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This paper presents a multi-objective mathematical model to optimize and harmonize a supply chain in order to reduce costs, improve quality, and gain a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to enhance both quality and customer satisfaction and reduce the production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was first validated. The parameters of the proposed algorithm were then adjusted to solve the model using Multi-Objective Simulated Annealing (MOSA) algorithm. To validate the performance of the designed algorithm, some examples were solved based on General Algebraic Modeling System (GAMS). The MOSA algorithm achieved average errors of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in the average less than one minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS failed to reach an optimal solution for large problems in a reasonable computational time. The average error of the designed algorithm was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper. [ABSTRACT FROM AUTHOR]

This research paper focuses on the development of advanced mathematical models for disease diagnosis and prediction, and the subsequent creation of automated systems based on these models. These systems leverage a range of mathematical models and incorporate cutting-edge information technology achievements to provide medical professionals with valuable decision-making support. By amalgamating mathematical rigor and technological innovation, this research endeavors to enhance the accuracy and efficiency of medical diagnoses, thereby improving patient care and healthcare outcomes. This study delves into the persistent need for contemporary information systems, where information plays a crucial role in decision-making. It aims to provide an objective approach to addressing pressing medical challenges, particularly in disease diagnosis and prediction, enhancing the effectiveness of these critical tasks. Automated medical information systems, built on advanced mathematical models, significantly empower physicians. Machine diagnostics, relying on deterministic logic, the phase interval method, and information-probabilistic logic, bolster diagnostic capabilities. Functional entropy enables individuals to handle vague information, aiding decision-making. Assessing imprecision and uncertainty computationally diminishes subjectivity, while employing fuzzy set theory enhances diagnostic modeling. Mathematical models assess diagnostic indicators, and linguistic variables quantify resemblance. The diagnostic model for primary biliary cirrhosis and active hepatitis utilizes a diagnostic table and gradient projection. This comprehensive study advances medical diagnostics through mathematical models and automated systems, addressing critical healthcare challenges. [ABSTRACT FROM AUTHOR]

Assessing uncertainty in decision-making is a major challenge for DecisionMakers (DMs), and the g-Rung Orthopair Fuzzy Set (q-ROFS) as the direct extension of Intuitionistic Fuzzy Set (IFS) and Pythagorean Fuzzy Set (PFS) play a crucial role in this aspect. The Complex g-Run Orthopair Fuzzy Set (Cq-ROFS) is a strong tool to deal with imprecision, vagueness, and fuzziness by expanding the scope of Membership Degree (MD) and Non-Membership Degree (NMD) of q-ROFS from real to complex unit disc. In this paper, we develop some new Cq-ROF Hamacher Aggregation Operators (AOs), i.e., the Cq-ROF Hamacher Weighted Averaging (Cq-ROFHWA) operator, the Cq- ROFH Weighted Geometric (Cq-ROFHWG) operator, the Cq-ROFH Ordered Weighted Averaging (Cq-ROFHOWA) operator, and the Cq-ROFH Ordered Weighted Geometric (Cq-ROFHOWG) operator. Subsequently, we establish a novel Cq-ROF graph framework based on the Hamacher operator called Cq-ROFH Graphs (Cq-ROFHGs) and evaluate its energy and Randic energy. In particular, we compute the energy of a splitting Cq-ROFHG and shadow Cq-ROFHG. Further, we describe the notions of Cq-ROFH digraphs (Cq- ROFHDGs). Moreover, an algorithm is given to solve Multiple Attribute Group DecisionMaking (MAGDM) problems and the main steps are discussed clearly. Finally, a numerical instance related to the Facade Clothing Systems (FCS) selection is presented to show the effectiveness of the developed concepts in decision-making circumstances. In order to verify the effectiveness of our proposed scheme, a comparative analysis with previous approaches is provided. [ABSTRACT FROM AUTHOR]

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data Envelopment Analysis (DEA) technique is widely applied for performance assessment of Decision-Making Units (DMUs). Revenue Efficiency (RE) evaluation is one of the controversial subject matters that can be performed through DEA context. The amount of production and its prices are crucial factors in the RE. The classical DEA models consider the prices fixed and known, which are not the case in the real world. Also, the classical DEA models consider linear pricing in revenue assessment. However, most of real-world problems deal with nonlinear prices. This paper evaluates the RE given the piecewise linear theory in non-competitive situations. In doing so, a stepwise pricing function is introduced that allows the prices to vary with respect to the amount of production. As an innovative idea, a more accurate mathematical modeling for the RE is proposed. A dynamic weight function is defined in the maximum revenue optimization model that no longer considers prices fixed. A case study validates our proposed model. [ABSTRACT FROM AUTHOR]

