Research

20 pages, 2446 KiB

Open AccessArticle

Selecting the Fintech Strategy for Supply Chain Finance: A Hybrid Decision Approach for Banks

by Yu-Cheng Kao, Kao-Yi Shen, San-Ting Lee and Joseph C. P. Shieh

Mathematics 2022, 10(14), 2393; https://doi.org/10.3390/math10142393 - 07 Jul 2022

Cited by 4 | Viewed by 1894

Many banks are eager to adopt technology solutions to enhance operational efficiency in managing supply chain finance, which involves various participants and complex financial activities. Previous research either focuses on the technology aspect or the optimization of a supply chain; there is little [...] Read more.

Many banks are eager to adopt technology solutions to enhance operational efficiency in managing supply chain finance, which involves various participants and complex financial activities. Previous research either focuses on the technology aspect or the optimization of a supply chain; there is little specific guidance on how banks can form a holistic model to evaluate their Fintech strategy for supply chain finance. By using an integrated approach, this study adopted the decision- making trial and evaluation laboratory (DEMATEL) and several analytical methods to construct a hybrid decision model for banks. We concluded four plausible Fintech strategies from previous research and highlighted the advantages of the blockchain-based strategy. We used a domestic bank in Taiwan as a case study during the evaluation phase and implemented crisp and confidence-based fuzzy assessments. The result indicates that the blockchain-based leading strategy would be ideal for this bank. The hybrid decision model also unveils the complicated relationships among those evaluation factors, which sheds light on banks pursuing their innovation in financial services. The findings contribute to banks developing their Fintech-based supply chain financing business, and the supply chain participants may also benefit from securing efficient loans to expedite their operations. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

18 pages, 4623 KiB

Open AccessArticle

An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

by Kathiresan Shankar, Sachin Kumar, Ashit Kumar Dutta, Ahmed Alkhayyat, Anwar Ja’afar Mohamad Jawad, Ali Hashim Abbas and Yousif K. Yousif

Mathematics 2022, 10(13), 2358; https://doi.org/10.3390/math10132358 - 05 Jul 2022

Cited by 18 | Viewed by 2506

Abstract

Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. [...] Read more.

Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. As a result, intelligent systems using machine learning and computer vision approaches were explored for ripeness grading, fruit defect categorization, and identification over the last few years. Recently, deep learning (DL) methods for classifying fruits led to promising performance that effectively extracts the feature and carries out an end-to-end image classification. This paper introduces an Automated Fruit Classification using Hyperparameter Optimized Deep Transfer Learning (AFC-HPODTL) model. The presented AFC-HPODTL model employs contrast enhancement as a pre-processing step which helps to enhance the quality of images. For feature extraction, the Adam optimizer with deep transfer learning-based DenseNet169 model is used in which the Adam optimizer fine-tunes the initial values of the DenseNet169 model. Moreover, a recurrent neural network (RNN) model is utilized for the identification and classification of fruits. At last, the Aquila optimization algorithm (AOA) is exploited for optimal hyperparameter tuning of the RNN model in such a way that the classification performance gets improved. The design of Adam optimizer and AOA-based hyperparameter optimizers for DenseNet and RNN models show the novelty of the work. The performance validation of the presented AFC-HPODTL model is carried out utilizing a benchmark dataset and the outcomes report the promising performance over its recent state-of-the-art approaches. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

20 pages, 1405 KiB

Open AccessArticle

Identifying Key Risk Factors in Product Development Projects

by Yu-Jing Chiu, Yi-Chung Hu, Chia-Yin Yao and Chia-Hung Yeh

Mathematics 2022, 10(8), 1295; https://doi.org/10.3390/math10081295 - 13 Apr 2022

Cited by 6 | Viewed by 2614

Abstract

In a rapidly changing world, most operational processes of enterprises are conducted in the form of development projects. The development of new products is an important organizational strategy to meet consumer needs. During this process, enterprises often encounter many bottlenecks and risks that [...] Read more.

