The framework of data-driven and multi-criteria decision-making for detecting unbalanced bidding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huimin Li ◽  
Limin Su ◽  
Jian Zuo ◽  
Xiaowei An ◽  
Guanghua Dong ◽  
...  
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Public Procurement ◽  
Unit Price ◽  
Data Driven ◽  
Relative Distance ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Content Type ◽  
The Government

PurposeUnbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.Design/methodology/approachThe identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.FindingsThe proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.Originality/valueThe data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

scholarly journals Modelling Uncertainties in Multi-Criteria Decision Making using Distance Measure and TOPSIS for Hesitant Fuzzy Sets

2017 ◽  
Vol 7 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Ismat Beg ◽  
Tabasam Rashid
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Fuzzy Data ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Hesitant Fuzzy Sets ◽  
New Approach ◽  
Order Preference ◽  
Modelling Uncertainties

Abstract A notion for distance between hesitant fuzzy data is given. Using this new distance notion, we propose the technique for order preference by similarity to ideal solution for hesitant fuzzy sets and a new approach in modelling uncertainties. An illustrative example is constructed to show the feasibility and practicality of the new method.

Applying the Multi-Criteria Decision Making Model for Ranking Commercial Banks: The Case of Vietnam

2019 ◽  
pp. 125-133
Author(s):  
Duong Truong Thi Thuy ◽  
Anh Pham Thi Hoang
Keyword(s):  
Decision Making ◽  
Commercial Banks ◽  
Decision Model ◽  
Business Models ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Decision Making Model

Banking has always played an important role in the economy because of its effects on individuals as well as on the economy. In the process of renovation and modernization of the country, the system of commercial banks has changed dramatically. Business models and services have become more diversified. Therefore, the performance of commercial banks is always attracting the attention of managers, supervisors, banks and customers. Bank ranking can be viewed as a multi-criteria decision model. This article uses the technique for order of preference by similarity to ideal solution (TOPSIS) method to rank some commercial banks in Vietnam.

Optimum criterion for lightweight nonlinear confusion component with multi-criteria decision making

2021 ◽  
pp. 1-12
Author(s):  
Nabilah Abughazalah ◽  
Majid Khan ◽  
Noor Munir ◽  
Amna Zafar
Keyword(s):  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Mathematical Structures ◽  
Substitution Boxes

In this article, we have designed a new scheme for the construction of the nonlinear confusion component. Our mechanism uses the notion of a semigroup, Inverse LA-semigroup, and various other loops. With the help of these mathematical structures, we can easily build our confusion component namely substitution boxes (S-boxes) without having specialized structures. We authenticate our proposed methodology by incorporating the available cryptographic benchmarks. Moreover, we have utilized the technique for order of preference by similarity to ideal solution (TOPSIS) to select the best nonlinear confusion component. With the aid of this multi-criteria decision-making (MCDM), one can easily select the best possible confusion component while selecting among various available nonlinear confusion components.

The use and non-use of the government financial report by Maltese Members of Parliament

2018 ◽  
Vol 31 (4) ◽  
pp. 1124-1144 ◽  
Author(s):  
Josette Caruana ◽  
Brady Farrugia
Keyword(s):  
Decision Making ◽  
Information Overload ◽  
Qualitative Interviews ◽  
Financial Reports ◽  
Content Type ◽  
Financial Report ◽  
Members Of Parliament ◽  
The Government ◽  
And Performance ◽  
Questionnaire Research

Purpose The purpose of this paper is to examine the use and non-use of the Government Financial Report by Maltese Members of Parliament (MPs). It refers to information overload theory to analyse the gap between financial reports and their relevance for decision making. Design/methodology/approach A mix of qualitative (interviews) and quantitative (questionnaire) research tools are applied, with the Maltese MPs being the research participants. This method is acclaimed to be comprehensive, but this study highlights certain disadvantages when applied in the political arena. Findings The characteristics of the information itself could be the main cause of information overload, resulting in the non-use of the financial report for decision making. Politicians refer to financial data for their decision making, but not to the data presented in the financial report. Irrespective of the politician’s professional background, the data in the financial report is perceived as incomplete and outdated. Practical implications The cause of information overload and its effects are important considerations for preparers of financial information and accounting standard setters, if they wish that their production is relevant for decision makers. Originality/value There is an increase in research concerning politicians’ use of budgetary and performance information, at local and regional levels of government. This study investigates exclusively the use of the financial report by politicians at central level, in a politically stable environment.

