Multiple-Criteria Decision-Making and Computational Intelligence: Recent Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 34267
Please contact the Guest Editor or the Journal Editor for any queries about the scope, discount, submission procedure and publication process.

Special Issue Editors

Faculty of Applied Management, Economics and Finance in Belgrade, University Business Academy in Novi Sad, Jevrejska 24, 11000 Belgrade, Serbia
Interests: multiple-criteria decision-making (MCDM); decision support systems (DSS); computational intelligence; decision-making theory; informatics; management
Special Issues, Collections and Topics in MDPI journals
Technical Faculty in Bor, University of Belgrade, 19210 Bor, Serbia
Interests: decision-making theory; expert systems; intelligent decision support systems
Special Issues, Collections and Topics in MDPI journals
Faculty of Applied Management, Economics and Finance in Belgrade, University Business Academy in Novi Sad, 11000 Belgrade, Serbia
Interests: multiple-criteria decision-making; operational research; management
Special Issues, Collections and Topics in MDPI journals
Department of International Trade and Logistics, Faculty of Economics and Administrative Sciences, Sivas Cumhuriyet University, 58140 Sivas, Turkey
Interests: fuzzy; multi criteria decision making; stochastic
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Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovića bb, 36300 Novi Pazar, Serbia
Interests: cryptography; steganography; data protection; machine learning; applied mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human decision makers are guided by their own experience and intuition. Multiple-criteria decision-making (MCDM) and application of mathematical methods significantly reduces the influence of subjectivism and intuition in decision making. Multi-criteria decision-making is the process of choosing one alternative from a set of available alternatives or, in some cases, involves ranking alternatives based on a predefined set of specific criteria that usually have different meanings.

Computational intelligence (CI) is based on the three main complementary techniques: neural networks, fuzzy systems, and evolutionary computing. CI represents the mechanisms of intelligent behavior in complex and changing environments; mechanisms that can learn, adapt, etc.

This unique Special Issue on “Multiple-Criteria Decision-Making and Computational Intelligence: Recent Applications“ will include recent developments and applications in the aforementioned two areas. Topics include, but are not limited to:

  • Decision theory and methods;
  • MCDM in management;
  • MCDM in engineering;
  • Fuzzy, neutrosophic, and grey MCDM methods;
  • Decision support systems;
  • Group decision-making;
  • Computational intelligence;
  • Fuzzy systems;
  • Artificial neural networks;
  • Evolutionary computing;
  • Data mining and text mining;
  • Probabilistic methods;
  • Computational learning theory.

Prof. Dr. Darjan Karabašević
Prof. Dr. Dragiša Stanujkić
Prof. Dr. Gabrijela Popovic
Prof. Dr. Alptekin Ulutaş
Prof. Dr. Muzafer Saračević
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MCDM
  • decision support systems
  • group decision-making
  • fuzzy systems
  • artificial neural networks
  • evolutionary computing

Published Papers (15 papers)

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Research

19 pages, 986 KiB  
Article
A Hybrid Model for Evaluating the Bikeability of Urban Bicycle Systems
by Chao-Che Hsu, Ya-Wen Kuo and James J. H. Liou
Axioms 2023, 12(2), 155; https://doi.org/10.3390/axioms12020155 - 02 Feb 2023
Cited by 2 | Viewed by 1154
Abstract
Improving people’s willingness to ride bicycles has become the main green transportation policy of the government in the world. Bikeability is an important factor affecting the willingness to ride. Since the urban riding environment is more complex than the suburbs, it is necessary [...] Read more.