Probabilistic linguistic term set (PLTS), an efficient tool to describe decision information, can sufficiently express decision makers' hesitation and preference. Probabilistic linguistic preference relation (PLPR) is based on PLTSs to describe the preference information of experts for paired alternatives. However, in practice, due to the complexity of the problem, the incompleteness of information and the lack of professional knowledge, the incomplete PLPR (InPLPR) with missing information often appears. Therefore, this paper proposes a decision-making method under InPLPR. Firstly, in order to fully consider the specific situation of missing values, missing linguistic term-InPLTS (MLT-InPLTS) is subdivided into missing single linguistic term-InPLTS (MSLT-InPLTS) and missing multiple linguistic terms-InPLTS (MMLT-InPLTS). Then, a two-stage mathematical optimization model of missing information estimation based on additive consistency, fuzzy entropy and hesitation entropy is established. Subsequently, aiming at the unacceptable consistency of complete PLPR (CPLPR) after filling in the missing values, a consistency improvement method based on the idea of gradient descent is proposed. Afterward, probabilistic linguistic weighted averaging (PLWA) operator is used to rank alternatives. Finally, medical supplier selection is taken as an example to verify the effectiveness of the proposed decision-making method, and the robustness and advantages of this method are illustrated by sensitivity analysis and comparison with other methods. [ABSTRACT FROM AUTHOR]

The paper considers some problems and peculiarities of modeling and efficient development in social and economic monitoring. As we know, accurate and enhanced monitoring of the market and customer data base is a necessary requirement for the organization's survival in intense competition. The research subject is a maintenance service distributed organization possessing a network of affiliates and representatives. Improving monitoring processes will ensure timely revealing potential situations of customers' change of a service company, broadening the customer data base and eventual strengthening the company's position in competition. The authors have used the methodology of conceptual and mathematical modeling based on monitoring intensity. The paper presents the analysis and heuristic approach towards rationalized management of monitoring researches, proposes modeling and algorithmization of managing customers' changing maintenance service distributed organization affiliates (representatives) and adaptive techniques of monitoring management under organizational and economic restrictions. Streamlining monitoring study enables to optimize the monitoring of clients, especially those expected to be ready to change the service organization. In our view, the obtained results are to be applied both in static and dynamic configuration of distributed companies. The article gives heuristic methods of planning monitoring researches in organizational and economic systems, featuring the accounting of restrictions to the total monitoring amount and providing short- and long-term monitoring planning in the course of data collection needed for management decisions to enhance the organization competitiveness. [ABSTRACT FROM AUTHOR]

In this paper, we propose new aggregation operators for multi-criteria decision making under linguistic settings. The proposed operators are based on two sets of criteria weights. Besides the primary conventional criteria weights, we introduce a method to deduce secondary criteria weights from the criteria evaluations, which reflect the role of the different criteria in discriminating among the alternatives. The properties of the proposed operators are investigated. An approach for the application of the said operators in a group multi-criteria decision making problem is presented. Following the same, the proposed operators are applied in a case study on supplier selection. The empirical validation of the proposed operators is performed on a set of 12 real datasets. Note : All usages of he, him, his in the paper, also refer to she, and her. [ABSTRACT FROM AUTHOR]

In risk investment, investors have to rely on uncertain information when it is difficult to obtain enough precise data. Dual hesitant fuzzy set (DHFS) is more applicable to deal with uncertain information because it involves membership degrees and non-membership degrees, which can validly describe positive and negative information, respectively. Although there has been research on decision-making based on the DHFS, the focus still remains on ranking the alternatives and choosing the best one, which cannot help investors to find the optimal portfolios. Therefore, to solve this problem, we mainly propose two novel portfolio selection models based on the DHFS in this paper. Firstly, we propose a Max-score dual hesitant fuzzy portfolio selection model with information preference (Model 3) for investors focusing on returns regardless of risks. Secondly, to consider the risks of portfolios, we improve Model 3 and develop a score-deviation dual hesitant fuzzy portfolio selection model with information preference and risk appetite (Model 5). Finally, a case study is conducted to highlight the effectiveness of the proposed models. A detailed sensitivity analysis and an efficient frontier analysis show that Model 5 can validly capture investors' information preferences and risk appetites. Furthermore, compared with the hesitant fuzzy portfolio model, Model 5 can offer more options to the investors with different information preferences. [ABSTRACT FROM AUTHOR]

To date, research on the prescribing decisions of physician lacks sound theoretical foundations. In fact, drug prescribing by doctors is a complex phenomenon influenced by various factors. Most of the existing studies in the area of drug prescription explain the process of decision-making by physicians via the exploratory approach rather than theoretical. Therefore, this review is an attempt to suggest a value conceptual model that explains the theoretical linkages existing between marketing efforts, patient and pharmacist and physician decision to prescribe the drugs. The paper follows an inclusive review approach and applies the previous theoretical models of prescribing behaviour to identify the relational factors. More specifically, the report identifies and uses several valuable perspectives such as the ‘persuasion theory - elaboration likelihood model’, the stimuli–response marketing model’, the ‘agency theory’, the theory of planned behaviour,’ and ‘social power theory,’ in developing an innovative conceptual paradigm. Based on the combination of existing methods and previous models, this paper suggests a new conceptual model of the physician decision-making process. This unique model has the potential for use in further research. [ABSTRACT FROM AUTHOR]

