Search results for: a decision support methodology for the multi-attribute group decision-making (MAGDM) problem is developed

Authors: Viliam Makis, Farnoosh Naderkhani, Leila Jafari

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In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example.

Keywords: Bayesian control chart, semi-Markov decision process, quality control, partially observable process

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Authors: Serhat Tuzun, Tufan Demirel

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Multi-Criteria Decision Making (MCDM) is the modelling of real-life to solve problems we encounter. It is a discipline that aids decision makers who are faced with conflicting alternatives to make an optimal decision. MCDM problems can be classified into two main categories: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM), based on the different purposes and different data types. Although various MADM techniques were developed for the problems encountered, their methodology is limited in modelling real-life. Moreover, objective results are hard to obtain, and the findings are generally derived from subjective data. Although, new and modified techniques are developed by presenting new approaches such as fuzzy logic; comprehensive techniques, even though they are better in modelling real-life, could not find a place in real world applications for being hard to apply due to its complex structure. These constraints restrict the development of MADM. This study aims to conduct a comprehensive analysis of preference methods in MADM and propose an approach based on information. For this purpose, a detailed literature review has been conducted, current approaches with their advantages and disadvantages have been analyzed. Then, the approach has been introduced. In this approach, performance values of the criteria are calculated in two steps: first by determining the distribution of each attribute and standardizing them, then calculating the information of each attribute as informational energy.

Keywords: literature review, multi-attribute decision making, operations research, preference method, informational energy

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Authors: Niken Hartati

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This study provides a description of the problem and sources of social support that given to 90 mothers from poor households. Data were collected using structured interviews with the three main questions: 1) what kind of problem in mothers daily life, 2) to whom mothers ask for help to overcome it and 3) the form of the assistances that provided. Furthermore, the data were analyzed using content analysis techniques were then coded and categorized. The results of the study illustrate the problems experienced by mothers of poor households in the form of: subsistence (37%), child care (27%), management of money and time (20%), housework (5%), bad place of living (5%), the main breadwinner (3%), and extra costs (3%). While the sources of social support that obtained by mothers were; neighbors (10%), extended family (8%), children (8%), husband (7%), parents (7%), and siblings (5%). Unfortunately, more mothers who admitted not getting any social support when having problems (55%). The form of social support that given to mother from poor household were: instrumental support (91%), emotional support (5%) and informational support (2%). Implications for further intervention also discussed in this study.

Keywords: household problems, social support, mothers, poor households

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Authors: Dimitrios J. Dimitriou

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The decades after the end of the second War provide evidence that infrastructures investments contibute to economic development, on terms of productivity and income growth. In order to force productivity and increase competitiveness the financing of large transport infrastructure projects are on the top of the agenda in strategic planning process. Such a decision may take form some days to some decades and stakeholders as well as decision makers need tools in order to estimate the economic impact on natioanl economy of such an investment. The key question in such decisions is if the effects caused by the new infrastructure could be able to boost economic development on one hand, and create new jobs and activities on the other. This paper deals with the review of estimation of the mega transport infrastructure projects economic effects in economy.

Keywords: economic impact, transport infrastructure, strategic planning, decision making

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Authors: Aseela Alhinaai, Suliman Abdullah, Adil Albusaidi

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Knowledge has been considered an important asset in private and public organizations. It is utilized in the libraries sector to run different operations of technical services and administrative works. As a result, the International Federation of Library Association (IFLA) established a department “Knowledge Management” in December 2003 to provide a deep understanding of the KM concept for professionals. These are implemented through different programs, workshops, and activities. This study aims to identify the impact of the knowledge-sharing factors (technology, collaboration, management support) to improve decision-making at Sultan Qaboos University Libraries. This study conducted a quantitative method using a questionnaire instrument to measure the impact of technology, collaboration, and management support on knowledge sharing that lead to improved decision-making. The study population is the (SQU) libraries (Main Library, Medical Library, College of Economic and political science library, and Art Library). The results showed that management support, collaboration, and technology use have a positive impact on the knowledge-sharing process, and knowledge-sharing positively affects the decision making process.

