Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches

Sustainable agility of product development process based on a rough cloud technique: A case study on China's small and medium enterprises

The importance of incorporating an agile approach into creating sustainable products has been widely discussed. This approach can enhance innovation integration, improve adaptability to changing development circumstances, and increase the efficiency and quality of the product development process. While many agile methods have originated in the software development context and have been formulated based on successful software projects, they often fail due to incorrect procedures and a lack of acceptance, preventing deep integration into the process. Additionally, decision-making for market evaluation is often hindered by unclear and subjective information. Therefore, this study introduces an extended TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for sustainable product development. This method leverages the benefits of cloud model theory to address randomness and uncertainty (intrapersonal uncertainty) and the advantages of rough set theory to flexibly handle market demand uncertainty without requiring extra information. The study proposes an integrated weighting method that considers both subjective and objective weights to determine comprehensive criteria weights. It also presents a new framework, named Sustainable Agility of Product Development (SAPD), which aims to evaluate criteria for assessing sustainable product development. To validate the effectiveness of this proposed method, a case study is conducted on small and medium enterprises in China. The obtained results show that the company needs to conduct product structure research and development to realize new product functions.

Sustainable supplier selection based on VIKOR with single-valued neutrosophic sets

Considering economic, environmental, and social issues, the sustainability of the supply chain has drawn considerable attention due to societal and environmental changes within the supply chain network. The strategic study of the entire supply chain process and maximizing an organization's competitive advantage depend heavily on supplier selection based on sustainable indicators. Selecting sustainable suppliers for the supply chain is challenging since it is a multi-criteria decision-making (MCDM) problem with significant uncertainty in the decision-making process. This study uses the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique and single-valued neutrosophic sets (SVNS) to deal with the challenge of choosing a sustainable supplier with insufficient information. This method reduces the influence of personal experience and preference on the final evaluation results and the problem of excessive individual regret caused by factor correlation and improves the consistency of evaluation results. Finally, the method's success and adaptability are demonstrated by sensitivity analysis and additional comparison analysis, and the benefits and drawbacks of the suggested framework are examined. Compared to other approaches, it can assist decision-makers in communicating fuzzy and uncertain information, offering a perspective and approach for MCDM in the face of such situations, and helping them select suppliers of high caliber and who practice sustainable business practices.

Sustainability assessment in construction projects: a sustainable earned value management model under uncertain and unreliable conditions

All three pillars of sustainability must be met for a construction project to be considered sustainable. Although several studies have been carried out on sustainability in the construction industry under uncertain conditions, there has been a lack of a comprehensive model to assess all three pillars of sustainability during the execution of a construction project with consideration of the uncertain and unreliable nature of the gathered data. In this article, a fuzzy inference system model was developed to fill this research gap. The Cost Performance Index from earned value management, an effective tool for cost control of projects, is used to assess the economic sustainability status during the project execution. Besides, from the expert opinions and literature review, sustainability-related attributes are gathered to identify the dimensions and enablers of a construction project. This research aimed not only to improve the previous social sustainability Indexes for construction projects by using Z-numbers but also to develop and introduce a comprehensive Z Construction Environmental Sustainability Index alongside the other two pillars. The previous hierarchal method to calculate the Fuzzy indexes is also improved to overcome possible complexity problems. Calculating the three pillars of sustainability and using them as inputs into the Mamdani FIS model to obtain the overall sustainability status, the proposed model, which is calculated with the Z-numbers, is compared with the results of conventional Fuzzy numbers and deterministic approach. The results of the comparison show that the proposed model is more rigorous and effective than the previous methods in the numerical case.