Copyright of Education Policy Analysis Archives / Archivos Analíticos de Políticas Educativas / Arquivos Analíticos de Políticas Educativas is the property of Educational Policy Analysis Archives & Education Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Decision-making processes in different organizations often have a hierarchical and multilevel structure with various criteria and sub-criteria. The application of hierarchical decision-making has been increased in recent years in many different areas. Researchers have used different hierarchical decision-making methods through mathematical modeling. The best-worst method (BWM) is a multi-criteria evaluation methodology based on pairwise comparisons. In this paper, we introduce a new hierarchical BWM (HBWM) which consists of seven steps. In this new approach, the weights of the criteria and sub-criteria are obtained by using a novel integrated mathematical model. To analyze the proposed model, two numerical examples are provided. To show the performance of the introduced approach, a comparison is also made between the results of the HBWM and BWM methodologies. The analysis demonstrates that HBWM can effectively determine the weights of criteria and sub-criteria through an integrated model. [ABSTRACT FROM AUTHOR]

Hesitant fuzzy linguistic term set (HFLTS) provides a new and useful tool for expressing decision makers (DMs)'s qualitative views and ideas since it allows DMs to hesitate about several possible linguistic terms. Consistency is the fundamental research in decision-making process under hesitant fuzzy linguistic preference relations (HFLPRs) decision-making environment and the distance measure is utilized as a technology to measure the consistency level of HFLPRs. However, traditional distance measures were developed based on an assumption that the number of linguistic terms in the corresponding HFLTSs is the same. Some optimization models to improve consistency level of HFLPRs did not give DMs strong sense of participation and a sense of respect for their preferences. To solve the above issues, in this paper, a new distance measure of HFLTSs is defined, and some desirable properties are discussed. After that, in line with the distance measure, we propose a consistency index of HFLPRs by computing the deviation between the normalized hesitant fuzzy linguistic preference relation (N-HFLPR) and its expected hesitant fuzzy linguistic preference relation (E-HFLPR). Furthermore, for unacceptable consistent HFLPRs, some local revised strategies provided by an automatic iterative algorithm are used to modify original HFLPRs until it satisfies acceptable consistency. Finally, some comparisons between the existing methods and our proposed approaches are to demonstrate the feasibility of the proposed approaches by utilizing several illustrative examples. [ABSTRACT FROM AUTHOR]

Supplier selection is the process by which firms identify, evaluate and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm's operational and financial resources and is considered as an important determinant of the success of its supply chain. In spite of being strategically important to organizations, the decision for supplier selection is often complex and unstructured. Furthermore, it is inherently a multi-criterion decision-making (MCDM) problem, which pertains to structuring and solving decision problems involving multiple criteria. The paper provides a framework to analyze and evaluate supplier selection in Indian pharmaceutical sector (IPS) using MCDM technique of fuzzy analytic hierarchy process. It intends to improve managerial decision-making in the IPS in developing a supplier selection strategy based on multi-criteria evaluation technique. [ABSTRACT FROM AUTHOR]

Mobile devices have gained popularity worldwide. The mobile device flexibility has encouraged users to turn their mobile devices into primary hubs for storing information. This paper adopts the classical CW-Lite security models as the framework and the human behavioral patterns as the context. The selected behavioral aspects refer to mobile application and mobility, deployed as major characteristics that determine security control decisions. This proposed paper requires the application of human behavioral context on mobile phones. The solution involves the novel use of behavioral aspects to improve the security of mobile phones. Two important scenarios are incorporated: analytic hierarchy process (AHP) mobile application and AHP mobility. The proposed methodology is superior because it can detect the change in the user behavior in comparison with an intruder. The applied intelligent human behavioral context on the CW-Lite model shows the advantages of AHP in detecting the changes in the user behavior and in authenticating the identity of the main user. These advantages ensure reliability and security of the phone. [ABSTRACT FROM AUTHOR]

In this paper, we are focusing on creating a pupil behaviour model in a lesson. In terms of game theory, we consider the lesson to be a conflicting situation, which gives us an opportunity to use the game theory methods to model and then analyze the lesson in terms of its dynamics. We consider a "profit" to be the satisfaction of the personal needs of the elements of this dynamic system, i.e. pupils and teachers. As an external manifestation of decisionmaking, we observe the disruption of the lesson by pupils' behaviour not in accord with accepted standards. This allows us to capture essential data, to which we compare the data created by the model. [ABSTRACT FROM AUTHOR]

