Sahar Khoshfetrat

Bio: Sahar Khoshfetrat is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Data envelopment analysis & Analytic hierarchy process. The author has an hindex of 3, co-authored 8 publications receiving 52 citations.

Sustainable supplier selection and order allocation: a fuzzy approach

Abstract: This research develops a fuzzy, multi-objective, multi-product and multi-period mathematical model for sustainable supplier selection and order allocation in the automotive industry. The problem ha...

Sustainable Supplier Selection and Order Allocation Under Risk and Inflation Condition

Abstract: 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 $\varepsilon $ -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.

Deriving Priorities the Alternatives in an Analytic Hierarchy Process

Abstract: The data envelopment analysis (DEA) is a mathematical programming technique, which is used for evaluating relative efficiency of decision making units (DMUs). However, the DEA does not provide more information about the efficient DMUs. Recently, some researchers have been carried out in the background of using DEA models to generate local weights of alternatives from pairwise comparison matrices used in the analytic hierarchy process (AHP). In this paper, an application of a common set of weights is used for determining priorities in the AHP. First, we determine DEA efficient alternatives as DMUs. Then, these alternatives are ranked according to the efficiency score weighted by the common set of weights in the AHP. This application is applied successfully and the result is valid and assured. A numerical example is utilized to illustrate the capability of this procedure.

Introducing a nonlinear programming model and using genetic algorithm to rank the alternatives in analytic hierarchy process

Abstract: As ranking is one of the most important issues in data envelopment analysis (DEA), many researchers have comprehensive studies on the subject and presented different approaches. In some papers, DEA and Analytic hierarchy process (AHP) are integrated to rank the alternatives. AHP utilizes pairwise comparisons between criteria and units, assessed subjectively by the decision maker, to rank the units. In this paper, a nonlinear programming (NLP) model is introduced to derive the true weights for pairwise comparison matrices in AHP. Genetic algorithm (GA) is used in order to solve this model. We use MATLAB software to solve proposed model for ranking the alternatives in AHP. A numerical example is applied to illustrate the proposed model.

Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda

Abstract: Sustainability has emerged as an important industrial strategic outlook expanding beyond organizational boundaries to include the supply chain. Simultaneously, the industry has also been faced with supply chain resilience concerns. Research on the intersection of supply chain sustainability and resilience is nascent and is a consequence of their observed mutual influences. However, confusion about concepts, implementation methods, and measurements of sustainable and resilient supply chains remains. This study completes a systematic literature review that critically examines several major observations and directions. We find the concept of sustainable supply chains is more established, and general agreement on its theoretical foundations exists. Supply chain resilience is relatively less mature. The nexus and relationships between the two topics are often incoherent: there is confusion on sustainable and resilient supply chains establishment; there is no clarity on what practices could jointly advance both areas. A major conflict exists since sustainability generally focuses on efficiency, while resilience seeks effectiveness. We recommend studies to analyze implementation relationships and impact. We also observe that performance measurement systems should be developed to assess supply chain sustainability and resilience performance taking with explicit consideration time horizons considered in these measures.

The State of the Art in Fuzzy Data Envelopment Analysis

Abstract: Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This chapter provides a taxonomy and review of the fuzzy DEA (FDEA) methods. We present a classification scheme with six categories, namely, the tolerance approach, the α-level based approach, the fuzzy ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy random/type-2 fuzzy set. We discuss each classification scheme and group the FDEA papers published in the literature over the past 30 years.