In a rapidly changing world, most operational processes of enterprises are conducted in the form of development projects. The development of new products is an important organizational strategy to meet consumer needs. During this process, enterprises often encounter many bottlenecks and risks that can cause delays in, and even the failure of, development projects. In this study, we developed a research framework based on relevant literature and expert interviews and then used the decision making trial and evaluation laboratory (DEMATEL) and the analytic network process (ANP) to determine the relationships among and the importance of risks in the development of new products. The results of a case study show that the six key risks of product development projects include project completion time, mastery of key technical capabilities, controlling the progress of the project, uniqueness and complexity of the project, ability to control the market, and functional integrity of the product. According to the results of importance performance analysis, six key factors were classified in the concentrate quadrant. The optoelectronic manufacturing industry should focus on reducing risks to the project. A cause-and-effect diagram shows that if an enterprise wants to improve performance in terms of these key factors, it should first improve the project completion time or the mastery of key technical capabilities. Therefore, it is appropriate to start by improving the project completion time. In this study, we developed a practical and simple decision support system that allows managers of research and development to examine the risk of projects and assess the relevant risks. A case study was also conducted to test the accuracy of the proposed risk-management method. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

17 pages, 301 KiB

Open AccessArticle

Guaranteed Estimation of Solutions to the Cauchy Problem When the Restrictions on Unknown Initial Data Are Not Posed

by Oleksandr Nakonechnyi, Yuri Podlipenko and Yury Shestopalov

Mathematics 2021, 9(24), 3218; https://doi.org/10.3390/math9243218 - 13 Dec 2021

Viewed by 1453

Abstract

The paper deals with Cauchy problems for first-order systems of linear ordinary differential equations with unknown data. It is assumed that the right-hand sides of equations belong to certain bounded sets in the space of square-integrable vector-functions, and the information about the initial [...] Read more.

The paper deals with Cauchy problems for first-order systems of linear ordinary differential equations with unknown data. It is assumed that the right-hand sides of equations belong to certain bounded sets in the space of square-integrable vector-functions, and the information about the initial conditions is absent. From indirect noisy observations of solutions to the Cauchy problems on a finite system of points and intervals, the guaranteed mean square estimates of linear functionals on unknown solutions of the problems under consideration are obtained. Under an assumption that the statistical characteristics of noise in observations are not known exactly, it is proved that such estimates can be expressed in terms of solutions to well-defined boundary value problems for linear systems of impulsive ordinary differential equations. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

20 pages, 1807 KiB

Open AccessArticle

E-Learning Platform Assessment and Selection Using Two-Stage Multi-Criteria Decision-Making Approach with Grey Theory: A Case Study in Vietnam

by Pham Ngoc Toan, Thanh-Tuan Dang and Le Thi Thu Hong

Mathematics 2021, 9(23), 3136; https://doi.org/10.3390/math9233136 - 05 Dec 2021

Cited by 16 | Viewed by 3362

Abstract

Education has changed dramatically due to the severe global pandemic COVID-19, with the phenomenal growth of e-learning, whereby teaching is undertaken remotely and on digital platforms. E-learning is revolutionizing education systems, as it remains the only option during the ongoing crisis and has [...] Read more.