Highlights • Use of outdoor microclimate maps to compare different design options at urban scale. • Tool to evaluate impact on urban comfort of the place/city garden design. • Decision-making tool to improve urban climate design considering outdoor comfort. Abstract The study is aimed at evaluating the potential effects of alternative design solutions with different green elements on outdoor microclimate with relation to a real case study application. The study has been commissioned in the framework of the follow up of a design competition, launched by the Municipality of Cesena to reshape a square in the historic city center, when a public debate raised around the arrangement of trees and green surfaces envisaged by the architectural layout. Different options were considered and the design team and the public authorities sought for evidences on the deriving benefits in the respective configurations in order to properly drive the process. Thus the scientific research approach was applied to investigate the potential impacts according to a microclimate oriented perspective. The outcomes showed that green surfaces significantly improved the outdoor comfort conditions compared to original paved ones and that a minor contribution derived by the trees arrangement. The paper reports the applied methodology according to the specific context, the interpretation of results and how they have been translated into user friendly visualizations in order to make them understandable to a broader and non technical audience. [ABSTRACT FROM AUTHOR]

A number of effective methods for multiple-attribute group decision making (MAGDM) with interval-valued intuitionistic fuzzy numbers (IVIFNs) have been proposed in recent years. However, the different methods frequently yield different, even sometimes contradictory, results for the same problem. In this paper a novel criterion to determine the advantages and disadvantages of different methods is proposed. First, the decision-making process is divided into three parts: translation of experts' preferences, aggregation of experts' opinions, and comparison of the alternatives. Experts' preferences aggregation is considered the core step, and the quality of the collective matrix is considered the most important evaluation index for the aggregation methods. Then, methods to calculate the similarity measure, correlation, correlation coefficient, and energy of the intuitionistic fuzzy matrices are proposed, which are employed to evaluate the collective matrix. Thus, the optimal method can be selected by comparing the collective matrices when all the methods yield different results. Finally, a novel approach for aggregating experts' preferences with IVIFN is presented. In this approach, experts' preferences are mapped as points into two-dimensional planes, with the plant growth simulation algorithm (PGSA) being employed to calculate the optimal rally points, which are inversely mapped to IVIFNs to establish the collective matrix. In the study, four different methods are used to address one example problem to illustrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Next generation wireless networks will integrate various heterogeneous technologies like WLAN, WiMax and cellular technologies etc., to support multimedia services with higher bandwidth and guaranteed quality of service (QoS). In order to keep the mobile user always connected to the best wireless network in terms of QoS parameters and user preferences, an optimal network selection technique in heterogeneous networks is required. This paper proposes a novel fuzzy-Analytic Hierarchy Process (AHP) based network selection in heterogeneous wireless networks. Triangular fuzzy numbers are used to represent the elements in the comparison matrices for voice, video and best effort applications. Deriving crisp weights from these fuzzy comparison matrices is a challenging task. When extent analysis method is applied, irrational zero weights are obtained for some attributes. Due to this, many important criteria are not considered in the decision making process. To overcome this problem, a new non-linear fuzzy optimization model for deriving crisp weights from fuzzy comparison matrices for network selection is presented. The weights obtained from this model are more consistent than the existing optimization models. Also, parameterized utility functions are used to model the different Quality of Service (QoS) attributes (bandwidth, delay, jitter, bit error rate) and user preferences (cost) for three different types of applications. Finally, scores are calculated exclusively for each network by three MADM (Multiple Attribute Decision Making) methods Simple Additive Weighting (SAW), TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and MEW (Multiplicative Exponential Weighting). Results show that the MEW method gives more appropriate scores with utility functions than the SAW and TOPSIS methods. [ABSTRACT FROM AUTHOR]

A consensus reaching process is an iteratively developed negotiation process designed to ensure that a mutual agreement is reached by decision makers. To incorporate distribution information of hesitant fuzzy sets, probability-hesitant fuzzy sets have recently been proposed. In the context of probability-hesitant fuzzy preference relations (PHFPR), this paper aims to provide a novel consensus reaching process for group decision making problems. By means of fuzzy preference relations, an optimization based consistency improvement process is proposed to deal with the inconsistencies in a given PHFPR. Consensus measures that are developed based on the distances between the individuals are computed on three levels: an alternative pair level, an alternatives level, and a preference relations level. An algorithm that adopts a local feedback strategy is designed to improve the consensus reaching process. The feedback strategy sequentially identifies the preferences with respect to the position and the anti-ideal individuals who need to change, after which the convergence of the proposed algorithm is proven. The novelty of the proposed strategy is that it avoids the need to compute the collective preference relations and recommendations are generated for the individuals in their original domains. Finally, some numerical examples taken from the literature are given to compare the proposed approaches with existing studies. The obtained results confirm the theoretical analysis and highlight the advantages of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-demand negotiation model that takes the effect of human users’ psychological characteristics into consideration. Specifically, in our model each negotiating agent's preference over its demands can be changed, according to human users’ attitudes to risk, patience and regret, during the course of a negotiation. And the change of preference structures is determined by fuzzy logic rules, which are elicited through our psychological experiments. The applicability of our model is illustrated by using our model to solve a problem of political negotiation between two countries. Moreover, we do lots of theoretical and empirical analyses to reveal some insights into our model. In addition, to compare our model with existing ones, we make a survey on fuzzy logic based negotiation, and discuss the similarities and differences between our negotiation model and various consensus models. [ABSTRACT FROM AUTHOR]