Keywords: knowledge sharing, decision-making, information technology, management support, corroboration, Sultan Qaboos University

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Authors: Saeideh Salimpour, Sophie-Charlotte Viaux, Ahmed Azab, Mohammed Fazle Baki

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The Facility Layout Problem (FLP) is key to the efficient and cost-effective operation of a system. This paper presents a hybrid heuristic- and mathematical-programming-based approach that divides the problem conceptually into those of clustering and sequencing. First, clusters of vertically aligned facilities are formed, which are later on sequenced horizontally. The developed methodology provides promising results in comparison to its counterparts in the literature by minimizing the inter-distances for facilities which have more interactions amongst each other and aims at placing the facilities with more interactions at the centroid of the shop.

Keywords: clustering-sequencing approach, mathematical modeling, optimization, unequal facility layout problem

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Authors: Chiara Mansanta, Daniela Sani

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This paper studies the concept of intrapreneurship discovery for innovation and technology development related to the manufacturing industries set up in the center of Italy, in Marche Region. The study underlined the key drivers of the innovation process and the main factors that influence innovation. Starting from a literature study on open innovation, this paper examines the role of human capital to support company’s development. The empirical part of the study is based on a survey to 151 manufacturing companies that represent the 34% of that universe at the regional level. The survey underlined the main KPI’s that influence companies in their decision processes; then tools for these decision processes are presented.

Keywords: business model, decision making, intrapreneurship discovery, standard methodology

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Authors: D. Tedesco, G. Feletti, P. Trucco

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The present study aims to develop a Decision Support System (DSS) to support the operational decision of the Emergency Medical Service (EMS) regarding the assignment of medical emergency requests to Emergency Departments (ED). In the literature, this problem is also known as “hospital selection” and concerns the definition of policies for the selection of the ED to which patients who require further treatment are transported by ambulance. The employed research methodology consists of the first phase of revision of the technical-scientific literature concerning DSSs to support the EMS management and, in particular, the hospital selection decision. From the literature analysis, it emerged that current studies are mainly focused on the EMS phases related to the ambulance service and consider a process that ends when the ambulance is available after completing a request. Therefore, all the ED-related issues are excluded and considered as part of a separate process. Indeed, the most studied hospital selection policy turned out to be proximity, thus allowing to minimize the transport time and release the ambulance in the shortest possible time. The purpose of the present study consists in developing an optimization model for assigning medical emergency requests to the EDs, considering information relating to the subsequent phases of the process, such as the case-mix, the expected service throughput times, and the operational capacity of different EDs in hospitals. To this end, a Discrete Event Simulation (DES) model was created to evaluate different hospital selection policies. Therefore, the next steps of the research consisted of the development of a general simulation architecture, its implementation in the AnyLogic software and its validation on a realistic dataset. The hospital selection policy that produced the best results was the minimization of the Time To Provider (TTP), considered as the time from the beginning of the ambulance journey to the ED at the beginning of the clinical evaluation by the doctor. Finally, two approaches were further compared: a static approach, which is based on a retrospective estimate of the TTP, and a dynamic approach, which is based on a predictive estimate of the TTP determined with a constantly updated Winters model. Findings reveal that considering the minimization of TTP as a hospital selection policy raises several benefits. It allows to significantly reduce service throughput times in the ED with a minimum increase in travel time. Furthermore, an immediate view of the saturation state of the ED is produced and the case-mix present in the ED structures (i.e., the different triage codes) is considered, as different severity codes correspond to different service throughput times. Besides, the use of a predictive approach is certainly more reliable in terms of TTP estimation than a retrospective approach but entails a more difficult application. These considerations can support decision-makers in introducing different hospital selection policies to enhance EMSs performance.

Keywords: discrete event simulation, emergency medical services, forecast model, hospital selection

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Authors: Zouari Ezzeddine, Tarchoun Monaem

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Despite the multiplicity of advanced approaches, the concept of financial integration couldn’t be an explicit analysis. Indeed, empirical studies appear that the measures of international financial integration are one-dimensional analyses. For the ambivalence of the concept and its multiple determinants, it must be analyzed in multidimensional level. The interest of this research is a proposal of a decision support by multicriteria approach for determining the positions of countries according to their international and financial dependencies links with the behavior of financial actors (trying to make governance decisions or diversification strategies of international portfolio ...

