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With the advancement of global sustainable development goals, promoting sustainable supply chain management has become the key to enhance the competitiveness of enterprises. However, it is difficult for the existing management methods to deal with the balance between cost, efficiency and environmental impact, and the complexity of decision-making under uncertain conditions has increased significantly. Therefore, a mixed integer programming model based on the branch delimitation method and relaxation variables is proposed to transform the enterprise management problem of the sustainable supply chain into a mathematical programming model. With the goal of minimizing the total cost, the effective search is carried out through boundary conditions and priority queues, and the relaxation variable technique is used to reduce the complexity of the problem and ensure that the model can still obtain a feasible solution when considering the uncertainty factors. The results showed that the hybrid integer programming model had the smallest annual discount cost, the largest number of item searches, and an average processing time of less than 2 s, which effectively solved the problem between supplier selection and order allocation in the supply network, and showed good algorithm efficiency and application efficiency. The model can provide reference guidance for enterprise management decision-making and provide a reference value for the improvement of enterprise environmental, social and economic performance.

In today's increasingly competitive business world, selecting and evaluating the suppliers is one of the most important activities of a company. In the Taiwanese TV industry, selecting optimal suppliers of programs is also vital. But few attempts have so far been made to address this specific point. After reviewing the literature, we collect criteria for the selection of a program supplier. Considering the interdependence among the selection criteria, the analytic network process (ANP) is applied to help Taiwanese TV companies to effectively select optimal program suppliers. To avoid the complicated computing process from additional pairwise comparisons of the ANP, we retain the 12 critical criteria according to the opinions of the 44 senior executives about the importance of criteria: Quality, On-time, Marketability, Reputation, Rating, Finance, Relationship, Attitude, Communication, Creativity, Price and Time. Subsequently, we discuss with senior executives to group these criteria into 4 perspectives to structure the hierarchy for the selection of program suppliers. According to the hierarchy based on 4 perspectives and 12 vital criteria, Taiwanese TV companies could select optimal program suppliers more effectively. Moreover, the practical application of the ANP presented in Section 4 is generic and also suitable to be exploited for Taiwanese TV companies.

A novel decision support framework has been proposed herein to solve supplier selection problems by considering green as well as resiliency criteria, simultaneously. In this work subjectivity of evaluation criteria has been tackled by exploring fuzzy set theory. A dominance based approach has been conceptualized which is basically a simplified version of TODIM. Application potential of the proposed dominance based fuzzy decision making approach has been compared to that of fuzzy-TOPSIS, fuzzy-VIKOR and also fuzzy-TODIM. The concept of a unique performance index, i.e. “g-resilient” index has been introduced here to help in assessing suppliers’ performance and thereby selecting the best candidate. The work has also been extended to identify the areas in which suppliers are lagging; these seek further improvement towards g-resilient suppliers’ performance to be boosted up to the desired level.

As a basic part of organizations’ logistics management, purchasing function has supplier selection as one of its main responsibilities. One of the main objectives a buyer follows in supplier selection is to determine optimal quota to be allocated to each supplier. How to allocate orders to different suppliers is as important task as it may affect efficiency of the whole chain. Also, due to variations in real-world business environment, order allocation process is always associated with uncertainties that make it complicated. Therefore, a three-stage integrated framework with environmental uncertainties considered is proposed to allocate orders; this framework can determine qualified suppliers to whom it assigns optimal quota. Considering multi-period purchases and uncertainties, this framework presents a multi-objective nonlinear programming model to determine optimal quota to be allocated to each qualified supplier within each specified period. In order to have the order allocation process closer to real-world cases while increasing the reliability of the obtained solutions, time value of money, inflation, transportation modes, supplier’s profit, and pricing strategy are considered in this model. According to uncertain structure of the proposed model, a solution strategy is proposed to convert this model into a single-objective deterministic model. Then, the resulted single-objective deterministic model is solved by proposing three evolutionary metaheuristic algorithms based on cuckoo optimization algorithm and imperialist competitive algorithm. Finally, a sample problem is presented together with some statistical tests and sensitivity analyses to assess and examine the proposed framework.