The modeling and solving of multi-criteria decision-making (MCDM) problems under uncertainty is still a challenging topic. In real-life decision-making, using linguistic terms to represent experts’ judgments is suitable and straightforward since precise quantitative values may often be unavailable or the cost for their computation is too high. The introduction of hesitant fuzzy linguistic term sets (HFLTSs) was motivated by the limitations of prior linguistic fuzzy models and need for richer linguistic tools. However, since their introduction, comparing HFLTSs is still one of the major concerns of researchers in this area. The existing approaches in the literature commonly rely on (1) labels and intervals from the linguistic terms as the central elements of an envelope-based approach or (2) linguistic scale functions as the basis of a distance-based approach. The two approaches retain certain shortcomings resulting information distortion and loss which may inevitably degrade their credibility. In this paper, the authors are involved in the recent proposal of combining outranking approaches with HFLTSs in an MCDM context. After reviewing the existing approaches, an outranking method based on a novel knowledge-based paradigm for comparing HFLTSs is developed. Alternatively, the paradigm's foundations are the introduced concepts of fuzzy preference relations and profiles considering uncertainty degrees in decision makers’ assessments. The paradigm is then associated with a multi-criteria relational clustering (MCRC) algorithm that additionally extracts fuzzy preference relations between the resultant clusters. Last, an illustrative example is given to verify the appropriateness and efficacy of the developed approach and comparisons are made with other existing ones. [ABSTRACT FROM AUTHOR]

Abstract: The paper focuses on the assessment of greenhouse gas emissions produced by the walls of buildings according to their lifespan. These assessments take account of the construction, maintenance and end of life. The contribution of the utilization phase must be equivalent for all technical solutions for a given usage function. In the first part of the paper, the methodology is described by (1) considering a unit area of wall (i.e. 1 square metre), (2) determining a long service life, (3) choosing technical solutions in agreement with the specifications, (4) establishing the lifespan of each technical solution according to experts, (5) finding the corresponding greenhouse gas index from an appropriate database, and finally (6) modelling the evolution of these indicators with time. Several technical solutions (concrete, brick, stone, wood, aerated concrete) are considered and lifespans range from a few years to centuries. The results of this analysis suggest and quantify the important impact of lifespan on greenhouse gas emission indicators. For example, the best technical solution for a short lifetime can be the worst on a longer duration and vice versa. In the second part, since the lifespan of a product is very difficult to determine objectively, it is considered as a variable. The numerical results presented point out the need to revisit the current life cycle tools. [Copyright &y& Elsevier]

Abstract: Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks. [Copyright &y& Elsevier]

Super-organization is a new mode of development which provides the enterprises with new ideas to use external resources to the greatest extent so as to gain competitive edge in the background of globalization. Knowledge conversion among organizations can promote the formation of super-organizations. This paper is intended to analyze the process of the formation of super-organizations through employing Game Theory. It is found that the main driving factor for the super-organizational formation is from knowledge conversion, which is caused by the differences in the storage of knowledge possessed by different organizations and the ensuing comparative advantages acquired by these organizations. In the light of this observation, this paper tries to discuss the super-organizational formation mechanism with game theory and from the perspective of knowledge conversion, and to explore the strategies that the organizations may take in the process of maximizing their profits. [ABSTRACT FROM AUTHOR]

Abstract: On the basis of Brennan''s (1998) dynamic investment decision model and engineering management, this paper builds an investor''s learning-decision model, allowing for the uncertainty of the expected return and the investor''s learning process. In our model, the variable of learning cost is added, which modifies the condition for decision of the investor, and assumes that the investment payoff cannot be observed, if an investor does not pay the learning cost. With the above conditions, this paper derives the optimal result of a three-period decision model on the basis of an investor''s learning behavior and decision engineering. The empirical evidence verifies the result of our model in this paper. [Copyright &y& Elsevier]

Abstract: Historical commercial districts in city centres in Turkey face social, cultural, environmental and economical challenges of managing rapid urban and economical development in the last two decades. They are being subjected to dramatic physical deterioration and rapid social and economical decline. Enriching relationships between local tradesmen associations, local authorities and non-governmental organisations through a structured engagement process can deliver innovative new revitalisation approaches and design options towards sustainable futures of historical commercial districts both in Turkey and in the world. This paper presents a community engagement model, which can be seen as the revival of a guild system (Lonca), which is unique to Ottoman Turkish culture, for the sustainable future of a historical commercial district in a Turkish city, Bursa. One of the main objectives of this study is to discuss and evaluate successes and failures of this community engagement model. Other objective of the paper is to discuss the effectiveness of this civil organisation for creating scenarios about sustainable future of a historical commercial district. The key finding of this study shows community engagement models should support public decision making by developing a coherent framework to identify the sustainable future scenarios with multi- or interdisciplinary collaborations. [Copyright &y& Elsevier]

