Improvement Decisions Research Articles

Multiple attribute group decision making based on quasirung orthopair fuzzy sets: Application to electric vehicle charging station site selection problem

This paper aims to greatly improve decision makers’ capability of capturing their judgment in a broader space. In order to achieve this, we introduce the notion of a p,q-quasirung orthopair fuzzy set (p,q-QOFS), which is an extension of the q-rung orthopair fuzzy set. In p,q-QOFS, the sum of the pth power of membership degree and qth power of nonmembership degree is less than or equal to 1, where p and q are natural numbers. Thus, due to the additional parameter p, the p,q-QOFS can express incomplete information more flexibly and elaborately. This paper first develops the concept of p,q-QOFS and establishes that it is an extension of several existing fuzzy sets. Then, we introduce the score and accuracy functions of p,q-QOFS and analyze a few mathematical properties. Next, we define the Hamming distance measure between two p,q-QOFSs and some important properties. After that, we investigate the basic operations of p,q-QOFSs and extend these operational laws to aggregation operators. Further, we introduce the weighted averaging and geometric aggregation operators to aggregate p,q-quasirung orthopair fuzzy data. Moreover, we establish a p,q-quasirung orthopair fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for solving multi-attribute group decision-making problems with unknown attribute weights. The present study also discusses a case study illustrating the applicability of the method through the selection of the most appropriate site for an electric vehicle charging station in an Indian city. In this case study, we consider seven alternative sites, including Raniganj, Jamuria, Kulti, and Burnpur. As a result of this study, Jamuria appears to be the best location to build an electric vehicle charging station. Finally, we illustrate the validity and practicability of our developed method through a comparative analysis with existing methods.

Techno-economic feasibility analysis of a eucalyptus-based power plant using woodchips

Forest biomass plays a critical role in developing countries, with high potential to contribute to the energy needs as a cheaper and carbon-friendly alternative. In Brazil, eucalyptus plantations have gained attention in recent years as a promising source for the power sector. Aiming to assess this potential, this work analyzed the techno-economic feasibility of a eucalyptus woodchip-based power plant. A typical 10 MW power plant was modeled using empirical parameters based on the Steam Rankine Cycle. Woodchip samples were collected and energy properties, fuel demand, eucalyptus area required and unit variable cost were determined. In economic analysis, NEWAVE (Long and Medium Term Interconnected Hydrothermal Systems Operation Planning Model) and ANAFIN (Financial Analysis Model for Electrical Projects), both developed by CEPEL (Brazilian Electrical Research Center) were used for the first time to analyze forest biomass projects taking into account fuel availability and hydrological risks. Three energy trading scenarios were evaluated: (1) full commercialization in Brazilian regulated market, (2) full in free market and (3) partial in both markets. Results showed that 4,200 ha of eucalyptus plantations would be needed to ensure sustainable woodchip supply. Moisture content, basic density and woodchip price played an essential role in determining future costs. All scenarios of energy trading were economic feasible, and scenario (3) mitigated energy spot price risk and might be considered the effective risk-return strategy. The simulated strategies may improve decision makers visibility over the risk-return relation of a eucalyptus power plant. This methodology may be used to evaluate new forest biomass energy projects in potential areas. This study will help encourage bioenergy and power diversification in developing countries.

Sensegiving in organizations via the use of business analytics

PurposeBusiness analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.Design/methodology/approachThis study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.FindingsThe thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.Research limitations/implicationsThis study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).Practical implicationsThis study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.Originality/valueThis study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

Assessing the Value Extension Adds to Decision- Making Among Natural Resources Leaders

Extension professionals face challenges in quantifying the impact of their efforts in advancing the decision-making process inherent in setting natural resources policy. We developed a flexible tool that measures the impact of improving decision makers’ planning efforts. The tool consists of two sets of survey questions that can be modified to fit an Extension program’s goals. We illustrated how to use the tool by surveying leaders working with private forest owners to advance natural resources management. However, the tool can be of use to Extension professionals across various program areas.