Keywords: financial integration, decision support, behavior, multicriteria approach, governance and diversification

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Authors: Jacob Livingston Slosser, Naja Holten Moller, Thomas Troels Hildebrandt, Henrik Palmer Olsen

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In this paper, we propose a model for human-AI interaction in public administration that involves legal decision-making. Inspired by Alan Turing’s test for machine intelligence, we propose a way of institutionalizing a continuous working relationship between man and machine that aims at ensuring both good legal quality and higher efficiency in decision-making processes in public administration. We also suggest that our model enhances the legitimacy of using AI in public legal decision-making. We suggest that case loads in public administration could be divided between a manual and an automated decision track. The automated decision track will be an algorithmic recommender system trained on former cases. To avoid unwanted feedback loops and biases, part of the case load will be dealt with by both a human case worker and the automated recommender system. In those cases an experienced human case worker will have the role of an evaluator, choosing between the two decisions. This model will ensure that the algorithmic recommender system is not compromising the quality of the legal decision making in the institution. It also enhances the legitimacy of using algorithmic decision support because it provides justification for its use by being seen as superior to human decisions when the algorithmic recommendations are preferred by experienced case workers. The paper outlines in some detail the process through which such a model could be implemented. It also addresses the important issue that legal decision making is subject to legislative and judicial changes and that legal interpretation is context sensitive. Both of these issues requires continuous supervision and adjustments to algorithmic recommender systems when used for legal decision making purposes.

Keywords: administrative law, algorithmic decision-making, decision support, public law

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: B. Güney, Ç. Teke

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In recent years, rapid and correct decision making is crucial for both people and enterprises. However, uncertainty makes decision-making difficult. Fuzzy logic is used for coping with this situation. Thus, fuzzy linear programming models are developed in order to handle uncertainty in objective function and the constraints. In this study, a problem of a factory in food industry is investigated, required data is obtained and the problem is figured out as a fuzzy linear programming model. The model is solved using Zimmerman approach which is one of the approaches for fuzzy linear programming. As a result, the solution gives the amount of production for each product type in order to gain maximum profit.

Keywords: food industry, fuzzy linear programming, fuzzy logic, linear programming

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Authors: Antonio Boggia, Luisa Paolotti, Gianluca Massei, Lucia Rocchi, Elaine Pace, Maria Attard

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The aim of this paper is to present a methodology and a computer model for sustainability assessment based on the integration of Multi-criteria Decision Analysis (MCDA) with a Geographic Information System (GIS). It presents the result of a study for the implementation of a model for measuring sustainability to address the policy actions for the improvement of sustainability at territory level. The aim is to rank areas in order to understand the specific technical and/or financial support that is required to develop sustainable growth. Assessing sustainable development is a multidimensional problem: economic, social and environmental aspects have to be taken into account at the same time. The tool for a multidimensional representation is a proper set of indicators. The set of indicators must be integrated into a model, that is an assessment methodology, to be used for measuring sustainability. The model, developed by the Environmental Laboratory of the University of Perugia, is called GeoUmbriaSUIT. It is a calculation procedure developed as a plugin working in the open-source GIS software QuantumGIS. The multi-criteria method used within GeoUmbriaSUIT is the algorithm TOPSIS (Technique for Order Preference by Similarity to Ideal Design), which defines a ranking based on the distance from the worst point and the closeness to an ideal point, for each of the criteria used. For the sustainability assessment procedure, GeoUmbriaSUIT uses a geographic vector file where the graphic data represent the study area and the single evaluation units within it (the alternatives, e.g. the regions of a country, or the municipalities of a region), while the alphanumeric data (attribute table), describe the environmental, economic and social aspects related to the evaluation units by means of a set of indicators (criteria). The use of the algorithm available in the plugin allows to treat individually the indicators representing the three dimensions of sustainability, and to compute three different indices: environmental index, economic index and social index. The graphic output of the model allows for an integrated assessment of the three dimensions, avoiding aggregation. The presence of separate indices and graphic output make GeoUmbriaSUIT a readable and transparent tool, since it doesn’t produce an aggregate index of sustainability as final result of the calculations, which is often cryptic and difficult to interpret. In addition, it is possible to develop a “back analysis”, able to explain the positions obtained by the alternatives in the ranking, based on the criteria used. The case study presented is an assessment of the level of sustainability in the six regions of Malta, an island state in the middle of the Mediterranean Sea and the southernmost member of the European Union. The results show that the integration of MCDA-GIS is an adequate approach for sustainability assessment. In particular, the implemented model is able to provide easy to understand results. This is a very important condition for a sound decision support tool, since most of the time decision makers are not experts and need understandable output. In addition, the evaluation path is traceable and transparent.