Multiple Criteria Decision Making (MCDM) aims at giving people a knowledge recommendation concerning a set of objects evaluated from multiple preference-ordered attributes. The Superiority and Inferiority Ranking (SIR) is a generation of the well-known outranking approach-PROMETHEE, which is an efficient approach for MCDM. As the traditional MCDM approach, however, it faces the obstacle in handling uncertainties of real world. We are concerned about the issue on how to extend the traditional MCDM approach for applications in uncertain environments. This paper proposes a new Intuitionistic Fuzzy SIR (IF-SIR for short) approach and focuses on its application to supplier selection which is the important activity in supply chain management. Toward practical applications, two factors are considered here: (1) multiple decision makers and (2) decision information in the form of linguistic terms. We firstly identify these terms via Intuitionistic Fuzzy Set (IFS) which is proven to be a powerful mathematical tool in modeling uncertain information. Then, we provide the IF-SIR approach for group aggregation and decision analysis. Hereinto, a rule-based method is developed for ranking and selection of suppliers. Finally, an illustrative example is used for illustration of the proposed approach.

In this paper, we concentrate on a class of linear multi-objective decision making models with level-2 TFNwTFC coefficients. Firstly, we define a level-2 triangular fuzzy number with a triangular fuzzy centre(TFNwTFC) based on level-2 fuzzy set theory, and propose a theorem on how to transform it into an (α, β)-level trapezoidal fuzzy number(TrFN). Secondly, in order to deal with the linear multi-objective decision making model with TrFN coefficients, we present an expected multi-objective model with chance constraints, and discuss its equivalent model. Then the ε-constraint method is employed to determine a solution to the crisp equivalent model. Finally, a supplier selection problem is used to illustrate the proposed models and approaches.

Supplier selection is a practical problem in supply chain management and quality is the most important criterion in supplier selection. In this study, we developed a supplier selection model based on process quality, in which the Six Sigma quality index $Q_{pk}$ is used as a tool to assess the process quality provided by suppliers. Note that index estimation based on sample data is prone to uncertainty in the assessment of process quality. Therefore, we derived the confidence interval of $Q_{pk}$ via mathematical programming to reduce the likelihood of assessment miscalculations, and then used this interval to perform a pairwise comparison of suppliers. Our goal was to identify criteria that can be used to select the optimal suppliers for long-term collaborations and sustainable partnerships. A case study is also presented to demonstrate the practical implementation of the proposed method.

Today's global business environment, characterized by unprecedented competitive pressures and sophisticated customers that demand speedy solution creates a bigger set of potential suppliers to evaluate and to choose from. To deal with the complexities of the supplier selection process, an integration of Quality Function Deployment (QFD), Analytical Hierarchy Process (AHP) and Preemptive Goal Programming (PGP) techniques is proposed. A QFD matrix is used to display the degree of relationship between each pair of requirement for suppliers and supplier evaluating criterion. This paper employs the AHP first to measure the relative importance weighting for each of the requirements in the QFD process. Secondly, it is used to assess the evaluating score for each of the candidate suppliers for each particular supplier-evaluating criterion. PGP is built to deal with some suppliers' constraints such that the total value of purchase (TVP) becomes maximum and the total cost of purchase (TCP) minimum.

This paper, using goal programming (GP) and the analytic hierarchy process (AHP), proposes an integrated methodology to aid decision makers in (1) evaluating, screening and selecting best suppliers from among an exhaustive list of available suppliers and (2) determining the amount to be purchased from the selected suppliers. Along the supply chain, the suppliers, by being situated at the upstream of the chain, play a crucial role in successful management of the entire (subsequent) members of the chain and can have a significant impact on the efficiency and effectiveness of the activities of the rest of the chain, and ultimately, on the delivery of the desired products/services. Thus supplier selection can certainly contribute greatly to a firm's competitive advantage and its organizational success. This study suggests a screening and evaluation method, named supplier priority index matrix, to eliminate the least qualified suppliers and to choose the most promising ones. The remaining potential suppliers are evaluated and the best are selected from among them. Finally the AHP and GP are used to allocate the firm's total supplies among the chosen/selected suppliers.