WITHDRAWN: Analyzing Human Resource Practices For Decision Making in Banking Sector using HR analytics

The aim of the present study is to improve decision makers’ ability to make informed decisions around human capital with HR practices. The HR practices which have been targeted are – recruitment, training, performance management and retention. The organizations which have been identified for responses are public and private banks. The study focuses on synthesis of literature conducted with an analytical approach in identifying predictors from HR practices for improved decision making. The paper explores the implication of analytics within organization in identified human resource functions. The data collection was conducted in form of self-structured questionnaire from the Branch Heads and Managers and their responses were analysed to find out the role of HR Analytics in Banking sector and how performance can improve using HR analytics. Respondent banks were chosen on basis of their Net profit from last three years (2018–20) in public and private sector. The role of HR Analytics in Banking sector will be analysed using certain statistical tools on data collected. The results can be used by banking organizations in order to discover answers to questions relating identified human resource practices that will enhance managerial decision making.

Cost-Benefit Analysis for Supporting Intermunicipal Decisions on Drinking Water Supply

Several countries promote a regionalization of the drinking water sector; however, few decision support tools are adapted to the intermunicipal level to aid in regional decisions. The aim of this paper is to describe and demonstrate a probabilistic cost-benefit analysis approach to assess the societal effects of regional water supply interventions to constitute support for decision makers. A special focus is given to the quantification of effects on consumers’ health, water supply reliability, and operation and maintenance costs. The uncertainties of the quantified values are represented by probability distribution functions and analyzed by means of Monte Carlo simulations. The proposed approach was demonstrated in the Goteborg region in Sweden, for which five alternative interventions were evaluated. In conclusion, the proposed approach facilitates the identification and prioritization of societal effects so that costs and benefits normally overlooked in evaluation processes can be explicitly considered and addressed. The paper provides a transparent handling of uncertainties and enables a structured approach to improve decision makers’ ability in making informed choices on regional water supply alternatives.

Quantitative target setting in balanced scorecard method using simultaneous equations system and goal programming

Purpose The purpose of this paper is to identify the accurate cause and effect relationships among strategic objectives and also to demonstrate how decision makers can be guided in the process of defining quantitative strategic target values in the framework of balanced scorecard (BSC) and performance measurement system. Design/methodology/approach Based on the proposed method in this research, after determining strategic objectives and developing an initial strategy map according to decision makers’ opinions, simultaneous equations system (SES) was used to determine the significance of the relationships among strategic objectives in higher perspectives of the BSC and corresponding strategic objectives in lower perspectives. Afterward, desirable values for performance measures were determined based on the equations and relationships obtained through SES and were optimized by goal programming method. Findings By applying the proposed method, a clearer picture of the associations among strategic objectives is obtained and the influence of strategic objectives on one another is determined. Afterward, optimal values for strategic objectives are determined to achieve the organization’s goals. Research limitations/implications This paper proposes a framework for constructing a strategy map and setting quantitative targets in the framework of BSC. Indeed, this paper presents a case study to demonstrate the applicability and effectiveness of the proposed approach. However, SES technique requires a greater amount of data to generate more accurate results. Although the advent of the Information Age has forced organizations’ decision makers to provide sufficient information and data for business analysis, the data requirements are met. Practical implications The presented quantitative approach is a supporting approach for improving decision makers’ opinions and enabling them to reach a more accurate picture of the relationships, valuing strategic objectives and achieving strategic goals. This research also presents a case study to demonstrate the applicability of the proposed approach. The application and implication of the proposed method in banking services show that the contributions of the paper are not only theoretical, but also practical. Originality/value The proposed method provides a novel approach for determining the most appropriate targets and applies a comprehensive and scientific model together with decision makers’ opinions and experiences and has two main contributions: first, the associations among strategic objectives are investigated and obtained in an effective way by conducting the SES for the first time in the framework of BSC. Second, quantitative targets have been determined to help in achieving the long-term goals. This task has been accomplished through a combination of SES, the three-stage least squares regression analysis and optimization by using weighted goal programming method.