Improving people’s willingness to ride bicycles has become the main green transportation policy of the government in the world. Bikeability is an important factor affecting the willingness to ride. Since the urban riding environment is more complex than the suburbs, it is necessary to establish a complete urban bikeability evaluation framework. This study applies Bayesian BWM (Best Worst Method) and modified VIKOR to develop an urban bikeability evaluation framework. First, this study collects criteria affecting urban bikeability through literature review and experts’ surveys to develop a novel evaluation framework. Second, the Bayesian BWM was used to evaluate the relative weights of criteria and dimensions. Finally, the modified VIKOR was used to evaluate the riding environment of urban bicycle systems. The modified VIKOR replaces the relatively good concept as the aspiration level, which can effectively reflect the real situation. This study used two cities of Taiwan as case studies to demonstrate the usefulness and effectiveness of the proposed model. The results show that “completeness of facilities” is the most important dimension and “maintenance of bicycle pavements”, “width of bicycle lanes”, and “separation of bicycle lanes and car lanes” are the critical criteria. Based on the findings, some management implications and improving strategies are provided. Full article
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19 pages, 1205 KiB  
Article
Use of the WASPAS Method to Select Suitable Helicopters for Aerial Activity Carried Out by the Military Police of the State of Rio de Janeiro
by Gustavo Soares de Assis, Marcos dos Santos and Marcio Pereira Basilio
Axioms 2023, 12(1), 77; https://doi.org/10.3390/axioms12010077 - 12 Jan 2023
Cited by 24 | Viewed by 2753
Abstract
Using a multi-criteria decision support method (WASPAS) to analyze and rank alternatives, this article proposes a method to assist in the selection of helicopter models that are the most suitable for police air activity in the State of Rio de Janeiro. A robust [...] Read more.
Using a multi-criteria decision support method (WASPAS) to analyze and rank alternatives, this article proposes a method to assist in the selection of helicopter models that are the most suitable for police air activity in the State of Rio de Janeiro. A robust technical basis for defining the essential requirements of an aircraft is established, and solutions that can ensure the effective and safe execution of missions are indicated. Helicopter models were evaluated by considering predefined criteria, and the weights of these criteria were attributed using a questionnaire that was administered to pilots and aerostatic operators of Public Air Units (UAP) in several states of the federation. As a result of the evaluation of the 15 helicopter models used by police services in the State of Rio de Janeiro, the modeling with the WASPAS method ranked the Sikorsky UH-60 (Black Hawk) model in first place, the Leonardo AW 139 model in second place, and the Bell 412 model in third place. Based on the available data, we suggest that a comparative study integrating the Entropy and CRITIC methods be conducted to measure the weights of the criteria associated with the application of other multi-criteria techniques, such as COMET, MACAB, SPOTIS, VIKOR, SAPEVO, and PROMETHEE. Full article
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29 pages, 3429 KiB  
Article
Improved Multidimensional Quality of Life Index Based on Outranking Relations
by María Auxiliadora De Vicente Oliva and Alberto Romero-Ania
Axioms 2023, 12(1), 41; https://doi.org/10.3390/axioms12010041 - 30 Dec 2022
Cited by 1 | Viewed by 1599
Abstract
The aim of this research is to propose an improved multidimensional quality of life index, which could replace the current methodology designed by Eurostat and applied by the national statistical institutes of the European Union member states. The novelty of the proposed index [...] Read more.
The aim of this research is to propose an improved multidimensional quality of life index, which could replace the current methodology designed by Eurostat and applied by the national statistical institutes of the European Union member states. The novelty of the proposed index is that it is based on a non-compensatory multicriteria decision method (ELECTRE III). All other quality of life indices propose compensatory aggregation methods at some stage in the construction of the index. The data used in this study are openly available on the website of the INE, which is the Spanish National Statistics Institute, and were obtained by INE from population surveys. The data were entered by the authors in the Diviz software to conduct an ELECTRE III method. Three innovative versions for the multidimensional quality of life index are proposed in this study, which are called Basic ELECTRE, Full ELECTRE, and Full Fuzzy ELECTRE. The comparison of the results obtained by INE with the results provided by our proposals shows that it is possible to construct an improved multidimensional quality of life index to be applied by the member states of the European Union. Full article
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13 pages, 1964 KiB  
Article
Efficient Cancelable Template Generation Based on Signcryption and Bio Hash Function
by Vani Rajasekar, Muzafer Saračević, Darjan Karabašević, Dragiša Stanujkić, Eldin Dobardžić and Sathya Krishnamoorthi
Axioms 2022, 11(12), 684; https://doi.org/10.3390/axioms11120684 - 29 Nov 2022
Viewed by 1069
Abstract
Cancelable biometrics is a demanding area of research in which a cancelable template conforming to a biometric is produced without degrading the efficiency. There are numerous approaches described in the literature that can be used to generate these cancelable templates. These approaches do [...] Read more.