Companies aim to make their supply chains more efficient by using modern information technology (IT). Owing to limited resources there is a need to prioritize IT projects and those generating the highest business value should be implemented first. In this paper, we first develop the theoretical foundations for the prioritization decision and then implement the theoretical principles by applying the ANP (analytic network process) approach. A model is developed to aid a buyer company in selecting prioritized suppliers for adopting electronic invoicing. The case company is a leading Finnish textile and clothing design company. The ANP approach is considered suitable for this type of decision problem with many interconnected decision-making criteria. As the result of the analysis, the prioritized suppliers, representing different supplier types in the case company, are determined. The results can be applied to schedule e-invoicing implementation projects among the supplier companies. Based on the case study it can be concluded that the theoretical principles and the ANP approach can also be applied to other IT implementation decisions concerning supply chain efficiency.

Against the backdrop of responsible economic development, sustainable supply chain management (SSCM) is key to achieving the sustainable development for enterprise and industry. In this regard, sustainable supplier selection is crucial in SSCM. By integrating the three dimensions of sustainability, economic, environmental and social, this paper presents a new evaluation system for supplier selection from a sustainability perspective. Specifically, we design a decision mechanism for sustainable supplier selection based on linguistic 2-tuple grey correlation degree. In this proposed mechanism, the hybrid attribute values whereby real numbers, interval numbers and linguistic fuzzy variables coexist are transformed into linguistic 2-tuples. A ranking method based on linguistic 2-tuple grey correlation degree is then presented to rank the suppliers. An application example is presented to highlight the implementation, availability and feasibility of the proposed decision making mechanism.

This paper presents an original integrated procedure to evaluate and select suppliers for purchasing decisions. The procedure exploits the quality function deployment approach to define the suppliers’ characteristics, coupled with the analytic network process to capture the interrelations among the selection criteria and integrated with a benefits, opportunities, costs and risks (BOCR) analysis. As such, the proposed approach is more structured than the existing methods for supplier selection; in particular, it allows simultaneously to take into account the relevant criteria for supplier selection, to capture the situation in which the decision criteria are somehow dependent on one another and to evaluate the positive and negative aspects of the selection process. By using the proposed approach, companies can derive useful information to guide their partner selection process. An extensive case study is reported to show the application of the model to a selection process of a real Italian company. The application shows that the model is effective in identifying the most suitable supplier; moreover, a detailed sensitivity analysis highlights that the results of the ranking are very robust against possible changes in the relative importance of the BOCR perspectives.

This paper deals with the hesitancy in decision making. Since the decision makers generally doubt to evaluate the alternatives and the criteria in hesitant situations, the existing methods do not satisfy them. Therefore, hesitant versions of Fuzzy-AHP (HF-AHP) and Fuzzy Axiomatic Design (HF-AD) are introduced in this paper. HF-AHP lets the decision makers to use hesitant fuzzy linguistic terms while performing the pairwise comparisons when they are indecisive. HF-AD is used to define the system and design ranges of the items in a hesitant situation. In addition, a case study is revealed as a numerical example in which the best supplier is selected among six alternatives regarding five criteria. In that case study, both weighted and unweighted versions of HF-AD are used. In the weighted version, HF-AHP is used to determine the weights of the criteria. Furthermore, sensitivity analysis is performed to check the robustness of the decision. Moreover, these proposed techniques are compared with non-hesitant versions. According to the results, the decision makers feel more confident with the hesitant versions. Hence, the primary contributions of this study are to develop HF-AD and HF-AHP which are helpful for the decision makers to express their preferences in hesitant situations.

This study aims to eliminate the subjectivity in the weight assignment process of Modified Kemeny Median Indicator Ranks Accordance (KEMIRA-M) and to remove the need for experts to reach a consensus on determining the criteria weights. Additionally, this study aims to apply KEMIRA-M for four different criteria groups and to prevent some criteria from taking a weight value of “0”, as in other studies using KEMIRA-M. In this context, the weighting procedure of KEMIRA-M is advanced using three different ranking-based weighting methods such as Rank Sum (RS), Rank Exponent (RE) and Rank Reciprocal (RR) to operate Median Priority Components (MPCs) more effectively. Accordingly, to determine which weighting method for which criterion group is more suitable, the selection procedure of KEMIRA-M was applied and alternative rankings were obtained for 81 different weight set combinations. Additionally, MATLAB codes have been used to provide flexibility for the application of the proposed approach in a supplier selection problem selected for a case study.

