Multi-Criteria Decision Making techniques for the management of public procurement tenders: A case study

Tracking the behavior of an accidental oil spill and its impacts on the marine environment in the Eastern Mediterranean

The eastern Mediterranean region is a vital hub for oil transportation and production because of its strategic location between Europe, Asia, and Africa. But its unique attributes, including narrow shipping routes, heavy marine traffic, and proximity to vulnerable ecosystems, render it particularly susceptible to accidental oil spills. In this research, an oil spill detection model, along with bathymetric and oceanographic parameters, was used to track oil spills that occurred at the Syrian Baniyas Station in the Eastern Mediterranean on August 23, 2021. Furthermore, the study employed a pairwise comparison matrix (PWCM) to assess the relative importance of wind speed and direction, water depth, and sea surface temperature (SST) in the dispersion of oil spills. Analysis of Sentinel-1 data obtained prior to, during, and after the incident revealed the accumulation of oil slicks along the Syrian coast from Baniyas to Latakia for up to twenty days. The spilled oil reached the coast of Cyprus six days after the incident. The study determined that wind speed and direction played a critical role in the dispersion of spilled oil, while water depth and SST were comparatively less significant factors in this process. The overall accuracy (OA) and Kappa coefficient (KC) for land, water, and oil slick classes derived from the random forest (RF) algorithm ranged from 90 % to 98 % and from 0.86 to 0.98, respectively. The spread of oil slicks at the incident location was revealed by the decorrelation stretch and band ratios of Sentinel-2 MultiSpectral Instrument (MSI) data. The accidental oil spill could have negative effects on the organic carbon cycle, chlorophyll production, and ecosystem productivity. It is essential to consider the vulnerability of specific regions in the Eastern Mediterranean to oil spills when developing adaptation strategies.

Effective machine learning, Meta-heuristic algorithms and multi-criteria decision making to minimizing human resource turnover

Employee turnover is one of the most important issues in human resource management, which is a combination of soft and hard skills. This makes it difficult for managers to make decisions. In order to make better decisions, this article has been devoted to identifying factors affecting employee turnover using feature selection approaches such as Recursive Feature Elimination algorithm and Mutual Information and Meta-heuristic algorithms such as Gray Wolf Optimizer and Genetic Algorithm. The use of Multi-Criteria Decision-Making techniques is one of the other approaches used to identify the factors affecting the employee turnover in this article. Our expert has used the Best-Worst Method to evaluate each of these variables. In order to check the performance of each of the above methods and to identify the most significant factors on employee turnover, the results are used in some machine learning algorithms to check their accuracy in predicting the employee turnover. These three methods have been implemented on the human resources dataset of a company and the results show that the factors identified by the Mutual Information algorithm can show better results in predicting the employee turnover. Also, the results confirm that managers need a support tool to make decisions because the possibility of making mistakes in their decisions is high. This approach can be used as a decision support tool by managers and help managers and organizations to have a correct insight into the departure of their employees and adopt policies to retain and optimize their employees.

A decision support approach for financial policy measures selection

Purpose

Business research and development (R&D) is of critical importance for innovation and economic growth. The purpose of this study is to present an application of the analytic hierarchy process (AHP) to select the most appropriate policy measure to support the business expenditure on R&D (BERD).

Design/methodology/approach

AHP method adopts a multi-criteria approach that can be used to analyse and prioritize the policy measures based on pairwise comparisons between several attributes that affect the selection of a policy tool. The model formulated in this study is applied to a real case of supporting decision-makers in some high-tech sectors in Iran.

Findings

The results highlight the four main financial policy measures implemented in Iran to enhance the BERD; those are, public procurement for R&D, direct subsidies for R&D, grants for R&D and income tax credit for firms have the priority values of 0.280, 0.260, 0.249 and 0.211, respectively.

Research limitations/implications

The findings of this study are based on subjective evaluation of policy measures by experts of designing policy measures. Objective assessment of policy measures is important too because the preferences of policy interventions change during the time. Another significant point is that the priorities of specific policy measures depend on the effectiveness of their implementing arrangement and the previously successful experience of firms in receiving them.

Originality/value

This paper presents an application of the AHP to select the most appropriate policy measure to support the BERD. This method could be used to prioritize the policies and interventions that governments implement to solve different problems, especially at the innovation system level.