Keywords: GIS, multi-criteria analysis, sustainability assessment, sustainable development

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Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

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Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

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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

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Authors: Nugzar Todua

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The paper studies the role of food labeling in order to promote healthy eating issue in Georgia. The main focus of the research is directed to consumer attitudes regarding food labeling. The methodology of the paper is based on the focus group work, as well as online and face to face surveys. The data analysis has been provided through ANOVA. The study proves that the impact of variables such as the interest, awareness, reliability, assurance and satisfaction of consumers' on buying decision, is statistically important. The study reveals that consumers’ perception regarding to food labeling is positive, but their level of knowledge and ability is rather low. It is urgent to strengthen marketing promotions strategies in the process of implementations of food security policy in Georgia.

Keywords: food labeling, buying decision, Georgian consumers, marketing research

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Authors: Sally Heyeon Hwang

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Within decision theory, there are normative principles that dictate how one should act in addition to empirical theories of actual behavior. As a normative guide to one’s actual behavior, evidential or causal decision-theoretic equations allow one to identify outcomes with maximal utility values. The choice that each person makes, however, will, of course, differ according to varying assignments of weight and probability values. Regarding these different choices, it remains a subject of considerable philosophical controversy whether individual subjects have the capacity to exercise free will with respect to the assignment of probabilities, or whether instead the assignment is in some way constrained. A version of this question is given a precise form in Richard Jeffrey’s assumption that free will is necessary for Newcomb’s paradox to count as a decision problem. This paper will argue, against Jeffrey, that decision theory does not require the assumption of libertarian freedom. One of the hallmarks of decision-making is its application across a wide variety of contexts; the implications of a background assumption of free will is similarly varied. One constant across the contexts of decision is that there are always at least two levels of choice for a given agent, depending on the degree of prior constraint. Within the context of Newcomb’s problem, when the predictor is attempting to guess the choice the agent will make, he or she is analyzing the determined aspects of the agent such as past characteristics, experiences, and knowledge. On the other hand, as David Lewis’ backtracking argument concerning the relationship between past and present events brings to light, there are similarly varied ways in which the past can actually be dependent on the present. One implication of this argument is that even in deterministic settings, an agent can have more free will than it may seem. This paper will thus argue against the view that a stable background assumption of free will or determinism in decision theory is necessary, arguing instead for a compatibilist decision theory yielding a novel treatment of Newcomb’s problem.

Keywords: decision theory, compatibilism, free will, Newcomb’s problem

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Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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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

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Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

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Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

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Authors: Rivindu Nethmin Bandara Menik Hitihamy Mudiyanselage, Matthias Hank Haeusler, Ben Doherty

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Investors and clients in the Architectural, Engineering and Construction industry find it difficult to understand complex datasets and reports with little to no graphic representation. The stakeholders examined in this paper include designers, design clients and end-users. Communicating embodied carbon information graphically and concisely can aid with decision support early in a building's life cycle. It is essential to create a common visualisation approach as the level of knowledge about embodied carbon varies between stakeholders. The tool, designed in conjunction with Bates Smart, condenses Tally Life Cycle Assessment data to a carbon hot-spotting visualisation, highlighting the sections with the highest amounts of embodied carbon. This allows stakeholders at every stage of a given project to have a better understanding of the carbon implications with minimal effort. It further allows stakeholders to differentiate building elements by their carbon values, which enables the evaluation of the cost-effectiveness of the selected materials at an early stage. To examine and build a decision-support tool, an action-design research methodology of cycles of iterations was used along with precedents of embodied carbon visualising tools. Accordingly, the importance of visualisation and Building Information Modelling are also explored to understand the best format for relaying these results.

Keywords: embodied carbon, visualisation, summarisation, data filtering, early-stage decision-making, materiality

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Authors: Katarina Rogulj, Ivana Racetin, Jelena Kilic