The supply chain is an important element for the development of all industries. It can improve efficiency and effectiveness of product transfer and information sharing between complex hierarchies of all the tiers. Supplier selection is an important step in the supply chain design. In many existing decision models for supplier selection, only quantitative criteria are considered. However, supplier selection is a multi-objective problem containing quantitative as well as qualitative factors. Hence, this paper attempts to demonstrate the application of the Analytic Hierarchy Process (AHP) to overcome the above-mentioned problem. From an extensive analysis of the results, it is evident that selection of an appropriate supplier would result in improving effectiveness of supply chain.

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.

Supply chain management has seen a wide application since the 1990s in satisfying diversified customer demands. To remain competitive on a global scale, manufacturing companies greatly increased the scope of their outsourcing activities. Consequently, supplier selection has become a highly prioritized activity with major significance to companies. Previous studies of supplier selection show that there are commonly accepted supplier selection criteria. However, there are insufficient studies on the association between the manufacturer's criteria of supplier selection and why it wins orders from its customers. Studies on the differences of supplier selection criteria among manufacturers from different countries are insufficient either. Through empirical study this paper tries to find out the association between manufacturer's criteria in supplier selection and how it wins orders. Considerations of supplier selection criteria in different national background are compared and the consistency of supplier selection criteria and competitive priority is analyzed.

Continuous and effective communication with suppliers can play a very important role in the strength and balance of the supply chain and create competitive advantages for manufacturers. Choosing the right supplier is crucial while choosing the wrong suppliers can affect the financial and operational activities of an organization negatively. In this study, an attempt is made to tackle a fuzzy multi-criteria supplier selection problem of a detergent factory as a case study. Moreover, the objective of the investigation is to put forth a model within a fully ambiguous setting, as this allows for the model’s practicality in addressing real-world issues and achieving more precise outcomes. This problem is modeled as ranking 5 suppliers according to 15 criteria selected from the literature. For this aim, a stepwise solution methodology consisting of fuzzy DEMATEL, fuzzy QFD, fuzzy TOPSIS, and fuzzy VIKOR approaches is developed for the first time in order to cover all aspects of the problem e.g., the fuzzy DEMATEL is used to select the effective criteria and eliminate the unnecessary ones, the fuzzy QFD is used to determine importance weight of each selected criterion, and the fuzzy TOPSIS and VIKOR approaches are used to rank the suppliers separately. According to the computations made on the data of the case study, the ranking of the suppliers is obtained by both of the methods while some similarities of the obtained rankings are discovered. Consequently, this study introduces a hybrid model that combines highly favorable decision-making methodologies specifically designed for a fully ambiguous environment.

In today’s highly turbulent and competitive environment, the success of the organization depends on the performance of its suppliers. However, supplier selection problems are complex as they involve a large number of criteria and, frequently, some of the criteria cannot be evaluated precisely. Moreover, fluctuations of supplier performances and unknown information always exist in real-world decision-making. It is a complex multiple-criteria decision-making (MCDM) problem as it involves a trade-off among various criteria with vagueness and imprecision and also involves a group of experts with diverse opinion. Therefore, to make more practical decisions, this paper is intended to propose an integrated technique for order preference by similarity to ideal solution (TOPSIS) in fuzzy environment with multi-choice goal programming (MCGP) to handle the supplier assessment and order allocation for a battery manufacturing organization. Using linguistic variables, the decision-makers assess the rating of suppliers as well as the importance of various factors. Linguistic variables are expressed in trapezoidal fuzzy numbers (TrFN). Fuzzy-TOPSIS method is proposed to obtain the rank of suppliers and MCGP method is used to allocate suitable orders to the selected suppliers. A case study is implemented to find the applicability and validity of the proposed model. Finally, sensitivity analysis is performed to observe the effect of weights of criteria on supplier evaluation problem.

Performance measurement of supply chains and supply chain processes is an important area of research that allows for benchmarking and improvement. Among the many methods, data envelopment analysis (DEA) is an interesting and useful quantitative method that is capable of considering multiple key performance indicators simultaneously. DEA has been widely used in the area of supply chain management, which spans sourcing decisions, logistics management, supply chain performance evaluation, and supply chain risk management. In this chapter, we provide an overview of these application areas, highlight major findings, and point to future research directions.