Assessing the role of financial inclusion and financial development to improve energy and environmental efficiency

Environmental concerns are increasingly assuming prominence across the globe. Undesirable output such as carbon dioxide is the end-product of desirable productivity in energy and environmental measurement. Therefore, the current study uses data envelopment analysis to measure the energy and environmental efficiency of developed and developing countries from 2001 to 2020. In order to incentivize environmentally benign generation, undesirable outputs ought to be coupled in the evaluation of the performance of countries. One of the widely acclaimed approaches for evaluating efficiency is the Data Envelopment Analysis (DEA). Within this analysis, the traditional modeling in data envelopment analysis is expanded to incorporate a novel framework for efficiency evaluation amongst undesirable outputs. The novel efficiency estimate is extra robust, plus gives more extraordinary differentiating ability. The findings highlighted that the environmental efficiency was essentially minimal, whiles their spatial attributes increased in the East and decreased in the West, beyond more significant in the South as well as lower in the North. The findings equally show that Iceland obtained a perfect efficiency score of one for energy and environmental efficiency owing greatly to its reliance on geothermal energy which is emission-free energy. The finding is in line with the reality that the twofold data envelopment analysis model compares DMUo with the entire decision-making units in the technology, plus not only with empirical decision-making units.

Prioritizing Post-Disaster Reconstruction Projects Using an Integrated Multi-Criteria Decision-Making Approach: A Case Study

As the destructive impacts of both human-made and natural disasters on societies and built environments are predicted to increase in the future, innovative disaster management strategies to cope with emergency conditions are becoming more crucial. After a disaster, selecting the most critical post-disaster reconstruction projects among available projects is a challenging decision due to resource constraints. There is strong evidence that the success of many post-disaster reconstruction projects is compromised by inappropriate decisions when choosing the most critical projects. Therefore, this study presents an integrated approach based on four multi-criteria decision-making (MCDM) techniques, namely, TOPSIS, ELECTRE III, VIKOR, and PROMETHEE, to aid decision makers in prioritizing post-disaster projects. Furthermore, an aggregation approach (linear assignment) is used to generate the final ranking vector since various methods may provide different outcomes. In the first stage, 21 criteria were determined based on sustainability. To validate the performance of the proposed approach, the obtained results were compared to the results of an artificial neural network (ANN) algorithm, which was applied to predict the projects’ success rates. A case study was used to assess the application of the proposed model. The obtained results show that in the selected case, the most critical criteria in post-disaster project selection are quality, robustness, and customer satisfaction. The findings of this study can contribute to the growing body of knowledge about disaster management strategies and have implications for key stakeholders involved in post-disaster reconstruction projects. Furthermore, this study provides valuable information for national decision makers in countries that have limited experience with disasters and where the destructive consequences of disasters on the built environment are increasing.

Evaluation of criteria and COVID-19 patients for intensive care unit admission in the era of pandemic: A multi-criteria decision making approach

BACKGROUND AND OBJECTIVE

The COVID-19 pandemic results in an intense flow of patients to hospitals especially to the intensive care units (ICUs) to be treated. The ICUs will therefore be confronted with a massive influx of patients (e.g. Spain and Italy). However, if the number of patients is higher than the resources available in ICUs, rationing decisions such as determining and evaluating the criteria for ICU admission becomes essential. In this case, the decision of which patients will be admitted to the ICUs may put significant pressure on healthcare personnel. The goal of this paper is to determine the criteria to be used in the decision of admission of COVID-19 patients to the ICUs.

METHODS

A three-step methodology is applied. In the first step, the evaluation criteria are determined, and then the criteria are prioritized using a fuzzy analytical hierarchy process (AHP) in an uncertain and multiple-criteria environment choice. Finally, COVID-19 patients are ranked using the Multi-Objective Optimization Method by Ratio Analysis to find out which patient is more urgent.

RESULTS

According to experts' evaluation of ICU admission criteria, "increment of >2 in SOFA score" seems the most dominant factor among others. The proposed methodology is tested on 10 anonymous COVID-19 positive patients being treated in a public hospital and the ICU admission results are discussed.

CONCLUSIONS

Obtained priorities and ranking is in line with the hospitals' behavior that potentially depicts the usefulness and validity of the proposed approach.

