, linguistic variable, rule-based reasoning, system evaluation
Procedia PDF Downloads 36010972 Web-Based Decision Support Systems and Intelligent Decision-Making: A Systematic Analysis
Authors: Serhat Tüzün, Tufan Demirel
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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review
Procedia PDF Downloads 27310971 Variogram Fitting Based on the Wilcoxon Norm
Authors: Hazem Al-Mofleh, John Daniels, Joseph McKean
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Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares.Keywords: non-linear wilcoxon, robust estimation, variogram estimation, wilcoxon norm
Procedia PDF Downloads 44710970 [Keynote Talk]: Evidence Fusion in Decision Making
Authors: Mohammad Abdullah-Al-Wadud
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In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty
Procedia PDF Downloads 41810969 System of System Decisions Framework for Cross-Border Railway Projects
Authors: Dimitrios J. Dimitriou, Maria F. Sartzetaki, Anastasia Kalamakidou
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Transport infrastructure assets are key components of the national asset portfolio. The decision to invest in a new infrastructure in transports could take from a few years to some decades. This is mainly because of the need to reserve and spent many capitals, the long payback period, the number of the stakeholders involved in the decision process and –many times- the investment and business risks are high. Decision makers and stakeholders need to define the framework and the outputs of the decision process taking into account the project characteristics, the business uncertainties, and the different expectations. Therefore, the decision assessment framework is an essential challenge linked with the key decision factors meet the stakeholder expectations highlighting project trade-offs, financial risks, business uncertainties and market limitations. This paper examines the decision process for new transport infrastructure projects in cross-border regions, where a wide range of stakeholders with different expectation is involved. According to a consequences analysis systemic approach, the relationship of transport infrastructure development, economic system development and stakeholder expectation is analysed. Adopting the on system of system methodological approach, the decision making the framework, variables, inputs and outputs are defined, highlighting the key shareholder’s role and expectations. The application provides the methodology outputs presenting the proposed decision framework for a strategic railway project in north Greece deals with the upgrade of the existing railway corridor connecting Greece, Turkey, and Bulgaria.Keywords: system of system decision making, managing decisions for transport projects, decision support framework, defining decision process
Procedia PDF Downloads 30010968 Decision Support System for the Management of the Shandong Peninsula, China
Authors: Natacha Fery, Guilherme L. Dalledonne, Xiangyang Zheng, Cheng Tang, Roberto Mayerle
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A Decision Support System (DSS) for supporting decision makers in the management of the Shandong Peninsula has been developed. Emphasis has been given to coastal protection, coastal cage aquaculture and harbors. The investigations were done in the framework of a joint research project funded by the German Ministry of Education and Research (BMBF) and the Chinese Academy of Sciences (CAS). In this paper, a description of the DSS, the development of its components, and results of its application are presented. The system integrates in-situ measurements, process-based models, and a database management system. Numerical models for the simulation of flow, waves, sediment transport and morphodynamics covering the entire Bohai Sea are set up based on the Delft3D modelling suite (Deltares). Calibration and validation of the models were realized based on the measurements of moored Acoustic Doppler Current Profilers (ADCP) and High Frequency (HF) radars. In order to enable cost-effective and scalable applications, a database management system was developed. It enhances information processing, data evaluation, and supports the generation of data products. Results of the application of the DSS to the management of coastal protection, coastal cage aquaculture and harbors are presented here. Model simulations covering the most severe storms observed during the last decades were carried out leading to an improved understanding of hydrodynamics and morphodynamics. Results helped in the identification of coastal stretches subjected to higher levels of energy and improved support for coastal protection measures.Keywords: coastal protection, decision support system, in-situ measurements, numerical modelling
Procedia PDF Downloads 18810967 Decision Making under Strict Uncertainty: Case Study in Sewer Network Planning
Authors: Zhen Wu, David Lupien St-Pierre, Georges Abdul-Nour
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In decision making under strict uncertainty, decision makers have to choose a decision without any information about the states of nature. The classic criteria of Laplace, Wald, Savage, Hurwicz and Starr are introduced and compared in a case study of sewer network planning. Furthermore, results from different criteria are discussed and analyzed. Moreover, this paper discusses the idea that decision making under strict uncertainty (DMUSU) can be viewed as a two-player game and thus be solved by a solution concept in game theory: Nash equilibrium.Keywords: decision criteria, decision making, sewer network planning, decision making, strict uncertainty
Procedia PDF Downloads 55210966 Prioritizing Temporary Shelter Areas for Disaster Affected People Using Hybrid Decision Support Model
Authors: Ashish Trivedi, Amol Singh