A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method

Identifying the flood susceptible areas is a vital and substantial element of disaster management for every country to control and mitigate injuries of the natural hazards. The current research presents a framework for the preparation of flood prone areas' maps by the integration of Geospatial Information System (GIS), fuzzy logic, and Multi-Criteria Decision Making (MCDM). To achieve this goal, a spectrum of geophysical, geomorphological, meteorological, hydrological, and geographical criteria have been addressed. Considering the linkage and the interdependencies of the criteria, DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) approach are used to form the network of relations among the criteria. Moreover, considering inevitable uncertainty, ambiguity/vagueness of the experts' opinions, fuzzy theory is integrated with DEMATEL to handle the issue. Finally, Analytic Network Process (ANP) are implemented to calculate the final weight of every single criterion. The Kurdistan province, located in the North-West of Iran, is considered as the case study in which numerous flood events has had recently happened. The efficiency of the model is assessed through the area under the curve (AUC) and statistical measures such as the Kappa index. In order to evaluate the produced classified flood susceptibility map, the map of historical flood events in the province is also used. About 85% of validation area is classified as “Very High flood Susceptible” which implies the efficiency of proposed framework for flood susceptibility mapping. Furthermore, for evaluating the functionality of the framework in comparison with traditional approaches, the well-known AHP methodology is implemented too. The validation results demonstrate that the Fuzzy-DEMATEL ANP model (AUC-ROC = 0.938, Kappa = 0.88) has a higher performance accuracy compare to the AHP model (AUC-ROC = 0.918, Kappa = 0.79). Moreover, comparing the proposed and validation model shows that the proposed framework can effectively improve decision makers to provide flood susceptibility maps, and recognize flood susceptible areas in data-scarce and ungauged regions.

Keeping an Eye on the Competition Observational Learning in Analysts’ Forecasts

Substantial research shows the importance of different network structures in the process of learning from others. Learning in most of these networks takes place when knowledge is transferred via social or business ties. I argue that network structure matters differently for observational learning, defined as an arm’s length process of observing and incorporating information from others who are unable or unwilling to transfer knowledge. I argue that, even in the absence of sociality and collaboration, closed networks improve decision makers’ performance by facilitating observational learning. Closed networks improve performance by focusing decision makers’ attention on relevant sources of information and providing context that is useful for interpreting what is observed. I find that security analysts’ earnings forecasts are more accurate when their stock coverage network is closed. This effect is robust only for analysts who reacted to the opinions their competitors published, which supports observational learning as the underlying mechanism. Learning from competitors’ forecasts is further supported using a natural experiment. I find that the sudden absence of analysts who perished in the September 11, 2001 attacks on the World Trade Center reduced survivors’ forecasting accuracy as a function of the damages to their coverage network.

Investigating causal linkages and strategic mapping in the balanced scorecard: A case study approach in the banking industry sector

One of the main challenges of strategic management is implementing the strategies. Designing the strategy map in Balanced Scorecard framework to determine the causality between strategic objectives is one of the most important issues in implementing the strategies. In designing the strategy map with intuition and judgment, the link between strategic objectives is not clear and it is not obvious which strategic objectives are related and influenced each other. Hence, it is essential to offer a quantitative and accurate method to design the strategy map and clarify these relationships. In this paper, after reviewing the methods for determining the causal relationships among BSC perspectives in the literature, a framework on the basis of historical data analysis and multi-response surface regression analysis is offered to determine causal relationships among strategic objectives with respect to data of key performance measures of past years in order to obtain the coefficients and equations that can be used in the prediction of the responses. Using statistically significant models, the correlations between the factors and several responses were acquired. The presented quantitative approach is useful for determining the causal relationships resulting in an accurate strategy map and is a supporting approach for improving decision makers’ opinions and enabling them to reach a more accurate picture of the relationships. This research also presents a case study to demonstrate the applicability of the proposed approach. The application and implication of the proposed method in a real case show that the contributions of the research are not only theoretical but practical as well. The strategy map constructed in this study can also serve as a reference point for similar businesses.