Cancelable biometrics is a demanding area of research in which a cancelable template conforming to a biometric is produced without degrading the efficiency. There are numerous approaches described in the literature that can be used to generate these cancelable templates. These approaches do not, however, perform well in either the qualitative or quantitative perspective. To address this challenge, a unique cancelable template generation mechanism based on signcryption and bio hash function is proposed in this paper. Signcryption is a lightweight cryptographic approach that uses hyper elliptic curve cryptography for encryption and a bio hash function for generating signatures in this proposed method. The cancelable templates are generated from iris biometrics. The hybrid grey level distancing method is used for perfect iris feature extraction for the CASIA and IITD datasets. The proposed approach is compared against the existing state-of-the-art cancelable techniques. The resulting analysis reveals that the proposed method is efficient in terms of accuracy of 98.86%, with lower EER of 0.1%. The average minimum TPR and TNR of the proposed method is about 99.81%. Full article
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24 pages, 2581 KiB  
Article
A Fuzzy Decision-Making Soft Model for Family Financial Planning in the Post-COVID-19 World
by Chia-Chi Sun
Axioms 2022, 11(9), 452; https://doi.org/10.3390/axioms11090452 - 02 Sep 2022
Cited by 2 | Viewed by 1428
Abstract
With COVID-19 still making headlines around the world, many people currently feel uncertain about many aspects of life, including family financial planning and wealth management. Financial planning is important at all times, but it becomes essential during a crisis such as the coronavirus [...] Read more.
With COVID-19 still making headlines around the world, many people currently feel uncertain about many aspects of life, including family financial planning and wealth management. Financial planning is important at all times, but it becomes essential during a crisis such as the coronavirus pandemic, which has disrupted people’s finances. Some economic consequences are already apparent, but the financially-induced stress caused by the uncertainty is less visible. With the increase in family wealth and size of organizations, there is a comparable increase in their assets. There is considerable demand for professionals to manage these assets and coordinate investment activities in order to maintain growth. This raises the issue of how to increase a wealth management bank’s competitive advantages. This study approached the issue by using experts and the application of fuzzy logic and decision-making trial and evaluation laboratory and multiple criteria decision-making to segment a set of the selection criteria used by prospective customers, to select a wealth management bank that can effectively manage personal wealth. The results showed that the management’s learning and growth perspective was the most important factor in respondents’ selection of a wealth management bank. This paper also provides managerial practice implications. Full article
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23 pages, 366 KiB  
Article
Incomplete Complex Intuitionistic Fuzzy System: Preference Relations, Expert Weight Determination, Group Decision-Making and Their Calculation Algorithms
by Fangdi Wang, Zengtai Gong and Yabin Shao
Axioms 2022, 11(8), 418; https://doi.org/10.3390/axioms11080418 - 19 Aug 2022
Cited by 4 | Viewed by 1227
Abstract
As is well known, complex intuitionistic fuzzy preference relation can describe the fuzzy characters of things in more detail and comprehensively and is very useful in dealing with decision-making problems that include periodic or recurring phenomena. However, sometimes, a decision-maker may provide incomplete [...] Read more.