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In the recent years, the magnitude and frequency of disasters have increased at an alarming rate. Every year, more than 400 natural disasters affect global population. A large-scale disaster leads to destruction or damage to houses, thereby rendering a notable number of residents homeless. Since humanitarian response and recovery process takes considerable time, temporary establishments are arranged in order to provide shelter to affected population. These shelter areas are vital for an effective humanitarian relief; therefore, they must be strategically planned. Choosing the locations of temporary shelter areas for accommodating homeless people is critical to the quality of humanitarian assistance provided after a large-scale emergency. There has been extensive research on the facility location problem both in theory and in application. In order to deliver sufficient relief aid within a relatively short timeframe, humanitarian relief organisations pre-position warehouses at strategic locations. However, such approaches have received limited attention from the perspective of providing shelters to disaster-affected people. In present research work, this aspect of humanitarian logistics is considered. The present work proposes a hybrid decision support model to determine relative preference of potential shelter locations by assessing them based on key subjective criteria. Initially, the factors that are kept in mind while locating potential areas for establishing temporary shelters are identified by reviewing extant literature and through consultation from a panel of disaster management experts. In order to determine relative importance of individual criteria by taking into account subjectivity of judgements, a hybrid approach of fuzzy sets and Analytic Hierarchy Process (AHP) was adopted. Further, Technique for order preference by similarity to ideal solution (TOPSIS) was applied on an illustrative data set to evaluate potential locations for establishing temporary shelter areas for homeless people in a disaster scenario. The contribution of this work is to propose a range of possible shelter locations for a humanitarian relief organization, using a robust multi criteria decision support framework.Keywords: AHP, disaster preparedness, fuzzy set theory, humanitarian logistics, TOPSIS, temporary shelters
Procedia PDF Downloads 19310965 Optimization Model for Support Decision for Maximizing Production of Mixed Fresh Fruit Farms
Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal
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Planning models for fresh products is a very useful tool for improving the net profits. To get an efficient supply chain model, several functions should be considered to get a complete simulation of several operational units. We consider a linear programming model to help farmers to decide if it is convenient to choose what area should be planted for three kinds of export fruits considering their future investment. We consider area, investment, water, productivity minimal unit, and harvest restrictions to develop a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability, and initial investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market. Also, this tool help to support decisions for government and individual farmers.Keywords: mixed integer problem, fresh fruit production, support decision model, agricultural and biosystems engineering
Procedia PDF Downloads 43010964 Robust Stabilization against Unknown Consensus Network
Authors: Myung-Gon Yoon, Jung-Ho Moon, Tae Kwon Ha
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This paper considers a robust stabilization problem of a single agent in a multi-agent consensus system composed of identical agents, when the network topology of the system is completely unknown. It is shown that the transfer function of an agent in a consensus system can be described as a multiplicative perturbation of the isolated agent transfer function in frequency domain. Applying known robust stabilization results, we present sufficient conditions for a robust stabilization of an agent against unknown network topology.Keywords: single agent control, multi-agent system, transfer function, graph angle
Procedia PDF Downloads 44610963 Introduction of Robust Multivariate Process Capability Indices
Authors: Behrooz Khalilloo, Hamid Shahriari, Emad Roghanian
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Process capability indices (PCIs) are important concepts of statistical quality control and measure the capability of processes and how much processes are meeting certain specifications. An important issue in statistical quality control is parameter estimation. Under the assumption of multivariate normality, the distribution parameters, mean vector and variance-covariance matrix must be estimated, when they are unknown. Classic estimation methods like method of moment estimation (MME) or maximum likelihood estimation (MLE) makes good estimation of the population parameters when data are not contaminated. But when outliers exist in the data, MME and MLE make weak estimators of the population parameters. So we need some estimators which have good estimation in the presence of outliers. In this work robust M-estimators for estimating these parameters are used and based on robust parameter estimators, robust process capability indices are introduced. The performances of these robust estimators in the presence of outliers and their effects on process capability indices are evaluated by real and simulated multivariate data. The results indicate that the proposed robust capability indices perform much better than the existing process capability indices.Keywords: multivariate process capability indices, robust M-estimator, outlier, multivariate quality control, statistical quality control
Procedia PDF Downloads 28010962 Cascade Screening for Beta-Thalassemia in Pakistan: Relatives’ Experiences of a Decision Support Intervention in Routine Practice
Authors: Shenaz Ahmed, Hussain Jafri, Muhammed Faran, Wajeeha Naseer Ahmed, Yasmin Rashid, Yasmin Ehsan, Shabnam Bashir, Mushtaq Ahmed