Abstract: The minimum size of subjects required for the research on human health, thermal comfort and productivity is a frequently asked question. In this paper the idea of power analysis, which helps to determine required sample size as well as to interpret research results, is introduced in order to promote good practice of power analysis in the context of human and building environment relationship research. How to calculate effect size from published article or experimental data is presented with plenty of examples. The effect sizes of several physiological and psychological measurements indicating the effect of indoor environment quality on human health, thermal comfort and productivity are presented, which could be worked as references when researchers planning their own studies. How to determine required sample size when planning a study and how to interpret the research results with power analysis are also illustrated step by step with samples. Finally how to make decisions when evaluating the study results is summarized. It is expected that these examples and the summary could help researchers to better apply power analysis in indoor environment quality (IEQ) studies. Some statistical terms used in this paper, such as power analysis, effect size, and t-test, etc., are explained in detail in the . [Copyright &y& Elsevier]

This paper presents a mathematical decision-making tool that is easy to use and express mathematically, requires only a little mathematical expertise, and gives results that are easy to interpret. The tool has been used extensively as part of a term project in a precalculus class. The decision-making process forces students to consider the Christian ideals of faith, morals, and ethics, and to use them in a quantitative form in order to make their decision. The overall success of the tool comes from the integration of several different kinds of student learning experiences in a properly designed project. That is to say, the student must look deeply into his or her own core values in an effort to quantify his or her feelings toward God, faith, morals, and ethics. This decision-making mathematical tool is something that students can take with them as they journey through life. It is the primary objective of this paper to identify the tool, to show how it is used, and to encourage other Christian educators to use it in their classes too. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a robot that actively communicates with a human, and explicitly extracts information from the human mind that is rarely expressed as verbal information, The Spoke dialogue system for information collection must independently decide whether it may or may not start communicating with a human. In this paper, we assume that the system begins to communicate with a human sitting and working at a desk, analyze the relationship between his behavioral pattern and the decisions made by the other humans, ie., whether or not to communicate with the working man, and construct the decision model for the system to start communicating with the human using hidden Markov model. [ABSTRACT FROM AUTHOR]

This paper reports experiments on applying machine learning for identifying functional arguments of verbs such as logical subjects. In particular, it is shown that using decision trees for functional arguments identification is beneficial. The paper also argues that linguistically- motivated features gathered from a large corpus can capture functional information. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of consensus formation of adaptive agents. We consider a group of adaptive agents required to make a group decision. The adaptive agents are modeled as having their own preference order as rational agents with different levels of adaptation. In order to make a consistent group decision, they are required to derive the group preference by aggregating their preference orders. Each agent, as both a rational and an autonomous agent, may behave to satisfy its own preference order. However, it may pose a difficult issue if priority is given to each agent's preference order. It is known that it is impossible to derive the consistent group preference order by summarizing individual preference orders. Therefore, in a group of humans, prior to a group decision, each member often changes his own views and modifies his own preference order. In this paper, we provide an adaptive model for consensus formation. Each agent modifies its own preference order by considering the others' preference orders and the status of the group decision. We also consider the heterogeneity of agents by reflecting different levels of adaptation. We show that a group of rational agents with different levels of adaptation enhances the possibility of reaching consensus. Heterogeneity among agents also affects the content of consensus formation. We develop a prototype model which is applied to the problem domain of allocating Internet resources. © 2000 Scripta Technica, Syst Comp Jpn, 31(7): 20–28, 2000 [ABSTRACT FROM AUTHOR]

Consistency analysis is very important to ensure the reasonable ranking order. However, all previous consistency concepts for interval multiplicative preference relations (IMPRs) are insufficient to address this type of preference relations. This paper introduces a new consistency concept for IMPRs that is a natural extension of the Saaty’s consistency concept for multiplicative preference relations. Several desirable properties are discussed, and the relationship between the new concept and two previous ones is studied. Then, a method to judge the consistency of IMPRs is proposed. Considering the inconsistent case, a 0–1 mixed programming model to derive consistent IMPRs from inconsistent ones is established. To determine missing values in incomplete HFPRs, 0–1 mixed programming models are constructed that can address the situation where ignore objects exist. Meanwhile, illustrative examples are offered to show the feasibility and efficiency of the developed theoretical results, and comparison analysis is provided. Finally, a consistency analysis based algorithm to decision making with IMPRs is developed that can address inconsistent and incomplete cases. [ABSTRACT FROM AUTHOR]

The paper presents a mathematical model of process optimisation for the architectural layout of small apartments in the multi-family buildings in dense city centre housing area. The model was used to analyse and select an optimal version of the functional layout from 6 chosen solutions which are frequently used in the modern implementations of residential buildings. To solve the issue, the modified Chang's multicriteria comparative analysis - the EA FAHP (Extent Analysis Fuzzy Analytic Hierarchy Process) was used. The discussed model presents a new approach towards the issue which was so far lacking in the literature. Advantages of the adopted method of assessment were presented in the final conclusions. [ABSTRACT FROM AUTHOR]