As is well known, complex intuitionistic fuzzy preference relation can describe the fuzzy characters of things in more detail and comprehensively and is very useful in dealing with decision-making problems that include periodic or recurring phenomena. However, sometimes, a decision-maker may provide incomplete judgments in a complex intuitionistic fuzzy preference relation because of a lack of knowledge, time pressure, and the decision-makers’ limited expertise related to the problem domain. In such cases, it would be sensible not to force the expert to express “false” preferences over these objects. Consequently, how to define incomplete complex intuitionistic fuzzy preference relations and to estimate their missing elements in an incomplete complex intuitionistic fuzzy preference relation becomes a necessary step in a decision-making process. In this paper, the concept of incomplete complex intuitionistic fuzzy preference relation is introduced and its properties are discussed. Meanwhile, the multiplicative consistent incomplete complex intuitionistic fuzzy preference relations are defined. Secondly, estimating algorithms are developed to estimate the missing elements in the acceptable incomplete complex intuitionistic fuzzy preference relations. Finally, an expert weight determination algorithm and the group decision-making algorithms based on incomplete complex intuitionistic fuzzy preference relations are established. The solving process of the algorithms is illustrated by an example, the practicability of the algorithms is verified, the advantages and disadvantages of two group decision-making algorithms are compared and analyzed, and the simulation verification of incomplete complex intuitionistic fuzzy system is carried out by MATLAB software. The framework proposed in this paper effectively generalizes and enriches the previous works and has a good application prospect. Full article
19 pages, 3235 KiB  
Article
An Explainable Machine Learning Framework for Forecasting Crude Oil Price during the COVID-19 Pandemic
by Xinran Gao, Junwei Wang and Liping Yang
Axioms 2022, 11(8), 374; https://doi.org/10.3390/axioms11080374 - 29 Jul 2022
Cited by 4 | Viewed by 2697
Abstract
Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting [...] Read more.
Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting crude oil price movements and interpreting the predictions. First, it compares six advanced machine learning (ML) models, including two state-of-the-art methods: extreme gradient boosting (XGB) and the light gradient boosting machine (LGBM). Second, it selects novel data, including user search big data, digital currencies and data on the COVID-19 epidemic. The empirical results suggest that LGBM outperforms other alternative ML models. Finally, it proposes an interpretable framework for facilitating decision making to interpret the prediction results of complex ML models and for verifying the importance of various features affecting crude oil price. The results of this paper provide practical guidance for participants in the crude oil market. Full article
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13 pages, 401 KiB  
Article
A New Fuzzy Extension of the Simple WISP Method
by Darjan Karabašević, Alptekin Ulutaş, Dragiša Stanujkić, Muzafer Saračević and Gabrijela Popović
Axioms 2022, 11(7), 332; https://doi.org/10.3390/axioms11070332 - 08 Jul 2022
Cited by 3 | Viewed by 1431
Abstract
The purpose of this article is to introduce to the literature a new extension of the Simple WISP method adapted for utilizing the triangular fuzzy numbers. This extension is proposed to allow the use of the Simple WISP method for addressing decision-making problems [...] Read more.
The purpose of this article is to introduce to the literature a new extension of the Simple WISP method adapted for utilizing the triangular fuzzy numbers. This extension is proposed to allow the use of the Simple WISP method for addressing decision-making problems related to uncertainties and inaccuracies, as well as for solving problems related to predictions. In addition, this article also discusses the use of linguistic variables to collect the attitudes of the respondents, as well as their transformation into appropriate triangular fuzzy numbers. The article discusses the use of two defuzzification procedures. The first normalization procedure is easy to use, while the second procedure uses the advantages that the application of asymmetric fuzzy numbers gives in terms of analysis. The usability of the proposed extension is presented through two examples. Full article
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9 pages, 991 KiB  
Article
Parameter Estimation of the Exponentiated Pareto Distribution Using Ranked Set Sampling and Simple Random Sampling
by Hossein Jabbari Khamnei, Ieva Meidute-Kavaliauskiene, Masood Fathi, Asta Valackienė and Shahryar Ghorbani
Axioms 2022, 11(6), 293; https://doi.org/10.3390/axioms11060293 - 15 Jun 2022
Cited by 3 | Viewed by 2200
Abstract
In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no [...] Read more.