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Low uptake of cascade screening for βeta-Thalassaemia Major (β-TM) in the ‘Punjab Thalassaemia Prevention Project’ (PTPP) in Pakistan led to the development of a ‘decision support intervention for relatives’ (DeSIRe). This paper presents the experiences of relatives of children with β-TM of the DeSIRe following its use by PTPP field officers in routine clinical practice. Fifty-four semi-structured qualitative interviews were conducted (April to June 2021) with relatives in seven cities in the Punjab province (Lahore, Sheikhupura, Nankana Sahab, Kasur, Gujranwala, Multan, and Faisalabad). Thematic analysis shows that participants were satisfied with the content of the DeSIRe and its delivery by the field officers in a family meeting. They understood the main purpose of the DeSIRe was to improve their knowledge of β-TM and its inheritance, to enable them to make decisions about thalassemia carrier testing, particularly before marriage. While participants raised concerns about the stigma of testing positive, they believed the DeSIRe was an appropriate intervention, which supported relatives to make informed decisions. Our findings show the DeSIRe is appropriate for use by healthcare professionals in routine practice in a low-middle income country and has the potential to facilitate shared decision-making about cascade screening for thalassemia. Further research is needed to prove the efficacy of the DeSIRe.Keywords: thalassemia, Pakistan, cascade screening, decision support
Procedia PDF Downloads 23410961 Robust Control of a Dynamic Model of an F-16 Aircraft with Improved Damping through Linear Matrix Inequalities
Authors: J. P. P. Andrade, V. A. F. Campos
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This work presents an application of Linear Matrix Inequalities (LMI) for the robust control of an F-16 aircraft through an algorithm ensuring the damping factor to the closed loop system. The results show that the zero and gain settings are sufficient to ensure robust performance and stability with respect to various operating points. The technique used is the pole placement, which aims to put the system in closed loop poles in a specific region of the complex plane. Test results using a dynamic model of the F-16 aircraft are presented and discussed.Keywords: F-16 aircraft, linear matrix inequalities, pole placement, robust control
Procedia PDF Downloads 29510960 Aircraft Line Maintenance Equipped with Decision Support System
Authors: B. Sudarsan Baskar, S. Pooja Pragati, S. Raj Kumar
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The cost effectiveness in aircraft maintenance is of high privilege in the recent days. The cost effectiveness can be effectively made when line maintenance activities are incorporated at airports during Turn around time (TAT). The present work outcomes the shortcomings that affect the dispatching of the aircrafts, aiming at high fleet operability and low maintenance cost. The operational and cost constraints have been discussed and a suggestive alternative mechanism is proposed. The possible allocation of all deferred maintenance tasks to a set of all deferred maintenance tasks to a set of suitable airport resources have termed as alternative and is discussed in this paper from the data’s collected from the kingfisher airlines.Keywords: decision support system, aircraft maintenance planning, maintenance-cost, RUL(remaining useful life), logistics, supply chain management
Procedia PDF Downloads 49410959 Machine Learning Predictive Models for Hydroponic Systems: A Case Study Nutrient Film Technique and Deep Flow Technique
Authors: Kritiyaporn Kunsook
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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting
Procedia PDF Downloads 36310958 A Robust Optimization Model for Multi-Objective Closed-Loop Supply Chain
Authors: Mohammad Y. Badiee, Saeed Golestani, Mir Saman Pishvaee
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In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown.Keywords: supply chain management, closed-loop supply chain, multi-objective optimization, goal programming, uncertainty, robust optimization
Procedia PDF Downloads 40910957 Digital Platform of Crops for Smart Agriculture
Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye
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In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.Keywords: prediction, machine learning, artificial intelligence, digital agriculture
Procedia PDF Downloads 7410956 Decision Traps of Military Leaders
Authors: Ahmet Ali Turk, Muhterem Bayram
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In this study, it is intended to determine that what kind of traps military leaders fall into during the decision making and how they make take a measure against them. In the study, the domestic and foreign literature on the military leadership has been reviewed and military decision-making process of the different countries has been introduced and study has been designed by making interviews as a sample with 50 people who had made military leadership. The issues resulting from the literature review that led to wrong decisions of military leaders and the points obtained as a result of interview have been evaluated by comparing. As a result, it has been emerged that the personnel who have made especially military leadership are in tendency of making the wrong decision due to decision traps such as excessive self-confidence, lack of experience, unplanned movement, hasty decision making and prohibitive conditions and also the need for increased situational awareness about this condition has been emerged.Keywords: military leadership, decision making, military decision making, military decision making traps
Procedia PDF Downloads 35010955 Balanced Scorecard (BSC) Project : A Methodological Proposal for Decision Support in a Corporate Scenario
Authors: David de Oliveira Costa, Miguel Ângelo Lellis Moreira, Carlos Francisco Simões Gomes, Daniel Augusto de Moura Pereira, Marcos dos Santos