D-Intuitionistic Hesitant Fuzzy Sets and their Application in Multiple Attribute Decision Making

Hesitant fuzzy sets (HFSs) and generalized hesitant fuzzy sets (GHFSs) provide useful tools for uncertain information processing in situations in which decision makers have doubts among several possible membership degrees. In practice, however, decision makers may have a degree of belief for hesitant memberships based on their knowledge and experience. The aim of our study is to propose a new manifestation of uncertain information, called D-intuitionistic hesitant fuzzy sets (D-IHFSs), by combining D numbers and GHFSs. First, arithmetic operations, score functions, and comparison laws related to D-IHFSs are introduced. Next, an extension principle is proposed for the application of aggregation operators of GHFSs to the D-intuitionistic hesitant fuzzy environment. Finally, a decision-making approach based on D-IHFSs is developed. An illustrative example shows the effectiveness and flexibility of D-IHFSs to handle uncertainties, such as fuzziness, hesitation, and incompleteness. D-IHFSs, combining D numbers and GHFSs, improve decision makers’ ability to handle uncertain information.

Exploring Institutional Mechanisms for Scientific Input into the Management Cycle of the National Protected Area Network of Peru: Gaps and Opportunities.

Understanding how to improve decision makers' use of scientific information across their different scales of management is a core challenge for narrowing the gap between science and conservation practice. Here, we present a study conducted in collaboration with decision makers that aims to explore the functionality of the mechanisms for scientific input within the institutional setting of the National Protected Area Network of Peru. First, we analyzed institutional mechanisms to assess the scientific information recorded by decision makers. Second, we developed two workshops involving scientists, decision makers and social actors to identify barriers to evidence-based conservation practice. Third, we administered 482 questionnaires to stakeholders to explore social perceptions of the role of science and the willingness to collaborate in the governance of protected areas. The results revealed that (1) the institutional mechanisms did not effectively promote the compilation and application of scientific knowledge for conservation practice; (2) six important barriers hindered scientific input in management decisions; and (3) stakeholders showed positive perceptions about the involvement of scientists in protected areas and expressed their willingness to collaborate in conservation practice. This collaborative research helped to (1) identify gaps and opportunities that should be addressed for increasing the effectiveness of the institutional mechanisms and (2) support institutional changes integrating science-based strategies for strengthening scientific input in decision-making. These insights provide a useful contextual orientation for scholars and decision makers interested in conducting empirical research to connect scientific inputs with operational aspects of the management cycle in other institutional settings around the world.

Urban freight transport and policy changes: Improving decision makers' awareness via an agent-specific approach

This paper derives policy implications from agent-specific data with respect to the implementation of policy changes in the case of urban freight transport. In particular, the research, based on the case of Rome's Limited Traffic Zone, discusses alternative policy scenarios. After describing attribute definition and selection, questionnaire administration, data collection and treatment, willingness to pay estimates are calculated.The paper tests, from a policy-maker's perspective, the implications deriving from the presence of inter-agent heterogeneity and the specific policy composition of an improving and equally impacting interventions on all agent-types' utility. The paper shows how an agent-specific approach might increase decision makers' awareness and help taking better decisions.

