Sustainable Supplier Selection Problem Research Articles

An interactive possibilistic programming approach for sustainable supplier selection and inventory management problem in the physical internet

PurposeThis paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.Design/methodology/approachTo address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.FindingsVarious numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.Originality/valueDue to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

An integrated decision-making process for sustainable supplier selection and order allocation in the automotive industry

A sustainable supplier selection problem can be concisely defined as a classical supplier selection problem that incorporates environmental and social criteria to select and monitor suppliers’ performances, creating the sustainable supplier selection and order allocation (SSS&OA) problem. Finding the optimal order allocation to suppliers in the automotive industry is crucial as it can significantly improve the social, environmental, and economic performance of the whole supply chain. Therefore, this research aims to find an optimal order allocation plan for suppliers using an augmented ε-constraint optimization method in the automotive industry while considering all three dimensions of sustainability. Using a previously established comprehensive framework, this paper has adapted the solution approach of an existing bi-objective order allocation model that was used to make sourcing decisions. The results showed a higher total value of sustainable purchasing is achieved by the augmented ε-constraint optimization method compared to the traditional max-min method.

A multi-stage group decision making approach for sustainable supplier selection based on probabilistic linguistic time-ordered incentive operator.

This study proposes a novel multi-stage multi-attribute group decision making method under a probabilistic linguistic environment considering the development state and trend of alternatives. First, the probabilistic linguistic term set (PLTS) is used by decision makers (DMs) to describe qualitative evaluation information. Subsequently, the weights of DMs for different attributes in different periods are determined by the credibility degree, which is combined with the hesitancy degree and the similarity degree. The evaluations of different DMs for alternatives and the evaluations of DMs' intentions to reward or punish are then aggregated. Later, the trend change level and the trend change stability of alternatives are measured through the means of reward and punishment incentives. Additionally, the probabilistic linguistic time-ordered incentive operator is proposed to aggregate the development state evaluation information and development trend evaluation information in different periods, and alternatives are prioritized by the extended TOPSIS method in the probabilistic linguistic environment. Finally, the practical use of the proposed decision framework is validated by using a sustainable supplier selection problem, and the effectiveness and the applicability of the framework are discussed through comparative analysis. The results show that the proposed approach can select suitable sustainable suppliers by considering their development state and trend in multiple stages.

An extended TODIM method for hyperbolic fuzzy environments

Multi-criteria decision-making (MCDM) methods are increasingly being used to solve socio-economic problems; however, many real-world problems are characterised by high complexity and uncertainty. Hyperbolic fuzzy sets (HyFSs), a new extension of fuzzy sets, are able to deal with ambiguity effectively. They are more flexible than other fuzzy sets, and can model uncertainty better. This paper proposes a hybrid MCDM method based on the TODIM (an acronym in Portuguese for interactive and multiple attribute decision-making) approach in a hyperbolic fuzzy environment. One of the main elements of all MCDM methods is the decision matrix, but the construction of this matrix can be a subjective process. To address this issue, we propose a systematic approach based on the Kano model to make the process more objective. More specifically, we build a hyperbolic fuzzy decision matrix based on Kano-clustering of criteria for each alternative. We validate the effectiveness of our method by applying it to a sustainable supplier selection problem in the dairy industry. The results show that the proposed method can effectively deal with uncertainty and make better decisions in complex situations. We also conduct a sensitivity analysis to assess the impact of the decision-maker’s (DM's) psychological behavior on the ranking of the alternatives, and show that the proposed method is robust to these behaviors. The proposed method represents a promising approach to decision-making under conditions of uncertainty; it is easy to understand and implement, and is feasible for real-world applications.