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Strategic management is a fundamental process for global companies that intend to remain competitive in an increasingly dynamic and complex market. To do so, it is necessary to maintain alignment with their principles and values. The Balanced Scorecard (BSC) proposes to ensure that the overall business performance is based on different perspectives (financial, customer, internal processes, and learning and growth). However, relying solely on the BSC may not be enough to ensure the success of strategic management. It is essential that companies also evaluate and prioritize strategic projects that need to be implemented to ensure they are aligned with the business vision and contribute to achieving established goals and objectives. In this context, the proposition involves the incorporation of the SAPEVO-M multicriteria method to indicate the degree of relevance between different perspectives. Thus, the strategic objectives linked to these perspectives have greater weight in the classification of structural projects. Additionally, it is proposed to apply the concept of the Impact & Probability Matrix (I&PM) to structure and ensure that strategic projects are evaluated according to their relevance and impact on the business. By structuring the business's strategic management in this way, alignment and prioritization of projects and actions related to strategic planning are ensured. This ensures that resources are directed towards the most relevant and impactful initiatives. Therefore, the objective of this article is to present the proposal for integrating the BSC methodology, the SAPEVO-M multicriteria method, and the prioritization matrix to establish a concrete weighting of strategic planning and obtain coherence in defining strategic projects aligned with the business vision. This ensures a robust decision-making support process.Keywords: MCDA process, prioritization problematic, corporate strategy, multicriteria method
Procedia PDF Downloads 7110954 Computational Intelligence and Machine Learning for Urban Drainage Infrastructure Asset Management
Authors: Thewodros K. Geberemariam
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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space
Procedia PDF Downloads 14610953 Multi-Objective Electric Vehicle Charge Coordination for Economic Network Management under Uncertainty
Authors: Ridoy Das, Myriam Neaimeh, Yue Wang, Ghanim Putrus
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Electric vehicles are a popular transportation medium renowned for potential environmental benefits. However, large and uncontrolled charging volumes can impact distribution networks negatively. Smart charging is widely recognized as an efficient solution to achieve both improved renewable energy integration and grid relief. Nevertheless, different decision-makers may pursue diverse and conflicting objectives. In this context, this paper proposes a multi-objective optimization framework to control electric vehicle charging to achieve both energy cost reduction and peak shaving. A weighted-sum method is developed due to its intuitiveness and efficiency. Monte Carlo simulations are implemented to investigate the impact of uncertain electric vehicle driving patterns and provide decision-makers with a robust outcome in terms of prospective cost and network loading. The results demonstrate that there is a conflict between energy cost efficiency and peak shaving, with the decision-makers needing to make a collaborative decision.Keywords: electric vehicles, multi-objective optimization, uncertainty, mixed integer linear programming
Procedia PDF Downloads 17510952 Patient-Specific Modeling Algorithm for Medical Data Based on AUC
Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper
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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions
Procedia PDF Downloads 47210951 Online Robust Model Predictive Control for Linear Fractional Transformation Systems Using Linear Matrix Inequalities
Authors: Peyman Sindareh Esfahani, Jeffery Kurt Pieper
Abstract:
In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l2-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.Keywords: linear fractional transformation, linear matrix inequality, robust model predictive control, state feedback control
Procedia PDF Downloads 38610950 Knowledge Management in the Interactive Portal for Decision Makers on InKOM Example
Authors: K. Marciniak, M. Owoc
Abstract:
Managers as decision-makers present in different sectors should be supported in efficient and more and more sophisticated way. There are huge number of software tools developed for such users starting from simple registering data from business area – typical for operational level of management – up to intelligent techniques with delivering knowledge - for tactical and strategic levels of management. There is a big challenge for software developers to create intelligent management dashboards allowing to support different decisions. In more advanced solutions there is even an option for selection of intelligent techniques useful for managers in particular decision-making phase in order to deliver valid knowledge-base. Such a tool (called Intelligent Dashboard for SME Managers–InKOM) is prepared in the Business Intelligent framework of Teta products. The aim of the paper is to present solutions assumed for InKOM concerning on management of stored knowledge bases offering for business managers. The paper is managed as follows. After short introduction concerning research context the discussed supporting managers via information systems the InKOM platform is presented. In the crucial part of paper a process of knowledge transformation and validation is demonstrated. We will focus on potential and real ways of knowledge-bases acquiring, storing and validation. It allows for formulation conclusions interesting from knowledge engineering point of view.Keywords: business intelligence, decision support systems, knowledge management, knowledge transformation, knowledge validation, managerial systems
Procedia PDF Downloads 506