A two-level groups consensus reaching process for selecting suppliers of complex equipment under uncertain environment

With the development of complexity in complex equipment, the selection of suppliers referred to several groups. How to select the suppliers for the complex equipment under several groups becomes an important topic. To solve the problem, a two-level consensus reaching process is designed to select the suppliers of the complex equipment in uncertain environments. First, considering the fuzzy environment of selection, the cloud model, which could reflect the fuzziness and randomness, is used to present the uncertain preferences of the decision-makers. Then, considering the negotiation and interaction of two groups, the bi-level consensus reaching process is established to present the master-slave features of complex equipment. Third, to solve the proposed bi-level model, the improved artificial bee colony is proposed, which adopts the gray wolf algorithm’ searching mechanism and levy flying method. The adopted strategies could enhance the searching power of artificial bee colony. Finally, a case study is used to verify the advantages of our study.

A Hybrid OPA and Fuzzy MARCOS Methodology for Sustainable Supplier Selection with Technology 4.0 Evaluation

The concern of sustainable supplier selection has been raised recently in organizations’ decision making to enhance their competitiveness. Many tools have been developed to support supplier evaluation, yet the factors of Industry 4.0 (I4.0) have been ignored despite their impact on sustainable performance. Hence, this paper aims to include the technology of I4.0 as the criteria to evaluate the competence of suppliers in sustainability. Multiple-criteria decision making (MCDM) has been used to build decision-making systems; thus, this study employed two advanced methods of MCDM, the ordinal priority approach (OPA) and measurement of alternatives and ranking according to compromise solution (MARCOS) in a fuzzy environment. To test the feasibility of the proposal, five manufacturers of Vietnam’s leather and footwear industry were hypothetically assigned. Firstly, the evaluation criteria were weighted by OPA. Then, the ranking of alternatives was determined by fuzzy MARCOS. The results show that “green image”, “green product innovation”, “cloud computing”, “service level”, and “blockchain” are the topmost significant criteria in evaluating sustainable practices in the supply chain from the I4.0 perspective. Furthermore, sensitivity and comparison analyses were carried out to verify the robustness of the methodology. The outcomes of this paper contribute a new model of decision making with respect to the involvement of sustainability and I4.0.

Supplier Selection through Multicriteria Decision-Making Algorithmic Approach Based on Rough Approximation of Fuzzy Hypersoft Sets for Construction Project

The suppliers play a significant role in supply chain management. In supplier selection, factors like market-based exposure, community-based reputation, trust-based status, etc., must be considered, along with the opinions of hired experts. These factors are usually termed as rough information. Most of the literature has disregarded such factors, which may lead to a biased selection. In this study, linguistic variables in terms of triangular fuzzy numbers (TrFn) are used to manage such kind of rough information, then the rough approximations of the fuzzy hypersoft set (FHS-set) are characterized which are capable of handling such informational uncertainties. The FHS-set is more flexible as well as consistent as it tackles the limitation of fuzzy soft sets regarding categorizing parameters into their related sub-classes having their sub-parametric values. Based on these rough approximations, an algorithm is proposed for the optimal selection of suppliers by managing experts’ opinions and rough information collectively in the form of TrFn-based linguistic variables. To have a discrete decision, a signed distance method is employed to transform the TrFn-based opinions of experts into fuzzy grades. The proposed algorithm is corroborated with the help of a multi-criteria decision-making application to choose the best supplier for real estate builders. The beneficial facets of the put forward study are appraised through its structural comparison with few existing related approaches. The presented approach is consistent as it is capable to manage rough information and expert’s opinions about suppliers collectively by using rough approximations of FHS-set.

A New Hybrid Triple Bottom Line Metrics and Fuzzy MCDM Model: Sustainable Supplier Selection in the Food-Processing Industry

Vietnam’s food processing and production industries in the past have managed to receive many achievements, contributing heavily to the growth of the country’s economic growth, especially the production index. Even with an increase of 7% per year over the past five years, the industry currently also faces problems and struggles that require business managers to rewrite legal documents and redevelop the business environment as well as the production conditions in order to compete better and use the available resources. Xanthan gum (a food additive and a thickener) is one of the most used ingredients in the food-processing industry. Xanthan gum is utilized in a number of variety of products such as canned products, ice cream, meats, breads, candies, drinks, milk products, and many others. Therefore, in order to improve competitiveness, the stage of selecting raw-material suppliers is a complicated task. The purpose of this study was to develop a new composite model using Triple Bottom Line Metrics, the Fuzzy Analytical Hierarchy Process (FAHP) method, and the Combined Compromise Solution (CoCoSo) algorithm for the selection of suppliers. The application process was accomplished for the Xanthan-gum (β-glucopyranose (C35H49O29)n) supplier selection in a food processing industry. In this study, the model building, solution, and application processes of the proposed integrated model for the supplier selection in the food-processing industry are presented.