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In this study, the fuzzy logic approach (FLA) was developed for construction project management (CPM) under uncertainty and duality. The focus was on decision making in selecting the type of the glass facade for a residential-commercial building in the main design. The adoption of fuzzy sets was capable of reflecting construction managers’ reliability level over subjective judgments, and thus the robustness of the system can be achieved. An α-cuts method was utilized for discretizing the fuzzy sets in FLA. This method can communicate all uncertain information in the optimization process, taking into account the values of this information. Furthermore, FLA provides in-depth analyses of diverse policy scenarios that are related to various levels of economic aspects when it comes to the construction projects' valid decision making. The developed approach is applied to CPM to demonstrate its applicability. Analyzing the materials of glass facades, variants were defined. The development of the FLA for the CPM included relevant construction projec'ts stakeholders that were involved in the criteria definition to evaluate each variant. Using fuzzy Decision-Making Trial and Evaluation Laboratory Method (DEMATEL) comparison of the glass facade was conducted. This way, a rank, according to the priorities for inclusion into the main design, of variants is obtained. The concept was tested on a residential-commercial building in the city of Rijeka, Croatia. The newly developed methodology was then compared with the existing one. The aim of the research was to define an approach that will improve current judgments and decisions when it comes to the material selection of buildings facade as one of the most important architectural and engineering tasks in the main design. The advantage of the new methodology compared to the old one is that it includes the subjective side of the managers’ decisions, as an inevitable factor in each decision making. The proposed approach can help construction projects managers to identify the desired type of glass facade according to their preference and practical conditions, as well as facilitate in-depth analyses of tradeoffs between economic efficiency and architectural design.

Keywords: construction projects management, DEMATEL, fuzzy logic approach, glass façade selection

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Authors: S. Ghorbani, N. I. Polushin

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The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied.

Keywords: decision tree forest, GMDH, surface roughness, Taguchi method, turning process

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Authors: Wiem Daoud Ben Amor, Luis Martínez López, Hela Moalla Frikha

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Most of the multi-criteria group decision making (MCGDM) problems dealing with qualitative criteria require consideration of the large background of expert information. It is common that experts have different degrees of knowledge for giving their alternative assessments according to criteria. So, it seems logical that they use different evaluation scales to express their judgment, i.e., multi granular linguistic scales. In this context, we propose the extension of the classical additive ratio assessment (ARAS) method to the case of a hierarchical linguistics term for managing multi granular linguistic scales in uncertain contexts where uncertainty is modeled by means in linguistic information. The proposed approach is called the extended hierarchical linguistics-ARAS method (ARAS-ELH). Within the ARAS-ELH approach, the DM can diagnose the results (the ranking of the alternatives) in a decomposed style, i.e., not only at one level of the hierarchy but also at the intermediate ones. Also, the developed approach allows a feedback transformation i.e the collective final results of all experts able to be transformed at any level of the extended linguistic hierarchy that each expert has previously used. Therefore, the ARAS-ELH technique makes it easier for decision-makers to understand the results. Finally, An MCGDM case study is given to illustrate the proposed approach.

Keywords: additive ratio assessment, extended hierarchical linguistic, multi-criteria group decision making problems, multi granular linguistic contexts

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Authors: Eva Štěpánková

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The decision-making process is theoretically clearly defined. Generally, it includes the problem identification and analysis, data gathering, goals and criteria setting, alternatives development and optimal alternative choice and its implementation. In practice however, various modifications of the theoretical decision-making process can occur. The managers can consider some of the phases to be too complicated or unfeasible and thus they do not carry them out and conversely some of the steps can be overestimated. The aim of the paper is to reveal and characterize the perception of the individual phases of decision-making process by the managers. The research is concerned with managers in the military environment–commanders. Quantitative survey is focused cross-sectionally in the individual levels of management of the Ministry of Defence of the Czech Republic. On the total number of 135 respondents the analysis focuses on which of the decision-making process phases are problematic or not carried out in practice and which are again perceived to be the easiest. Then it is examined the reasons of the findings.

Keywords: decision making, decision making process, decision problems, concrete organization

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Authors: Kyle Burke Pfeiffer, Carmella Burdi, Karen Marsh

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The evacuation and shelter-in-place planning process employed by most jurisdictions within the United States is not informed by a scientifically-derived framework that is inclusive of the behavioral and policy-related indicators of public compliance with evacuation orders. While a significant body of work exists to define these indicators, the research findings have not been well-integrated nor translated into useable planning factors for public safety officials. Additionally, refinement of the planning factors alone is insufficient to support science-based evacuation planning as the behavioral elements of evacuees—even with consideration of policy-related indicators—must be examined in the context of specific regional transportation and shelter networks. To address this problem, the Federal Emergency Management Agency and Argonne National Laboratory developed an agent-based model to support regional analysis of zone-based evacuation in southeastern Georgia. In particular, this model allows public safety officials to analyze the consequences that a range of hazards may have upon a community, assess evacuation and shelter-in-place decisions in the context of specified evacuation and response plans, and predict outcomes based on community compliance with orders and the capacity of the regional (to include extra-jurisdictional) transportation and shelter networks. The intention is to use this model to aid evacuation planning and decision-making. Applications for the model include developing a science-driven risk communication strategy and, ultimately, in the case of evacuation, the shortest possible travel distance and clearance times for evacuees within the regional boundary conditions.