A novel resilient and sustainable supplier selection model based on D-AHP and DEMATEL methods

The natural disasters that cause a sudden violent change in the earth and the disruptions made by humans disrupt operations in the supply chain, causing negative influences such as lost productivity, revenue, competitive advantage, and profitability for the company. Therefore, suppliers need to be capable of being resilient to any disruption and providing sustainable supply. Supplier selection is a multi criteria decision making (MCDM) problem that allows suppliers to evaluate different and often conflicting qualitative and quantitative criteria. Both resilient and sustainable supplier selection problems contain many qualitative criteria. Experts make subjective judgments based on qualitative criteria in the decision-making process. The D number is a method used to find an efficient and feasible solution to uncertain information. In this paper, the DAHP method, which integrates the traditional Analytic Hierarchy Process (AHP) method and the D numbers, is used for the resilient and sustainable supplier selection problem. The Decision Making Trial and Evaluation Laboratory (DEMATEL) method is integrated into DAHP to determine criteria and subcriteria weights. The proposed integrated DAHP-DEMATEL model is implemented in an automobile manufacturing company to demonstrate the effectiveness of the method. The Triantaphyllou method is applied to perform sensitivity analysis. The traditional AHP-DEMATEL hybrid method was applied to the same problem, and sensitivity analysis was performed. The sensitivity analysis results of the hybrid method were compared with the results of the applied AHP-DEMATEL method. It can be said that the proposed model is less affected by subjective evaluations and is a robust method.

Data‐driven distributionally robust supplier selection and order allocation problems considering carbon emissions

AbstractUnder the constraints of uncertainty and carbon emission policies, reasonable and flexible supplier selection and order allocation can minimize the total cost, which is of great significance to the supply chain. We study sustainable supplier selection and order allocation problems with multiple periods, multiple suppliers, and multiple products under a carbon cap‐and‐trade policy. We exploit distributionally robust versions of supplier selection and order allocation problems with an explicit distributional uncertainty set and derive computationally tractable reformulations of the distributionally robust optimization model with chance constraints by cone duality theory and conditional value‐at‐risk measure. Numerical experiments show that the proposed distributionally robust optimization models with nice computational tractability and probability guarantees are superior to the existing distributionally robust chance‐constrained optimization models in terms of cost and risk aversion. Relevant management insights on different carbon emission regulations and the application of distributionally robust optimization models are provided for policymakers and enterprises' decision‐makers based on the derived results.

A Hybrid Multi-Criteria Decision-Making Model Combining DANP with VIKOR for Sustainable Supplier Selection in Electronics Industry

Sustainability in operations and supply chains is becoming more popular among academics and practitioners through Sustainable Supplier Selection (SSS). In addition to balancing economic, social, and environmental factors, the awareness of the United Nations Sustainable Development Goals 2030 has affected the selection of long-term suppliers, ensuring green operations and sustainable supply chains. The criteria for SSS have multiple dimensions and are interdependent; this mimics the real-world scenario rather than assuming independently from an analytic hierarchy process. We use the multi-criteria decision-making (MCDM) model, combining DEMATEL-based on ANP (called DANP) with VIKOR, to solve the SSS problem. The DANP method is used to model and assess the interdependent relationships between criteria. Then, ranking the available alternatives and selecting the best one can be accomplished using the VIKOR method. We consider the electronic manufacturing industry in Taiwan as an empirical case. This study, in addition to selecting the best sustainable supplier, demonstrates the use of influential network relationship maps to analyze and improve the gaps in each dimension and criterion.

Consensus reaching for social network group decision making with ELICIT information: A perspective from the complex network

Consensus reaching is essential in group decision-making (GDM) since it can mitigate conflicts between expert opinions and promotes the further implementation of decision-making results. Meanwhile, interaction between experts commonly occurs within social networks and in practical GDM problems. Therefore, it neecessary to consider the trust relationship between experts and utilize it to facilitate the consensus-reaching process (CRP). However, most existing social network-based GDM studies mainly use local measures (e.g., degree centrality) to determine the importance of experts, which cannot reflect their actual influence on a global topological structure. To address this issue, we propose a novel consensus-reaching strategy from the perspective of complex network analysis. First, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) is adopted to flexibly facilitate the expression of experts’ uncertain evaluations. The hybrid centrality is then defined to determine the influence of experts in the social network by considering both node importance and edge weight. Since experts with greater influence have stronger information propagation capabilities, hybrid centrality is utilized to guide the CRP, which can better reflect information flows in the social network. Additionally, the BWM-CRITIC weighting method is developed to reflect the significance and relationship among criteria. Finally, we verify the effectiveness and superiority of the proposed method by means of a case study on a sustainable supplier selection problem.

Leagile and sustainable supplier selection problem in the Industry 4.0 era: a case study of the medical devices using hybrid multi-criteria decision making tool.

Given the crucial role of the supplier selection problem (SSP) in today’s competitive business environment, the present study investigates the SSP by considering the leagile, sustainability, and Industry 4.0 (I4.0) indicators for the medical devices industry (MDI). In this regard, at the outset, the list of criteria and sub-criteria is provided based on the literature and experts’ opinions. Then, the importance of the indicators is measured utilizing the rough best–worst method (RBWM). In the next step, the potential suppliers are ranked employing the multi-attributive border approximation area comparison (IR-MABAC) method. Due to the crucial role of medical devices during the COVID-19 outbreak, the present work selects a project-based organization in this industry as a case study. The obtained results show that agility and sustainability are the most important criteria, and manufacturing flexibility, cost, reliability, smart factory, and quality are the most important sub-criteria. The main theoretical contributions of this study are considering the leagile, sustainability, and I4.0 criteria in the SSP and employing the hybrid RBWM-IR-MABAC method in this area for the first time. On the other side, The results of this research can help supply chain managers to become more familiar with the sustainability, agility, leanness, and I4.0 criteria in the business environment.

Sustainable supplier selection using HF-DEA-FOCUM-MABAC technique: a case study in the Auto-making industry.

The assessment of sustainable supplier is very significant for supply chain management (SCM). The procedure of sustainable supplier selection (SSS) is a complex process for decision experts (DEs) due to the association of diverse qualitative and quantitative attributes. As the uncertainty is usually ensued in the SSS and hesitant fuzzy set (HFS), an extension of fuzzy set (FS) has been demonstrated as one of the effective ways to treat the uncertain information in realistic problems. The objective of this paper is to propose an integrated hesitant fuzzy–data envelopment analysis (DEA)–full consistency method (FOCUM)–multi attribute border approximation area comparison (MABAC) method called HF-DEA-FUCOM-MABAC framework to assess the multi-attribute decision-making (MADM) problems on HFSs settings. In this line, first, the efficient alternatives are chosen using the DEA method. Second, The FUCOM is used to compute the subjective weight of attributes. Third, The HF-MABAC method is presented to prioritize the alternatives in an MADM problem. In the following, a case study of SSS problem for an Auto-making company is taken to show the practicality and utility of the presented approach. Next, we present a sensitivity investigation with different attribute weights set to observe the steadiness of the presented approach. Finally, we draw attention toward a comparison between presented approach with the extant HF-FOCUM-TOPSIS model to show its advantage and potency as well.

Sürdürülebilir Tedarikçi Seçimi için Aralık Tip-2 Bulanık Kural Tabanlı BWM Yaklaşımı

Fuzzy logic is a theory based on human-specific approximate reasoning. Therefore, fuzzy logic applications can bring simple and more effective solutions to situations that classical methods cannot overcome. The type-1 fuzzy set is a set, which has a continuous (crisp) membership degree to which a membership degree between 0 and 1 is assigned, and is characterised by membership functions. Type-2 fuzzy sets, which have the power to express uncertainty better, are expressed by membership functions, where the membership degrees of each element belonging to that set also specify a fuzzy set.Therefore, type-2 fuzzy sets allow us to include the membership functions uncertainty in fuzzy set theory. Using expert knowledge and using sensitivity of human to reflect the level of the decision maker influence is expressed as a fuzzy rule based system. Recently, it has been seen that fuzzy rules are frequently used together with multi-criteria decision making (MCDM) methods. Again, combining fuzzy rules with type-2 fuzzy numbers is also found. In this study, the Best Worst Method (BWM), one of the MCDM methods, has been integrated with fuzzy rules based interval type-2. The developed hybrid method was defined as Interval Type-2 Fuzzy Rule-Based BWM (IT2 FRB BWM). The proposed hybrid method has an important place when there are alternatives with similar ranking positions. Thus, even if there is a small difference in each alternative, it will show the difference better (more sensitively). This makes the proposed hybrid method forceful and unique.The proposed approach has been applied to a sustainable supplier selection problem comparatively with the BWM. The results show that the IT2 FRB BWM approach is more successful in ordering alternatives than the classical BWM method.

Taguchi Loss Function in Intuitionistic Fuzzy Sets along with Personal Perceptions for the Sustainable Supplier Selection Problem

Sustainability, a new interdisciplinary paradigm, can be defined as a standard in terms of economic, environmental and social awareness of a company. In many applications, theoretical sustainability models considering the importance of three aspects equally differed from models used in practice. A sustainable supplier selection problem generally contains many conflicting information and the imprecise decision makers’ knowledge, and decision makers can judge suppliers based on their first impression. Hence, in this study, a sustainable supplier selection problem of a plastic packaging company in Turkey is taken into account under an expert-based model and a theorical-based model for three scenarios which consider personal perceptions of decision makers. First, an intuitionistic fuzzy set-based method is applied to the problem using two different distance measurement approaches, namely, fuzzy normalized Euclidean distance and the Taguchi loss function, for which an alternative method is proposed. Then, suppliers are ranked and the validity of the results is also checked using the Pearson product–moment correlation coefficient. The results indicate that (i) the personal perception of decision makers has an inevitable impact on results, (ii) the proposed approach can capture the associated uncertainties embedded in decision makers and fuzzy environment, and (iii) there is a disparity between the theory and the reality of sustainability.

A Novel Perspective to Sustainable Supplier Performance Evaluation Problem: A Case Study Based on Determining the Optimum Number of Clusters

In this study, it is aimed to contribute to the development of companies in order to reach the desired values utilizing the cluster features of the suppliers in a sustainable supplier evaluation process and to guide the supplier in this regard. The clustering features acquired at the end of the study will guide the determination of sustainable supplier criteria and potential new suppliers according to the sector and business line in future studies. The first step of the three-stage model is to apply the best–worst method to determine the weights of the criteria. At the second stage, suppliers are clustered together by the k-means algorithm and silhouette index method. At the third stage, suppliers are divided into categories, and suggestion policies for each supplier category are included. Then, sensitivity analysis has been performed to analyze the effects of variations of criteria weights on the result of the proposed model. Finally, to express the integrated method, a real-life sustainable supplier selection problem is solved and suppliers are categorized according to their similarities. Following evaluation, it was seen that the environmental management system and waste management criteria were the most critical criteria in the categorization of suppliers. When its reasons are investigated, sectoral characteristics come into prominence. The results indicate that the proposed model fits well as a multicriteria decision-making tool for sustainable supplier selection and evaluation decisions. Finally, it is predicted that this model will evaluate new suppliers or new products/services to be procured and selected from the set of features.

A Supplier Selection Model Using Alternative Ranking Process by Alternatives’ Stability Scores and the Grey Equilibrium Product

Supply chain management begins with supplier evaluation and selection. The supplier selection deals with various criteria with different contexts which makes it a complex multi-criteria decision-making (MCDM) method. In this paper, a novel MCDM method, called the alternative ranking process by alternatives’ stability scores (ARPASS), is proposed to solve supplier selection problems. ARPASS considers each alternative as a system that is constructed on integrated components. To perform properly, a system requires high integrity and stability. ARPASS utilizes the stability of alternatives as an effective element for ranking the alternatives. The ARPASS is developed in two forms, ARPASS and ARPASS*. The new method utilizes standard deviations and Shannon’s entropy to compute the alternatives’ stabilities. In this paper, in addition to the new MCDM methods, a new method called the grey equilibrium product (GEP) is introduced to convert grey linguistic variables into crisp values, using decision makers’ subjective perceptions and judgments. To highlight and validate the novel methods’ performance, they are applied to two sustainable supplier selection problems. For evaluation of the reliability of ARPASS and ARPASS*, their results were compared with the results of the popular MCDM methods. We compared the methods in terms of calculation time, simplicity, transparency, and information type.

Supplier selection in sustainable supply chains: Using the integrated BWM, fuzzy Shannon entropy, and fuzzy MULTIMOORA methods

With the increasing awareness of environmental protection and social responsibility, sustainable supplier selection (SSS) has been receiving more and more attention. However, traditional methods for selecting sustainable suppliers suffer from three main drawbacks: (i) Few existing studies have considered criteria weightings that have been determined both subjectively and objectively, reasonably and simultaneously. Moreover, among these limited studies, existing methods for combining weightings that have been calculated both subjectively and objectively provide only a poor degree of differentiation. (ii) Although MULTIMOORA method has high potential to solve the complex SSS problem, few researchers have given it much attention. (iii) The current Reference Point Approach within MULTIMOORA is not comprehensive as it only considers the distance between the alternative and the positive ideal point. Together, these drawbacks result in both inefficiency and ineffectiveness in SSS. This paper proposes a novel fuzzy MULTIMOORA-based method for SSS. The proposed method, firstly, obtains weights by both group Best Worst Method (subjective method) and fuzzy Shannon Entropy Method (objective method), respectively. Then the two types of weights are combined by Deviation Maximization method, reasonably and effectively. Finally, the paper introduces an improved fuzzy MULTIMOORA method to rank alternative suppliers, which considers both the minimum distance from the positive ideal point and the maximum distance from the negative ideal point. The practicability and effectiveness of the proposed method is verified in an illustrative application in Company L, a well-known international forklift truck manufacturer in China.

Pythagorean Fuzzy Soft Einstein Ordered Weighted Average Operator in Sustainable Supplier Selection Problem

Pythagorean fuzzy soft set (PFSS) is the most influential and operative extension of the Pythagorean fuzzy set (PFS), which contracts with the parametrized standards of the substitutes. It is also a generalized form of the intuitionistic fuzzy soft set (IFSS) and delivers a well and accurate estimation in the decision-making (DM) procedure. The primary purpose is to prolong and propose ideas related to Einstein’s ordered weighted aggregation operator from fuzzy to PFSS, comforting the condition that the sum of the degrees of membership function and nonmembership function is less than one and the sum of the squares of the degree of membership function and nonmembership function is less than one. We present a novel Pythagorean fuzzy soft Einstein ordered weighted averaging (PFSEOWA) operator based on operational laws for Pythagorean fuzzy soft numbers. Furthermore, some essential properties such as idempotency, boundedness, and homogeneity for the proposed operator have been presented in detail. The choice of a sustainable supplier is also examined as an essential part of sustainable supply chain management (SSCM) and is considered a crucial multiattribute group decision-making (MAGDM) issue. In some MAGDM problems, the relationship between alternatives and uncertain environments will be the main reason for deficient consequences. We have presented a novel aggregation operator for PFSS information to choose sustainable suppliers to cope with those complex issues. The Pythagorean fuzzy soft number (PFSN) helps to represent the obscure information in such real-world perspectives. The priority relationship of PFSS details is beneficial in coping with SSCM. The proposed method’s effectiveness is proved by comparing advantages, effectiveness, and flexibility among the existing studies.

An integrated decision-making approach for sustainable supplier selection in the chemical industry

Due to an increased awareness of ecological protection and resultant stringent legislations, business organizations are highly motivated to improve the sustainable performance of their supply chain in order to achieve sustainable development goals. The chemical industry is a high-risk, high-pollution, and high-efficiency industry, that would benefit from a systematic and sustainability focused evaluation system for supplier selection. Yet, to date, few studies have conducted the necessary in-depth analysis of the characteristics of this industry from the economic, social, and environmental perspectives. Despite the many methods and models that have been proposed to resolve the sustainable supplier selection (SSS) problem, no research has yet considered the different characteristics of each triple bottom line dimension. Accordingly, this paper addresses this problem by proposing a hybrid multi-method and multi-criteria decision-making framework for SSS in the chemical industry. Based on specific characteristics of the chemical industry, this study applies Fuzzy Grey Relational Analysis (FGRA), Failure Mode and Effects Analysis (FMEA), and cloud computing-entropy weight method (EWM) to analyze the economic, social, and environmental dimensions, respectively. Finally, this study integrates the evaluation results of the three dimensions using the Decision-making Trial and Evaluation Laboratory (DEMATEL). The proposed approach and decision-making model can help managers of sustainable supply chains in the chemical industry to choose more sustainable suppliers, respond to market demands quickly, and maintain high competitiveness in the market. An illustrative application of the proposed framework and model is undertaken in one of the biggest Chinese petrochemical companies to verify its practicality and reliability.

Sustainable Supplier Selection and Order Allocation Under Risk and Inflation Condition

The main objective of this paper is to develop an integrated multiobjective and multiperiod mathematical model for sustainable supplier selection and order allocation of an automotive manufacturer. In spite of the vast quantity of research on this area, integrated consideration of risk and inflation in sustainable supplier selection and order allocation problem is less investigated. To fill this gap, this paper aims to develop a multiobjective sustainable supplier selection and order allocation model considering risk and inflation. Particularly, six objective functions of total cost, economic score, environmental score, social score, inflation rate, and risk level along with related constraints are optimized in the model. The developed model is solved using weighted sum approach and augmented ε-constraint method. In addition, a sensitivity analysis has been conducted. The results provide the best sustainable suppliers and the optimum quantity of products to be purchase from each supplier in each period.

A multi-criteria decision framework for sustainable supplier selection and order allocation using multi-objective optimization and fuzzy approach

ABSTRACT With the growing social and environmental concerns, the integration of sustainability in supplier selection and order allocation is of paramount importance. This research presents a holistic multi-phase decision-support framework to solve the sustainable supplier selection and order allocation problem for the multi-echelon supply chain. The framework comprises a multi-objective mixed-integer nonlinear programming mathematical model augmented with fuzzy multi-criteria decision making techniques and forecast demand. The various economic, environmental and social objectives were optimized for a multi-modal transportation network of a multi-echelon supply chain. The results of the mathematical model highlighted the impact of multi-modal transportation on the total cost and total travel time. The results also demonstrated the relationship between the multi-modal transport network and the environmental impact of the supply chain. The proposed multi-phase holistic decision support framework can be used in the comprehensive sustainability-based analysis of supply chains.

Addressing a sustainable supplier selection and order allocation problem by an integrated approach: a case of automobile manufacturing

ABSTRACT This study introduces a novel integration of language entropy weight method (LEWM) and multi-objective programming (MOP) method to address a sustainable supplier selection and order allocation problem. To show the applicability of the proposed method, a real case study of automobile manufacturing is considered. First of all, the indicators of the proposed problem are defined. Then, the LEWM is considered to select the suppliers. To meet the standards of the sustainable development goals and the global supply chains for a case of automobile manufacturing system, an optimization model is established. The proposed model has three objectives including the total cost and the carbon emissions to be minimized while the procurement value to be maximized. These objectives are based on the economic, environmental, and social impacts simultaneously to address the triple bottom lines of sustainability. In addition, the proposed method improves the relationship between the supply chain practitioners and the potential suppliers to achieve the product development capabilities and qualities to achieve the sustainable development goals and the global supply chain trend.