Implementing a multi-criteria decision-making approach to a new party's election campaign - A case study

During pre-election campaigns, parties make great efforts to persuade constituents to vote for them. Usually, new parties have smaller budgets and fewer resources than veteran parties. In particular, the more heterogeneous the party's electorate, the more critical the issue of resource allocation is. This paper presents a method to allocate new party's campaign advertising resources efficiently to maximize its voters. The model developed uses Pareto principle and multi-criteria approach and integrates party's confidential data together with an official open-to-all data. The model produced clear and unbiased results, and these advantages made it effective and user-friendly for the strategic team and campaign managers. We implemented the model on a specific new party during the intensive political period before the April 2019 elections in Israel.•This paper analyses the issue of allocating new party's campaign advertising resources efficiently to maximize its voters.•Our model integrates Pareto principle with multi-criteria decision-making approach and uses the party's confidential data together with official open to all state data.•The model produced clear and unbiased results, and these advantages made it effective and user-friendly for the strategic team and campaign managers.

Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method

The COVID-19 outbreak, which emerged in China and continues to spread rapidly all over the world, has brought with it increasing numbers of cases and deaths. Governments have suffered serious damage and losses not only in the field of health but also in many other fields. This has directed governments to adopt and implement various strategies in their communities. However, only a few countries succeed partially from the strategies implemented while other countries have failed. In this context, it is necessary to identify the most important strategy that should be implemented by governments. A decision problem based on the decisions of many experts, with some contradictory and multiple criteria, should be taken into account in order to evaluate the multiple strategies implemented by various governments. In this study, this decision process is considered as a multi-criteria decision making (MCDM) problem that also takes into account uncertainty. For this purpose, q-rung orthopair fuzzy sets (q-ROFSs) are used to allow decision-makers to their assessments in a wider space and to better deal with ambiguous information. Accordingly, two different Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are recommended under the q-ROFS environment and applied to determine the most appropriate strategy. The results of the proposed approaches determine the A1 — Mandatory quarantine and strict isolation strategy as the best strategy. Comparisons with other q-rung orthopair fuzzy MCDM methods and intuitionistic fuzzy TOPSIS method are also presented for the validation of the proposed methods. Besides, sensitivity analyses are conducted to check the robustness of the proposed approaches and to observe the effect of the change in the q parameter.

Examining the Relationship between the Economic Performance of Technology-Based Small Suppliers and Socially Sustainable Procurement

Sustainable public procurement plays an important role in addressing not only environmental but also economic and social issues through government acquisitions from technology-based small suppliers. In this context, the objective of this study is to better understand the holistic public procurement process by assessing the operational efficiency of technology-based small suppliers and associating the economic aspect of public procurement with the social aspect, such as women-owned businesses. To this end, we analyzed U.S. Department of Defense Small Business Innovation Research grantees by combining network data envelopment analysis with bootstrap truncated regression analysis. Drawing on the analysis results, we found that (1) there is heterogeneity in the performance of research and development, network building, and commercialization sub-processes, and (2) there is a positive relationship between the overall performance and women-owned small suppliers who excel particularly in network building. The former implies that small suppliers may have different expertise in the chain of public procurement; the latter suggests that woman entrepreneurs with a business network may be able to outperform their counterparts in the public procurement market.

A fuzzy approach to assess outsourcing risks in Brazilian navy industrial military organizations

Purpose

This study assesses outsourcing risks using the fuzzy analytical hierarchy process (FAHP).

Design/methodology/approach

This descriptive research combines both qualitative and quantitative approaches. Risks identified in the literature review were classified with FAHP using questionnaire data from respondents in operations, procurement and risk management in Brazilian Navy Industrial Military Organizations (OMPS-I, by its Portuguese acronym).

Findings

The results indicate that FAHP is a method capable of producing relevant information to decision-making in the risk management process. A framework was created incorporating 16 major risks related to outsourcing. The results point to higher inherent risk levels related to outsourcing in the context of OMPS-Is: in order, hidden costs and unrealized savings; loss of knowledge/skills and/or corporate memory and difficulty in reacquiring a function; and loss of opportunities and reputation. The category of economic risk was revealed as the most important.