In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size. Full article
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16 pages, 2207 KiB  
Article
A GIS-MCDM Method for Ranking Potential Station Locations in the Expansion of Bike-Sharing Systems
by Mohammad Sadegh Bahadori, Alexandre B. Gonçalves and Filipe Moura
Axioms 2022, 11(6), 263; https://doi.org/10.3390/axioms11060263 - 31 May 2022
Cited by 1 | Viewed by 2134
Abstract
Bicycle-sharing systems (BSSs) are an effective solution to reduce private car usage in most cities and are an influential factor in encouraging citizens to shift to more sustainable transport modes. In this sense, the location of BSS stations has a critical impact on [...] Read more.
Bicycle-sharing systems (BSSs) are an effective solution to reduce private car usage in most cities and are an influential factor in encouraging citizens to shift to more sustainable transport modes. In this sense, the location of BSS stations has a critical impact on the system’s efficiency. This study proposed an integrated geographic information system–multi-criteria decision-making (GIS-MCDM) framework that includes the analytic hierarchy process (AHP), technique for order preference by similarity to the ideal solution (TOPSIS), and spatial data processing in GIS to determine a ranking of potential locations for BSS stations. The results of the proposed GIS-MCDM method can be used for both planning a new BSS or expanding one that is currently under operation. The framework was applied to a case study for expanding GIRA, the BSS of Lisbon, Portugal. In it, location criteria were selected in four categories, including criteria from the literature and extracted from available transaction data; in addition, we also suggested some criteria. The rebalancing operator’s staff were the decision makers in this study via their responses to the AHP questionnaire. The rebalancing staff believed that the main criterion of “city infrastructure” with the two sub-criteria of “population density” and “slope” were the most important. Furthermore, the proximity to the “bike network” with the sub-criterion of “proximity to the current bike stations” had less importance. Each criterion’s weight and inconsistency rate were obtained using the Expert Choice software. The geographic values of each criterion were created utilizing the ArcGIS software, and its network analyst module was employed for applying location techniques. Based on the created suitability map, the city’s center was the main suitable area for establishing new stations. Forty-five new bike stations were identified in those areas and ranked using the TOPSIS technique. Full article
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21 pages, 8145 KiB  
Article
The Possibility of Combining and Implementing Deep Neural Network Compression Methods
by Bratislav Predić, Uroš Vukić, Muzafer Saračević, Darjan Karabašević and Dragiša Stanujkić
Axioms 2022, 11(5), 229; https://doi.org/10.3390/axioms11050229 - 13 May 2022
Cited by 9 | Viewed by 2670
Abstract
In the paper, the possibility of combining deep neural network (DNN) model compression methods to achieve better compression results was considered. To compare the advantages and disadvantages of each method, all methods were applied to the ResNet18 model for pretraining to the NCT-CRC-HE-100K [...] Read more.