How to handle uncertainties in AHP: The Cloud Delphi hierarchical analysis

In practice, many practical problems occur in uncertain environments, especially in situations that involve human subjective evaluation such as that in the analytic hierarchy process (AHP). This paper presents a practical multi-criteria group decision-making method for decision making under uncertainty. To handle the randomness and fuzziness of individual judgments, the normal Cloud model, group decision-making technique, and the Delphi feedback method are adopted. In the proposed Cloud Delphi hierarchical analysis (CDHA), experts are asked to express their judgments using interval numbers. Individual fuzziness and randomness are then mined from the interval-value comparison matrices. Subsequently, the interval-value pairwise comparison matrices are converted into the corresponding Cloud matrices, and the one-iteration Delphi process is executed to diminish individual judgment mistakes. The individual Cloud weight vectors are calculated using the geometric mean technique and are finally weighted to form the group Cloud weight vector. A simple case study that involved reproducing the relative area sizes of six provinces in China shows that the CDHA method can effectively reduce mistakes and improve decision makers’ judgments in situations that require subjective expertise and judgmental inputs. In addition, a practical decision-making problem in which houses are ranked by home buyers shows that the proposed method is effective when applied to complex, large, multidisciplinary problems with considerable uncertainties.

An interval-parameter mixed integer multi-objective programming for environment-oriented evacuation management

Large crowds are increasingly common at political, social, economic, cultural and sports events in urban areas. This has led to attention on the management of evacuations under such situations. In this study, we optimise an approximation method for vehicle allocation and route planning in case of an evacuation. This method, based on an interval-parameter multi-objective optimisation model, has potential for use in a flexible decision support system for evacuation management. The modeling solutions are obtained by sequentially solving two sub-models corresponding to lower- and upper-bounds for the desired objective function value. The interval solutions are feasible and stable in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving decision makers’ estimates under different conditions. The resulting model can be used for a systematic analysis of the complex relationships among evacuation time, cost and environmental considerations. The results of a case study used to validate the proposed model show that the model does generate useful solutions for planning evacuation management and practices. Furthermore, these results are useful for evacuation planners, not only in making vehicle allocation decisions but also for providing insight into the tradeoffs among evacuation time, environmental considerations and economic objectives.

The use of qualitative system dynamics to identify sustainability characteristics of decentralized wastewater management alternatives

In order to pursue more sustainable alternatives in wastewater management, it is vital that we understand how a given infrastructure alternative will impact the various aspects of sustainability. A set of qualitative tools (force field diagrams and causal loop diagrams (CLDs)) for the assessment of wastewater management alternatives is proposed and demonstrated in the context of a decentralized wastewater infrastructure upgrade. The objective for the application of these tools is to improve decision makers' understanding of how a given alternative will impact the economic, environmental/ecological, social, and functional aspects of sustainability. In the proposed method, each aspect of sustainability is treated as a stock, and its movement (up or down) can be inferred using both qualitative and quantitative data. By incorporating these tools into a participatory planning process, project-specific CLDs can be developed and loops of interest can be identified to help elucidate stakeholder values. The ultimate goal of this methodology is to facilitate the pursuit of sustainability in wastewater management by allowing decision makers to address specific sustainability challenges without creating new ones.

Alternative Conceptions of Feedback in Dynamic Decision Environments: An Experimental Investigation

Studies conducted in recent years have shown that outcome feedback in dynamic decision-making tasks does not lead to improved performance. This has led researchers to examine alternatives to outcome feedback for improving decision makers' performance in such tasks. This study examines the feasibility of improving performance in dynamic tasks by providing cognitive feedback or feedforward. We report a laboratory experiment in which subjects managed a set of simulated software development projects. Results indicate that subjects provided with cognitive feedback performed best, followed by those provided with feedforward. Subjects provided with outcome feedback performed poorly. We discuss the implications of the results for decision support in dynamic tasks.

Decision Making during International Crises

This study investigated the hypothesis that high-quality decision-making procedures during crises are associated with better crisis outcomes than are defective decision-making procedures. Presidential decision making during 19 international crises since World War II was examined for seven symptoms of defective decision making proposed by Janis and Mann (1977). Crisis outcomes were rated by outside experts in terms of their effect on U.S. vital interests and on international conflict. Results indicated that crisis outcomes tended to have more adverse effects on U.S. interests and were more likely to increase international conflict when the decision-making process was characterized by a large number of symptoms. Alternative explanations are considered and the implications of these results for improving decision makers' procedures are discussed.