Education has changed dramatically due to the severe global pandemic COVID-19, with the phenomenal growth of e-learning, whereby teaching is undertaken remotely and on digital platforms. E-learning is revolutionizing education systems, as it remains the only option during the ongoing crisis and has tremendous potential to fulfill instructional plans and safeguard students’ learning rights. The selection of e-learning platforms is a multi-criteria decision-making (MCDM) problem. Expert analyses over numerous criteria and alternatives are usually linguistic terms, which can be represented through grey numbers. This article proposes an integrated approach of grey analytic hierarchy process (G-AHP) and grey technique for order preference by similarity to ideal solution (G-TOPSIS) to evaluate the best e-learning website for network teaching. This introduced approach handles the linguistic evaluation of experts based on grey systems theory, estimates the relative importance of evaluation criteria with the G-AHP method, and acquires e-learning websites’ ranking utilizing G-TOPSIS. The applicability and superiority of the presented method are illustrated through a practical e-learning website selection case in Vietnam. From G-AHP analysis, educational level, price, right and understandable content, complete content, and up-to-date were found as the most impactful criteria. From G-TOPSIS, Edumall is the best platform. Comparisons are conducted with other MCDM methods; the priority orders of the best websites are similar, indicating the robust proposed methodology. The proposed integrated model in this study supports the stakeholders in selecting the most effective e-learning environments and could be a reference for further development of e-learning teaching-learning systems. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

14 pages, 6654 KiB

Open AccessArticle

Global and Local Analysis for a Cournot Duopoly Game with Two Different Objective Functions

by Sameh Askar, Abdulaziz Foul, Tarek Mahrous, Saleh Djemele and Emad Ibrahim

Mathematics 2021, 9(23), 3119; https://doi.org/10.3390/math9233119 - 03 Dec 2021

Cited by 2 | Viewed by 1435

Abstract

In this paper, a Cournot game with two competing firms is studied. The two competing firms seek the optimality of their quantities by maximizing two different objective functions. The first firm wants to maximize an average of social welfare and profit, while the [...] Read more.

In this paper, a Cournot game with two competing firms is studied. The two competing firms seek the optimality of their quantities by maximizing two different objective functions. The first firm wants to maximize an average of social welfare and profit, while the second firm wants to maximize their relative profit only. We assume that both firms are rational, adopting a bounded rationality mechanism for updating their production outputs. A two-dimensional discrete time map is introduced to analyze the evolution of the game. The map has four equilibrium points and their stability conditions are investigated. We prove the Nash equilibrium point can be destabilized through flip bifurcation only. The obtained results show that the manifold of the game’s map can be analyzed through a one-dimensional map whose analytical form is similar to the well-known logistic map. The critical curves investigations show that the phase plane of game’s map is divided into three zones and, therefore, the map is not invertible. Finally, the contact bifurcation phenomena are discussed using simulation. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

23 pages, 3231 KiB

Open AccessArticle

Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function

by Irina Vinogradova-Zinkevič

Mathematics 2021, 9(19), 2455; https://doi.org/10.3390/math9192455 - 02 Oct 2021

Cited by 3 | Viewed by 1806

Abstract

Much applied research uses expert judgment as a primary or additional data source, thus the problem solved in this publication is relevant. Despite the expert’s experience and competence, the evaluation is subjective and has uncertainty in it. There are various reasons for this [...] Read more.

Much applied research uses expert judgment as a primary or additional data source, thus the problem solved in this publication is relevant. Despite the expert’s experience and competence, the evaluation is subjective and has uncertainty in it. There are various reasons for this uncertainty, including the expert’s incomplete competence, the expert’s character and personal qualities, the expert’s attachment to the opinion of other experts, and the field of the task to be solved. This paper presents a new way to use the Bayesian method to reduce the uncertainty of an expert judgment by correcting the expert’s evaluation by the a posteriori mean function. The Bayesian method corrects the expert’s evaluation, taking into account the expert’s competence and accumulated long-term experience. Since the paper uses a continuous case of the Bayesian formula, perceived as a continuous approximation of experts’ evaluations, this is not only the novelty of this work, but also a new result in the theory of the Bayesian method and its application. The paper investigates various combinations of the probability density functions of a priori information and expert error. The results are illustrated by the example of the evaluation of distance learning courses. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

22 pages, 3093 KiB

Open AccessArticle

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

by Farrukh Saleem, Zahid Ullah, Bahjat Fakieh and Faris Kateb

Mathematics 2021, 9(17), 2078; https://doi.org/10.3390/math9172078 - 27 Aug 2021

Cited by 23 | Viewed by 3603

Abstract

Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in [...] Read more.

Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

20 pages, 5618 KiB

Open AccessArticle

A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management

by Gholamreza Haseli, Reza Sheikh, Jianqiang Wang, Hana Tomaskova and Erfan Babaee Tirkolaee

Mathematics 2021, 9(16), 1881; https://doi.org/10.3390/math9161881 - 07 Aug 2021

Cited by 36 | Viewed by 3365

Abstract

Due to the complexity of real-world multi-criteria decision-making (MCDM) issues, analyzing different opinions from a group of decision makers needs to ensure appropriate decision making. The group decision-making methods collect preferences of the decision makers and present the best preferences using mathematical equations. [...] Read more.

Due to the complexity of real-world multi-criteria decision-making (MCDM) issues, analyzing different opinions from a group of decision makers needs to ensure appropriate decision making. The group decision-making methods collect preferences of the decision makers and present the best preferences using mathematical equations. The best–worst method (BWM) is one of the recently introduced MCDM methods that requires fewer pairwise comparisons to obtain the criteria weights than the other MCDM methods. In this research, we develop a novel approach to group decision-making problems based on the BWM called G-BWM. This approach helps us to analyze the preferences of decision makers to carry out democratic decision making using the BWM structure. In order to assess the applicability of the proposed methodology and represent its novelty, two numerical examples from the literature with the application to supply chain management (SCM) (i.e., green supplier selection and supplier development/segmentation) are examined and discussed. The results demonstrate the performance of our proposed G-BWM for group decision making in terms of a large number of decision makers, ease of use and achieving democratic decisions in the decision-making process. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

30 pages, 1247 KiB

Open AccessEditor’s ChoiceArticle

An Integrated Decision-Making Approach for Green Supplier Selection in an Agri-Food Supply Chain: Threshold of Robustness Worthiness

by Erfan Babaee Tirkolaee, Zahra Dashtian, Gerhard-Wilhelm Weber, Hana Tomaskova, Mehdi Soltani and Nasim Sadat Mousavi

Mathematics 2021, 9(11), 1304; https://doi.org/10.3390/math9111304 - 07 Jun 2021

Cited by 50 | Viewed by 4362

Abstract

Along with the increased competition in production and service areas, many organizations attempt to provide their products at a lower price and higher quality. On the other hand, consideration of environmental criteria in the conventional supplier selection methodologies is required for companies trying [...] Read more.

Along with the increased competition in production and service areas, many organizations attempt to provide their products at a lower price and higher quality. On the other hand, consideration of environmental criteria in the conventional supplier selection methodologies is required for companies trying to promote green supply chain management (GSCM). In this regard, a multi-criteria decision-making (MCDM) technique based on analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is used to evaluate and rate the suppliers. Then, considering the resource constraint, weight of criteria and a rank of suppliers are taken into account in a multi-objective mixed-integer linear programming (MOMILP) to determine the optimum order quantity of each supplier under uncertain conditions. To deal with the uncertain multi-objectiveness of the proposed model, a robust goal programming (RGP) approach based on Shannon entropy is applied. The offered methodology is applied to a real case study from a green service food manufacturing company in Iran in order to verify its applicability with a sensitivity analysis performed on different uncertainty levels. Furthermore, the threshold of robustness worthiness (TRW) is studied by applying different budgets of uncertainty for the green service food manufacturing company. Finally, a discussion and conclusion on the applicability of the methodology is provided, and an outlook to future research projects is given. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

23 pages, 4452 KiB

Open AccessArticle

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

by Mohammad Dehghani, Zeinab Montazeri and Štěpán Hubálovský

Mathematics 2021, 9(11), 1190; https://doi.org/10.3390/math9111190 - 24 May 2021

Cited by 17 | Viewed by 2290

Abstract

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions [...] Read more.

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms. Full article

(This article belongs to the Special Issue Decision Making and Its Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Decision Making and Its Applications

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (11 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI