Green Supplier Evaluation and Selection with an Extended MABAC Method Under the Heterogeneous Information Environment

Assessing industrialized countries' environmental sustainability performances using an integrated multi-criteria model and software

This study seeks to identify and highlight the differences in the environmental sustainability and environmental protection practices of industrialized countries. For this purpose, thirty industrialized countries were examined using a multi-criteria integrated decision model based on fourteen environmental sustainability performance criteria. An open-source software based on the Python that allows different multi-criteria decision-making methods to be used through a user-friendly interface was developed for evaluations. The results from the developed software rank Sweden first and India last in their environmental sustainability performances. Sweden stands out notably in terms of negative greenhouse gas emission technologies, afforestation, reforestation, environmental laws, carbon tax, bioenergy, and national consciousness. India, on the other hand, does not appear to handle environmental problems in a timely and appropriate manner considering its large population and development needs. Development, utilization, and expansion of renewable energy sources, waste recycling, waste reduction, proper waste disposal, and prioritization of the development of technologies with zero or negative greenhouse gas emissions have emerged as significant factors for environmentally friendly industrialization.

Double hierarchy hesitant fuzzy linguistic information based framework for personalized ranking of sustainable suppliers

With the growing appetite for reducing carbon footprint, organizations are tirelessly working towards green practices and one such crucial practice is purchasing raw materials from sustainable suppliers (SSs). Inspired by the drift in purchase habits, several sustainable suppliers emerged in the market and a rational selection of a suitable sustainable supplier is a complex decision problem. There are many criteria associated with the evaluation of sustainable suppliers, and double hierarchy hesitant fuzzy linguistic (DHHFL) structure is a popular preference style that accepts complex linguistic expressions in the natural language form. Earlier studies on sustainable supplier selection infer that (i) complex linguistic expressions are not properly modeled, (ii) interrelationship among criteria must be considered during importance assessment, (iii) direct assignment of attitudinal values of experts causes bias and subjectivity, and (iv) nature of criteria play a crucial role in ranking SSs. To overcome these limitations, a novel MCMD framework is proposed in this study in which the attitudinal characteristic values of experts are calculated by using a variance approach. Besides, importance of diverse sustainable criteria is calculated by proposing novel attitude-CRITIC approach that supports proper capturing of interrelationship among criteria along with experts' attitude values. Later, weighted distance approximation algorithm is presented to DHHFL setting for personalized and cumulative ranking of SSs by properly considering nature of criteria. These methods are integrated to form a framework under DHHFL setting, and its usefulness is exemplified by using a case study of SS selection in an automotive firm. A comprehensive sensitivity analysis as well performed to test the validity of the proposed model approves the applicability, validity, and robustness of the model. Lastly, comparison is done with other methods to understand the merits and shortcomings of the proposal.

Optimal Design of Wood/Rice Husk-Waste-Filled PLA Biocomposites Using Integrated CRITIC–MABAC-Based Decision-Making Algorithm

Based on the criteria importance through inter-criteria correlation (CRITIC) and the multi-attributive border approximation area comparison (MABAC), a decision-making algorithm was developed to select the optimal biocomposite material according to several conflicting attributes. Poly(lactic acid) (PLA)-based binary biocomposites containing wood waste and ternary biocomposites containing wood waste/rice husk with an overall additive content of 0, 2.5, 5, 7.5 and 10 wt.% were manufactured and evaluated for physicomechanical and wear properties. For the algorithm, the following performance attributes were considered through testing: the evaluated physical (density, water absorption), mechanical (tensile, flexural, compressive and impact) and sliding wear properties. The water absorption and strength properties were found to be the highest for unfilled PLA, while modulus performance remained the highest for 10 wt.% rice husk/wood-waste-added PLA biocomposites. The density of PLA biocomposites increased as rice husk increased, while it decreased as wood waste increased. The lowest and highest density values were recorded for 10 wt.% wood waste and rice husk/wood-waste-containing PLA biocomposites, respectively. The lowest wear was exhibited by the 5 wt.% rice husk/wood-waste-loaded PLA biocomposite. The experimental results were composition dependent and devoid of any discernible trend. Consequently, prioritizing the performance of PLA biocomposites to choose the best one among a collection of alternatives became challenging. Therefore, a decision-making algorithm, called CRITIC–MABAC, was used to select the optimal composition. The importance of attributes was determined by assigning weight using the CRITIC method, while the MABAC method was employed to assess the complete ranking of the biocomposites. The results achieved from the hybrid CRITIC–MABAC approach demonstrated that the 7.5 wt.% wood-waste-added PLA biocomposite exhibited the optimal physicomechanical and wear properties.