In the paper, the possibility of combining deep neural network (DNN) model compression methods to achieve better compression results was considered. To compare the advantages and disadvantages of each method, all methods were applied to the ResNet18 model for pretraining to the NCT-CRC-HE-100K dataset while using CRC-VAL-HE-7K as the validation dataset. In the proposed method, quantization, pruning, weight clustering, QAT (quantization-aware training), preserve cluster QAT (hereinafter PCQAT), and distillation were performed for the compression of ResNet18. The final evaluation of the obtained models was carried out on a Raspberry Pi 4 device using the validation dataset. The greatest model compression result on the disk was achieved by applying the PCQAT method, whose application led to a reduction in size of the initial model by as much as 45 times, whereas the greatest model acceleration result was achieved via distillation on the MobileNetV2 model. All methods led to the compression of the initial size of the model, with a slight loss in the model accuracy or an increase in the model accuracy in the case of QAT and weight clustering. INT8 quantization and knowledge distillation also led to a significant decrease in the model execution time. Full article
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16 pages, 3561 KiB  
Article
Co-Training Method Based on Semi-Decoupling Features for MOOC Learner Behavior Prediction
by Huanhuan Wang, Libo Xu, Zhenrui Huang and Jiagong Wang
Axioms 2022, 11(5), 223; https://doi.org/10.3390/axioms11050223 - 11 May 2022
Viewed by 1610
Abstract
Facing the problem of massive unlabeled data and limited labeled samples, semi-supervised learning is favored, especially co-training. Standard co-training requires sufficiently redundant and conditionally independent dual views; however, in fact, few dual views exist that satisfy this condition. To solve this problem, we [...] Read more.
Facing the problem of massive unlabeled data and limited labeled samples, semi-supervised learning is favored, especially co-training. Standard co-training requires sufficiently redundant and conditionally independent dual views; however, in fact, few dual views exist that satisfy this condition. To solve this problem, we propose a co-training method based on semi-decoupling features, that is, semi-decoupling features based on a known single view and then constructing independent and redundant dual views: (1) take a small number of important features as shared features of the dual views according to the importance of the features; (2) separate the remaining features one by one or in small batches according to the correlation between the features to make “divergent” features of the dual views; (3) combine the shared features and the “divergent” features to construct dual views. In this paper, the experimental dataset was from the edX dataset jointly released by Harvard University and MIT; the evaluation metrics adopted F1, Precision, and Recall. The analysis methods included three experiments: multiple models, iterations, and hyperparameters. The experimental results show that the effect of this model on MOOC learner behavior prediction was better than the other models, and the best prediction result was obtained in iteration 2. These all verify the effectiveness and superiority of this algorithm and provide a scientific and feasible reference for the development of the future education industry. Full article
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20 pages, 931 KiB  
Article
A MULTIMOORA-Based Risk Evaluation Approach for CCUS Projects by Utilizing D Numbers Theory
by Peilin Li, Lina Che, Luhe Wan and Liguo Fei
Axioms 2022, 11(5), 204; https://doi.org/10.3390/axioms11050204 - 26 Apr 2022
Cited by 3 | Viewed by 2024
Abstract
As the global climate warms, carbon emissions must be reduced in order to alleviate the human climate crisis. Carbon capture, utilization and storage (CCUS) is an emerging technology that can reduce carbon emissions. However, most of the CCUS projects have ended in failure. [...] Read more.
As the global climate warms, carbon emissions must be reduced in order to alleviate the human climate crisis. Carbon capture, utilization and storage (CCUS) is an emerging technology that can reduce carbon emissions. However, most of the CCUS projects have ended in failure. The reason can be attributed to insufficient risk assessment. To this end, the purpose of this study is to construct a comprehensive risk assessment model for CCUS projects. The main body of this research is divided into two parts. First, in order to evaluate the CCUS project, a risk indicator system is constructed. In what follows, a decision-making framework for risk assessment under the D numbers environment is proposed, including two stages of decision-making preparation and decision-making process. The main task of the preparation stage is to gather evaluation experts and collect decision-making information. In the decision-making stage, this paper takes the D numbers theory as the core (acting on the effective expression and fusion of subjective evaluation information), respectively, proposes the method of determining the weight of risk evaluators, the fusion method of decision-making information from different experts, and the comprehensive decision model based on the MULTIMOORA method. In order to verify the effectiveness of the constructed model, the case of CCUS project site selection in Shengli power plant is analyzed, and the results showed that the third site is the best option. This study finds the importance of a comprehensive and timely risk assessment for the successful implementation of CCUS projects, and suggests that stakeholders carry out a risk assessment of CCUS projects prior to implementation based on the method presented in this paper, so as to improve the success rate. Full article
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24 pages, 2043 KiB  
Article
Selection of Cold Chain Logistics Service Providers Based on a Grey AHP and Grey COPRAS Framework: A Case Study in Vietnam
by Ngoc-Ai-Thy Nguyen, Chia-Nan Wang, Le-Thanh-Hieu Dang, Le-Thanh-Tuyen Dang and Thanh-Tuan Dang
Axioms 2022, 11(4), 154; https://doi.org/10.3390/axioms11040154 - 27 Mar 2022
Cited by 17 | Viewed by 4919
Abstract
Choosing the most suitable cold chain logistics service providers (CLPs) is a vital strategic decision for businesses aiming to achieve an effective and sustainable cold supply chain. A sustainable CLP is one that integrates sustainable practices across its whole operation cycle to achieve [...] Read more.