Dyslipidemia is a common pathology throughout the industrialized world, and HMG-CoA reductase inhibitors (statins) are often administered to treat elevated lipid levels. Substantial concern has been raised regarding the aggressive clinical lowering of cholesterol, particularly in light of a growing body of research linking low circulating lipid levels with negative behavioral outcomes in both human samples and non-human primate models. In 2009, Goldstein and colleagues tentatively speculated that the greed, impulsiveness, and lack of foresight that lead to the worldwide economic collapse in 2007-2008 could have been caused (in part) by depressed population cholesterol levels resulting from the widespread use of statins by workers in the financial services industry. This paper reviews the literature that links low circulating lipid levels with neurobehavioral dysfunction, develops Goldstein and colleagues' initial speculation into a formal hypothesis, and proposes several specific studies that could rigorously empirically evaluate this hypothesis. [ABSTRACT FROM AUTHOR]

The single-valued neutrosophic set (SVNS) can be easier to describe the incomplete, indeterminate and inconsistent information, and Archimedean t-conorm and t-norm (ATT) can generalize most of the existing t-conorms and t-norms, including Algebraic, Einstein, and Hamacher Frank t-conorms and t-norms. In this paper, we extended ATT to the single-valued neutrosophic numbers (SVNNs), and proposed a single-valued neutrosophic number-weighted averaging (SVNNWA) operator and a single-valued neutrosophic number-weighted geometric (SVNNWG) operator based on ATT. First, we presented some new operational laws for SVNNs based on ATT, and discussed some special cases and properties of them. Then we proposed the SVNNWA and SVNNWG operators, and studied some properties and special cases of them. Further, we gave the decision-making methods for multiple attribute decision-making (MADM) and multiple attribute group decision-making (MAGDM) problems based on these operators. Finally, two examples are given to verify the developed approaches. [ABSTRACT FROM AUTHOR]

As an important part of cloud manufacturing, service composition and optimal selection (SCOS) has attracted many scholars' attentions. Although there are a large number of researches devoted to this field, plenty of challenges still remain, such as the difficulties of coping with real-life uncertainties and improving efficiency in making decisions. To this end, a bi-objective service composition and optimal selection (BoSCOS) problem which takes both QoS and robustness into account is firstly studied in this paper, its mathematical model is established after the two criteria are constructed. Then, a strengthened multi-objective gray wolf optimizer (SMOGWO) is developed to solve the above model with high efficiency, in which three improvement strategies are introduced to the original MOGWO: (1) the opposition based learning (OBL) strategy is adopted to improve the quality of initial population; (2) a leader update strategy is designed to explore better search leaders and balance the exploitation and exploration abilities; (3) an improved archiving strategy is developed to retain all possible dominant individuals, so as to improve the population diversity based on the Pareto dominance. Finally, the effectiveness of the proposed algorithm is verified on 8 BoSCOS problems with different scales and 17 benchmark functions, results show that the improvement strategies are effective and SMOGWO is superior to its competitors in terms of convergence and population diversity. • A BoSCOS problem considering both QoS and robustness is firstly proposed. • The robustness of a CMS is formulated in terms of response time caused by exceptions. • A leader update strategy is designed to explore better search leaders for SMOGWO. • An improved archive mechanism is developed to improve the population diversity. • Experimental results show that SMOGWO is obvious superior to common-used algorithms. [ABSTRACT FROM AUTHOR]

Research has shown that construction projects in Saudi Arabia have exhibited poor performance for the past three decades. The traditional risk management practices have been ineffective at helping contractors deliver projects on time and within budget while meeting quality expectations. Studies have identified that client decision making is one of the main causes of risks that occur on projects in Saudi Arabia. This paper proposes a new risk management model that can minimize client decision making, and enable the client to utilize expertise, thereby improving project quality and performance. The model is derived from the Information Measurement Theory (IMT) and Performance Information Procurement System (PIPS), both developed at Arizona State University in the United States (U.S.). The model has been tested over 1800 times in both construction and non-construction projects, showing a decrease in required management by owner by up to 80% and an increase in efficiency up to 40%. [ABSTRACT FROM AUTHOR]

Supply Chain consists of various components like supplier, manufacturer, factories, warehouses, distribution agents, customers, etc. Supply Chain Management encompasses all the activities from moving goods from sourcing to consumption. Sales forecasting is a part of downstream activity of supply chain and is the process of predicting future sales of the product. It helps in making informed business decisions. In this paper a study of various sales forecasting algorithms is done and results of sales of oral-care products are calculated using Back-Propagation Neural Network Model. The error rate for different products is also calculated. [ABSTRACT FROM AUTHOR]

In an era of globalization, we witness the encounter of cultures and the exchanges between them. Often, the cultural influences affect the consumer's decision to purchase goods. Many cultures have their own specific product offer, which is a way of promoting them. In this regard, a good example is the media culture called "anime". As a result, the consumer is faced with many decisions and its choice is influenced by many internal and external factors. When studying the market fluctuations due to the social, cultural, or otherwise influences, which may create new categories of consumers, we consider it is important to analyse the consumer behavior in the systemic terms, which could lead us to a new overview of the effects of these various influences. This paper proposes a mathematical model, starting from an original scheme, based on the Veblen theory. The study uses a simple matrix algorithm for the optimal solution of the dynamical systems with quadratic cost function. [ABSTRACT FROM AUTHOR]