Enhanced Fuzzy Delphi Method in Forecasting and Decision-Making

The Delphi method is a process where subjective data are transformed into quasi-objective data using statistical analysis and are converged to stable points. The Delphi method was developed by the RAND Corporation at Santa Monica, California, and is widely used for long-range forecasting in management science. It is a method by which the subjective data of experts are made to converge using some statistical analyses. This article proposes a variation of the Delphi method using triangular fuzzy numbers, where the communication method with the experts is the same, but the estimation procedure is different. The utility of the method is illustrated by a numerical example.

Hesitant Fuzzy SWARA-Complex Proportional Assessment Approach for Sustainable Supplier Selection (HF-SWARA-COPRAS)

The selection of sustainable supplier is an extremely important for sustainable supply chain management (SSCM). The assessment process of sustainable supplier selection is a complicated task for decision experts due to involvement of several qualitative and quantitative criteria. As the uncertainty is commonly occurred in sustainable supplier selection problem and hesitant fuzzy set (HFS), an improvement of Fuzzy Set (FS), has been proved as one of the efficient and superior ways to express the uncertain information arisen in practical problems. The present study proposes a novel framework based on COPRAS (Complex Proportional Assessment) method and SWARA (Step-wise Weight Assessment Ratio Analysis) approach to evaluate and select the desirable sustainable supplier within the HFSs context. In the proposed method, an extended SWARA method is employed for determining the criteria weights based on experts’ preferences. Next, to illustrate the efficiency and practicability of the proposed methodology, an empirical case study of sustainable supplier selection problem is taken under Hesitant Fuzzy (HF) environment. Further, sensitivity analysis is performed to check the stability of the presented methodology. At last, a comparison with existing methods is conducted to verify the strength of the obtained result. The final outcomes confirm that the developed framework is more consistent and powerful than other existing approaches.

A Border Approximation Area Approach Considering Bipolar Neutrosophic Linguistic Variable for Sustainable Energy Selection

In the last few decades, the computational methods under Multi-Criteria Decision-Making (MCDM) have experienced significant growth in research interests from various scientific communities. Multi-Attributive Border Approximation area Comparison (MABAC) is one of the MCDM methods where its computation procedures are based on distances and areas, and able to express a complex decision systematically. Previous literature have suggested the combination of MABAC with fuzzy sets, in which this combination is used to solve problems that are characterized by uncertain and incomplete information. Differently from the fuzzy MABAC, which directly used single membership, this paper proposes bipolar neutrosophic MABAC of which the positive and negative of truth, indeterminate and false memberships of bipolar neutrosophic set are introduced to enhance decision in sustainable energy selection. Fourteen criteria and seven alternatives of sustainable energy are the main MCDM structures that need to be solved using the proposed method. A group of experts were invited to provide rating of performance values of criteria and alternatives of sustainable energy problem using a bipolar neutrosophic linguistic scale. The distances of alternatives from the Border Approximation Area of bipolar neutrosophic MABAC are the main output of the proposed method prior to making the final decision. The computational results show that ‘Biomass’ is the optimal alternative to sustainable energy selection. Comparable results are also presented to check the consistency of the proposed method.

A New Integrated Multi-Criteria Decision Making and Multi-Objective Programming Model for Sustainable Supplier Selection and Order Allocation

With the increasing pressure from global competition, manufacturers have realized that sustainable production is significant in supply chain management. Sustainable supplier selection and order allocation (SSS&OA) play a distinct and critical role for organizations to achieve sustainable development and build competitive advantage. In this paper, we aim to develop a novel SSS&OA model for selecting the most suitable sustainable suppliers and determining the optimal order sizes among them. First, double hierarchy hesitant linguistic term sets (DHHLTSs) are adopted to deal with uncertainty in evaluating the sustainable performance of alternative suppliers. Then, an extended decision field theory is proposed to choose efficient sustainable suppliers dynamically. Considering quantity discount, a multi-objective linear programming (MOLP) model is established to allocate reasonable order quantities among the selected suppliers. Finally, the applicability and effectiveness of the developed model are illustrated through its application in the electronic industry and through a comparative analysis with other methods.