Choosing the most suitable cold chain logistics service providers (CLPs) is a vital strategic decision for businesses aiming to achieve an effective and sustainable cold supply chain. A sustainable CLP is one that integrates sustainable practices across its whole operation cycle to achieve product quality, on-time deliveries, and satisfied customer requirements, while preventing products from going to waste, which is especially important in the context of a developing country. This study aims to evaluate and select the best CLP regarding their sustainability performance. For this evaluation, a multi-criteria decision making (MCDM)-based framework is proposed that integrates the grey analytic hierarchy process (G-AHP) and grey complex proportional assessment (G-COPRAS) methodologies, in which grey numbers are used to express the linguistic evaluation statements of experts. Initially, the evaluation criteria based on service level, economic, environmental, and social dimensions were determined by means of a literature review and experts’ opinions to employ the MCDM approach. The G-AHP was utilized to identify the criteria weights, and then, G-COPRAS was used to select the best CLP among the alternatives. A case illustration in Vietnam is presented to exhibit the presented approach’s applicability. From the G-AHP findings, product quality, logistics costs, innovation, and effectiveness of cold chain processes, customer experience, and CO emissions of refrigerated vehicle were ranked as the five most important criteria. From the G-COPRAS analysis, Yoshida Saigon Cold Logistic (CPL-05) is the best CLP. The robustness of the applied integrated MCDM approach was also tested by conducting a comparative analysis, in which the priority rankings of the best CLPs were very similar. The assessment in this study is directed towards enabling managers, practitioners, and stakeholders of cold chain businesses to assess the most efficient CLP in the supply chain in the market and also to devise suitable strategies toward sustainable development. Full article
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14 pages, 5823 KiB  
Article
Comparative Analysis of the Simple WISP and Some Prominent MCDM Methods: A Python Approach
by Dragiša Stanujkić, Darjan Karabašević, Gabrijela Popović, Edmundas Kazimieras Zavadskas, Muzafer Saračević, Predrag S. Stanimirović, Alptekin Ulutaş, Vasilios N. Katsikis and Ieva Meidute-Kavaliauskiene
Axioms 2021, 10(4), 347; https://doi.org/10.3390/axioms10040347 - 17 Dec 2021
Cited by 5 | Viewed by 2505
Abstract
This article presents a comparison of the results obtained using the newly proposed Simple Weighted Sum Product method and some prominent multiple criteria decision-making methods. For comparison, several analyses were performed using the Python programming language and its NumPy library. The comparison was [...] Read more.
This article presents a comparison of the results obtained using the newly proposed Simple Weighted Sum Product method and some prominent multiple criteria decision-making methods. For comparison, several analyses were performed using the Python programming language and its NumPy library. The comparison was also made on a real decision-making problem taken from the literature. The obtained results confirm the high correlation of the results obtained using the Simple Weighted Sum Product method and selected multiple criteria decision-making methods such as TOPSIS, SAW, ARAS, WASPAS, and CoCoSo, which confirms the usability of the Simple Weighted Sum Product method for solving multiple criteria decision-making problems. Full article
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