In general, the result from conceptual design phase is described by text annotation and hand sketch. This information is not of a nature to provoke positive decisions for investment. Especially, in inventive design if decision maker (experts) have no time or little time, the result is often a good reason for projects to be abandoned (or placed on a shelve). Such situation is often due to their abstract description of a solution concept: often a rough graphical sketch and several sentences as written annotations. In parallel, computer support to conceptual design phase is not overwhelmed with many tools, they offer a useful tool for the concept generation but still poorly support investigating possible solutions, lacking of tools to size the geometry of the concepts and analyze their (physical) behavior. In order to give more chance for the solution concept to be selected and further developed in the next design phase. This paper will presents an approach and the software framework (RSC-Modeler) to support the need of tools for a type of conceptual design result: an inventive solution concept obtained from IDM-TRIZ (inventive design method based on TRIZ), by applied engineering analysis task and optimization techniques. Finally, a case study is given to demonstrate the practicability of the proposed approach and software framework. [ABSTRACT FROM AUTHOR]

More and more asset management organizations are relying on trusted data to drive their decision making process. Yet little systematic research has been performed regarding the generation of trusted data. Trusted data has a clear, defined and consistent quality which meets the expectations of the user. The aim of the research presented in this paper is to develop a conceptual model to support asset management organizations with regards to their development of data infrastructures which produce trusted data and provide organizations with the capability to make the right decisions at the right time. The autonomous characteristics of agent-based systems and process orientation of discrete-event simulation are combined in our conceptual model. In this way both the autonomous social behavior of organizations and their business processes can be modelled. Several predefined components containing the behavior of agents through various coordination mechanisms have been developed. These components can be further customized to model an empirical situation more closely. The approach is illustrated with a case study in the asset management domain. [ABSTRACT FROM AUTHOR]

Activity recognition in smart homes enables the remote monitoring of elderly and patients. In healthcare systems, reliability of a recognition model is of high importance. Limited amount of training data and imbalanced number of activity instances result in over-fitting thus making recognition models inconsistent. In this paper, we propose an activity recognition approach that integrates the distance minimization (DM) and probability estimation (PE) approaches to improve the reliability of recognitions. DM uses distances of instances from the mean representation of each activity class for label assignment. DM is useful in avoiding decision biasing towards the activity class with majority instances; however, DM can result in over-fitting. PE on the other hand has good generalization abilities. PE measures the probability of correct assignments from the obtained distances, while it requires a large amount of data for training. We apply data oversampling to improve the representation of classes with less number of instances. Support vector machine (SVM) is applied to combine the outputs of both DM and PE, since SVM performs better with imbalanced data and further improves the generalization ability of the approach. The proposed approach is evaluated using five publicly available smart home datasets. The results demonstrate better performance of the proposed approach compared to the state-of-the-art activity recognition approaches. [ABSTRACT FROM AUTHOR]

The stock selection problem is one of the major issues in the investment industry, which is mainly solved by analyzing financial ratios. However, considering the complexity and imprecise patterns of the stock market, obvious and easy-to-understand investment rules, based on fundamental analysis, are difficult to obtain. Therefore, in this paper, we propose a combined soft computing model for tackling the value stock selection problem, which includes dominance-based rough set approach, formal concept analysis, and decision-making trial and evaluation laboratory technique. The objectives of the proposed approach are to (1) obtain easy-to-understand decision rules, (2) identify the core attributes that may distinguish value stocks, (3) explore the cause–effect relationships among the attributes or criteria in the strong decision rules to gain more insights. To examine and illustrate the proposed model, this study used a group of IT stocks in Taiwan as an empirical case. The findings contribute to the in-depth understanding of the value stock selection problem in practice. [ABSTRACT FROM AUTHOR]

A baggage screening system is one of the factors that determine air transport security. It prevents objects and materials that could be used to commit an act of unlawful interference from being placed on board an aircraft. The aim of this paper was to present a model and a computer system supporting the management of a security screening checkpoint's organisation at an airport. This model was to take into account the role of the human factor in this process, in particular of many subjective factors that influence the effectiveness of an airport security system, such as an employee's experience, level of training or attitude to the work he/she does. The studied process involves numerous dependencies which are intuitive and subjective in character and which cannot be unequivocally described. Therefore, a fuzzy model and a fuzzy inference system were created because they are suitable for analysing decision-making processes in the context of uncertainty. The model was parameterized based on expert assessments as well as measurements and experiments that had been carried out at the Katowice-Pyrzowice airport. The model and the computer system that have been developed make it possible to evaluate the effectiveness of airport security screeners in detecting prohibited items in baggage. The experiments made it possible to evaluate individual employees and groups of employees working during the same shift. The tools that had been obtained allowed us to make a recommendation that comprehensive training sessions should be organised every 12 months and ongoing training sessions should be held every 6 months. [ABSTRACT FROM AUTHOR]