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

Grey assessment and prediction of the financial agglomeration degree in central five cities

2014 ◽  
Vol 4 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Li Li ◽  
Guo-hui Hu
Keyword(s):  
Decision Making ◽  
Scientific Development ◽  
Fuzzy Decision Making ◽  
Grey Prediction ◽  
Fuzzy Decision ◽  
Content Type ◽  
Multiple Dimension ◽  
The Government ◽  
Development Tendency ◽  
Decision Making Model

Purpose – At present, financial agglomeration tendency in domestic and foreign countries is increasingly evident. Therefore, from a comparative perspective, this paper aims to assess and predict the financial agglomeration degree in central five cities. Design/methodology/approach – According to the diversity of evaluating indexes and the uncertainty of financial agglomeration, this paper constructs a set of indexes of evaluating the financial agglomeration degree, comprehensively evaluates the financial agglomeration degree of the five cities – Wuhan, Changsha, Zhengzhou, Nanchang and Hefei – in China's middle region from 2001 to 2010 by using the multiple dimension grey fuzzy decision-making model, and predicts their development tendency by using the GM (1, 1, β) model. Findings – The results show that the multiple dimension grey fuzzy decision-making pattern cannot only be used to determine the weights of evaluating indexes, but also get the fuzzy partition and ranking order of the financial agglomeration in central five cities. The grey prediction results can objectively reflect the development tendency of the financial agglomeration in central five cities. Practical implications – From the results, it is necessary for any competitive city to clarify their relative strengths and weaknesses in order for the accurate location and scientific development, and it also provides a reference for the government decision-making. Originality/value – The paper succeeds in using the multiple dimension grey fuzzy decision-making model to measure the financial agglomeration degree of the five central cities and the grey prediction model to predict future trends.

Selection of resilient suppliers in manufacturing industries post-COVID-19: implications for economic and social sustainability in emerging economies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijit Majumdar ◽  
Jeevaraj S ◽  
Mathiyazhagan Kaliyan ◽  
Rohit Agrawal
Keyword(s):  
Decision Making ◽  
Incomplete Information ◽  
Emerging Economies ◽  
Supplier Selection ◽  
Fuzzy Decision Making ◽  
Ideal Solution ◽  
Fuzzy Decision ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Selection Of

PurposeSelection of resilient suppliers has attracted the attention of researchers in the past one decade. The devastating effect of COVID-19 in emerging economies has provided great impetus to the selection of resilient suppliers. Under volatile and uncertain business scenarios, supplier selection is often done under imprecise and incomplete information, making the traditional decision-making methods ineffective. The purpose of this paper is to demonstrate the application of a fuzzy decision-making method for resilient supplier selection.Design/methodology/approachA group of three decision makers was considered for evaluating various alternatives (suppliers) based on their performance under different primary, sustainability and resilience criteria. Experts' opinion about each criterion and alternative was captured in linguistic terms and was modelled using fuzzy numbers. Then, an algorithm for solving resilient supplier selection problem based on the trapezoidal intuitionistic fuzzy technique for order preference by similarity to ideal solution (TrIFTOPSIS) was introduced and demonstrated through a case study.FindingsA closeness coefficient was used to rank the suppliers based on their distances from intuitionistic fuzzy positive-ideal solution and intuitionistic fuzzy negative-ideal solution. Finally, the proposed fuzzy decision making model was applied to a real problem of supplier selection in the clothing industry.Originality/valueThe presented TrIFTOPSIS model provides an effective route to prioritise and select resilient suppliers under imprecise and incomplete information. This is the first application of intuitionistic fuzzy multi-criteria decision-making for resilient supplier selection.

Happiness at work: a multi-criteria decision-making approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rinki Dahiya ◽  
Juhi Raghuvanshi
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Transformational Leadership ◽  
Design Methodology ◽  
Causal Relation ◽  
Multi Criteria Decision Making ◽  
Human Resource Managers ◽  
Content Type ◽  
Resource Managers ◽  
Causal Framework

Purpose Researchers have strived to identify the factors enhancing happiness at work (HAW), and the causal relations among the enablers of happiness remained underexplored. Therefore, this study aims to map and prioritize the causal relation structures of enablers of HAW. Design/methodology/approach Data were collected from key representatives of information technology (IT) firms located in India. A framework based on the cause and effect relationship among enablers of HAW is proposed, and to establish this causality, the decision-making trial and evaluation laboratory (DEMATEL) technique was applied. Findings The findings indicate five out of 12 enablers as causal, namely, transformational leadership, authentizotic work climate, person–organization work fit, organizational virtuousness and meaningfulness in work. Originality/value Human resource managers, organizational policymakers and scholars will gain greater understanding through this causal framework of enablers of HAW. Knowledge and facilitation of these enablers will aid in nurturing a happy workplace.

Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Henry Lau ◽  
Yung Po Tsang ◽  
Dilupa Nakandala ◽  
Carman K.M. Lee
Keyword(s):  
Decision Making ◽  
Evaluation System ◽  
Small And Medium Enterprises ◽  
Handling Time ◽  
Cold Chain ◽  
Pairwise Comparisons ◽  
Temperature Sensitive ◽  
Multi Criteria Decision Making ◽  
Firm Level ◽  
Content Type

PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

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