Originality/value

This study improves understanding of outsourcing risks and improves risk assessment by refining decision-making information and developing a system of decision analysis with several criteria. It also contributes to the development and implementation of a usable version of decision analysis with several criteria at a managerial level.

Multi-Period Multi-Criteria Decision Making under Uncertainty: A Renewable Energy Transition Case from Germany

Methods of multi-criteria decision making (MCDM) from operations research have been applied to provide information for making long-term decisions in the energy sector, and energy policy. For example, in sustainability evaluations, multiple conflicting criteria can be considered. While most MCDM approaches have been applied to evaluate energy systems in a single period, the multi-criteria evaluation of energy system evolution over time has received less attention. To evaluate such transition paths, multi-period MCDM approaches can be used. Because of long-term planning horizons, deep uncertainties need to be considered. Based on prior multi-period MCDM approaches, this paper provides an extension of the outranking approach preference ranking and organization method for enrichment evaluations (PROMETHEE) for multi-period evaluations in deep uncertainty settings. In order to adequately address the consideration of uncertainties and to obtain an additional level of information, a multi-period PROMETHEE approach and scenario planning are combined. In an illustrative example, this method is applied to a case study from the German energy sector regarding a renewable energy transition. This highlights the potential interactions of a multi-period perspective and the consideration of external scenarios in the decision-making process.

Reducing the quality risk of elderly care services in government procurement from market-oriented private providers through ex ante policy design: lessons from the principal-agent theory analysis

Background

Government procurement of elderly care services from market-oriented private providers has become an important way to respond to the growing demands of elderly care. However, the government cannot accurately identify the actual quality efforts of these providers, and the government pursues social benefits while the providers pursue economic interests. The existence of asymmetric information and goal divergence increases the quality risk of services. From the perspective of maximizing the government’s net benefits, this study aimed to analyze how to reduce the quality risk through ex ante policy design.

Methods

On the basis of the principal-agent theory, this study defined the asymmetric information of market-oriented private providers’ efforts on quality as a random variable, and constructed the theoretical model in the case of asymmetric information to compare with the one in the reference case of complete information, in both of which the government is the principal and market-oriented private providers are the agents. And the models also introduced several parameters to describe key factors that affect the contract results, including the physical health of the elderly, the spillover benefits to the government and market-oriented private providers, and the market risks.

Results

The optimal results of the models in the two cases were obtained respectively, and the validity of the theoretical models was verified in a numerical example. Taking the case of complete information as the basic frame of reference, the difference of the optimal results in both cases showed the extent of negative impacts of asymmetric information, and highlighted the role of ex ante policy design in minimizing asymmetric information and reducing its negative impacts. Some ex ante policies that can improve the supervision of market-oriented private providers and their quality efforts, as well as have positive effects on key factors, were also recommended.

Conclusions

The government should attach importance to ex ante policy design to reduce the quality risk of elderly care services supplied by market-oriented private providers in government procurement. Our study provides main framework and critical directions for ex ante policy design, which is conducive to the realization of real and sustained quality improvement.

Interrelations among Leadership Competencies of BIM Leaders: A Fuzzy DEMATEL-ANP Approach

The use of new, digitally enabled innovations, such as building information modeling (BIM), raises issues such as the delineation of a competent leader. Even though BIM-based competency assessment models have become essential tools for maximizing the potential values of BIM implementation, the current competency models provide limited focus on leadership aspects that facilitate and enhance the BIM implementation efforts. This paper seeks to identify the specific competencies required for BIM implementation and examines the relationships between these competencies. Thirty-two experts from around the globe investigated a total of 15 leadership competencies under three categories pertaining to intellectual, managerial, and emotional leadership. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) was implemented to examine the cause-and-effect relationships among the BIM leadership competencies and fuzzy analytic network process (ANP) was performed to weigh those competencies. Findings show that the intellectual competencies act as the cause group, while managerial and emotional competencies are the effect groups. Moreover, the involving leadership is found to be the more suitable leadership style for BIM professionals, given the current capability and maturity levels of BIM implementation, in order to deal with the required changes throughout the BIM implementation process. This study contributes to the existing body of knowledge in the BIM domain to examine the associated leadership competencies by using the multi-criteria decision-making (MCDM) technique. The results of this research show the relative importance of criteria and sub-criteria, which contributes to further improvement of BIM leadership.