Keywords: agent-based modeling for evacuation, decision-support for evacuation planning, evacuation planning, human behavior in evacuation

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Authors: Arun Kumar, Neelesh Haulder

Abstract:

This paper identifies and resolves some key issues relating to a specific aspect within the supply chain logistics of the public health care industry in the Republic of Mauritius. The analysis and the proposed solution are performed using soft systems methodology (SSM). Through the application of this relevant systematic approach at problem solving, the aim is to obtain an in-depth analysis of the problem, incorporating every possible world view of the problem and consequently to obtain a well explored solution aimed at implementing relevant changes within the current supply chain logistics of the health care industry, with the purpose of tackling the key identified issues.

Keywords: soft systems methodology, CATWOE, healthcare, logistics

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Authors: Jong Woo Kim, Go Bong Choi, Sang Hwan Son, Dae Shik Kim, Jung Chul Suh, Jong Min Lee

Abstract:

The Markov Decision Process (MDP) based methodology is implemented in order to establish the optimal schedule which minimizes the cost. Formulation of MDP problem is presented using the information about the current state of pipe, improvement cost, failure cost and pipe deterioration model. The objective function and detailed algorithm of dynamic programming (DP) are modified due to the difficulty of implementing the conventional DP approaches. The optimal schedule derived from suggested model is compared to several policies via Monte Carlo simulation. Validity of the solution and improvement in computational time are proved.

Keywords: Markov decision processes, dynamic programming, Monte Carlo simulation, periodic replacement, Weibull distribution

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Authors: Yadav R. K., Baral S.

Abstract:

Introduction: Nepal is one of the world’s least developed countries and has a high incidence of tuberculosis (TB). The TB prevalence survey in 2019 showed 69,000 Nepalese is developing TB and 4,000 die every year. Given its disproportionate impact on the impoverished segments of society, TB often thrusts patients into extreme poverty or exacerbates their existing economic struggles. Consequently, not only the patients but also their families suffer from the loss of livelihood. This study aims to assess the support of nutritional allowance on treatment compliance among retreatment tuberculosis patients in Nepal. This is a secondary analysis of data from HMIS (Health Management Information System) to investigate treatment compliance among tuberculosis patients and its association with nutritional allowance. The study population consisted of all individuals (N=2972) who had received services from July 16, 2021, to December 14, 2022. The SPSS 21version was used to conduct descriptive and bivariate analysis. Out of the total TB patients (n=2972), a third-fourth (65.9%) of TB patients were male. More than one-tenth (12.3%) of respondents received a nutrition support allowance. The TB treatment compliance rate was more (89.91%) in the nutrition support allowance group compared to the non-nutritional support group (87.98%). TB patients who received the nutritional support allowance were nearly twice as likely to have a higher TB treatment compliance rate compared to those who did not receive the nutritional support allowance. Providing nutritional allowance support to tuberculosis (TB) patients can play a significant role in improving treatment compliance and outcomes. Age and the type of TB are important factors that have shown statistical significance in relation to treatment compliance. Therefore, it is recommended to provide nutritional allowance support to both new and retreatment TB patients. To enhance treatment compliance among TB patients, it is beneficial to provide timely nutrition allowances and arrange home visits by TB focal persons.

Keywords: nutrition, support, treatment compliance, TB, Nepal

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Authors: Ahmed M. El-Araby, Ibrahim Sabry, Ahmed El-Assal

Abstract:

The relationships between the industrial facilities, the capacity available for these facilities, and the costs involved are the main factors in deciding the correct selection of a facility layout. In general, an issue of facility layout is considered to be an unstructured problem of decision-making. The objective of this work is to provide a survey that describes the techniques by which a facility planning problem can be solved and also the effect of these techniques on the efficiency of the layout. The multi-criteria decision making (MCDM) techniques can be classified according to the previous researches into three categories which are the use of single MCDM, combining two or more MCDM, and the integration of MCDM with another technique such as genetic algorithms (GA). This paper presents a review of different multi-criteria decision making (MCDM) techniques that have been proposed in the literature to pick the most suitable layout design. These methods are particularly suitable to deal with complex situations, including various criteria and conflicting goals which need to be optimized simultaneously.

Keywords: facility layout, MCDM, GA, literature review

Procedia PDF Downloads 176