An integrated approach for a sustainable supplier selection based on Industry 4.0 concept

The recent advances in sustainable supply chain management are integrated with Industry 4.0 concepts. This study develops a new integrated model to consider the sustainability and Industry 4.0 criteria for the supplier selection management. The proposed approach consists of the fuzzy best worst method (FBWM) and the two-stage fuzzy inference system (FIS) to assess the selection of suppliers. Firstly, this study determines a comprehensive list of Industry 4.0 and sustainability criteria along with their definitions. Then, the importance weight of each criterion is computed by the FBWM. Subsequently, a two-stage FIS is devoted to nominate the suppliers' performance with regard to the sustainability and Industry 4.0 criteria. To show the applicability of our integrated model, a case study for a textile company in Iran is provided. Finally, some sensitivity analyses are done to assess the efficiency of the proposed integrated approach. One finding is to establish a decision-making framework to evaluate suppliers separately, rather than relatively in a fuzzy environment using Industry 4.0 and sustainability criteria.

Application of Fuzzy-TOPSIS Method in Supporting Supplier Selection with Focus on HSE Criteria: A Case Study in the Oil and Gas Industry

Supply chain management is an emerging topic in the oil and gas industry. There is higher exposure of contractors to undesirable incidents and supplier selection is a multicriteria decision problem (MCDM). A fuzzy-TOPSIS method was employed in the evaluation of three suppliers regarding four HSE criteria. This method was applied in a case study of the oil and gas industry involving a contractor bidding process. Results reinforced that fuzzy-TOPSIS is a versatile and suitable method for supplier selection problems, with low computational complexity and promoting a better user experience. This method contributes to greater effectiveness and agility in the selection processes of suppliers regarding HSE management. The fuzzy-TOPSIS model is suitable for supplier selection problems and some of the benefits of applying this method are that it allows the attribution weights according to the level of importance of each criterion and considers the complexity, subjectivity, and uncertainty of the decision process. One has determined that it was essential to have a robust and consistent process for weighting the criteria and defining the most appropriate linguistic variables.

Multi-Criteria Decision-Making Methods in Fuzzy Decision Problems: A Case Study in the Frozen Shrimp Industry

The European Union (EU) is the largest shrimp consumer market in the world in terms of requirements for shrimp product imports. Therefore, other enterprises that export frozen shrimp to the EU must consider many criteria when choosing suppliers of raw shrimp. The difficulty of choosing suppliers of raw shrimp makes selecting raw material suppliers in the fisheries sector a multi-criteria decision-making problem. In such problems, the decision makers must review and evaluate many criteria—including qualitative and quantitative factors—to achieve an optimal result. While there have been multiple multi-criteria decision making models developed to support supplier selection processes in different industries, none of these have been developed to solve the particular problems facing the shrimp industry, especially as it concerns a fuzzy decision-making environment. In this research, the authors propose a Multi-Criteria Decision Making model (MCDM) including the Fuzzy Analytical Network Process (FANP) and Weighted Aggregated Sum Product Assessment (WASPAS) for the evaluation and selection process of shrimp suppliers in the fisheries industry. The model is applied to a real-world case study and the results show that Supplier 3 (SA3) is the most optimal supplier of raw shrimp. The contribution of this work is the employment of FANP and WASPAS to propose an MCDM for ranking potential suppliers in the fisheries industry in a fuzzy environment. The proposed approach can also be modified to support complex decision-making processes in fuzzy environments in different industries.