Developing a worst practice DEA model for selecting suppliers in the presence of imprecise data and dual-role factor

Determining the appropriate supplier is a crucial strategic consideration in supply chain management. Worst-practice frontier data envelopment analysis (WPF-DEA) model is one of the new models in data envelopment analysis (DEA). In this paper, the concept of imprecise data envelopment analysis (IDEA) approach and dual-role factor is used to develop WPF-DEA model. Then, the proposed model is applied for supplier selection problem. A numerical example demonstrates the application of the proposed model. Copyright © 2012 Inderscience Enterprises Ltd

Developing a chance-constrained Free Replicability Hull model for supplier selection

Selecting appropriate suppliers has strategic significance for every company. The Free Replicability Hull (FRH) model is one of the models in Data Envelopment Analysis (DEA). In many real world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a Chance-Constrained FRH (CCFRH) model is developed; its deterministic equivalent, which is a nonlinear programme, is also derived. Furthermore, it is shown that the deterministic equivalent of the CCFRH model can be converted into a quadratic programme. In addition, sensitivity analysis of the CCFRH model is discussed with respect to changes in parameters. Finally, a numerical example demonstrates the application of the proposed model. Copyright © 2012 Inderscience Enterprises Ltd

Supplier selection using a new russell model in the presence of undesirable outputs and stochastic data

Supplier selection is one of the significant topics in Supply Chain Management (SCM). One of the techniques tli{dotless}at can be used for selecting suppliers is Data Envelopment Analysis (DEA). In this study, to handle uncertainty in supplier selection problem, a new Russell model in the presence of undesirable outputs and stochastic data is developed. This study proposed a deterministle equivalent of the stochastic model and convert this deterministle problem into a quadratic programming problem. This quadratic programming problem is then solved using algorithms available for this elass of problems. A numerical example is presented to demonstrate the applicability of the proposed approach. © 2012 Asian Network for Scientific Information

Developing a new chance-constrained DEA model for suppliers selection in the presence of undesirable outputs

Supplier selection is a significant and widely studied theme since it has a significant influence on purchasing management in supply chain. Slacksbased measure - undesirable output (SBM-undesirable output) model is one of the new models in data envelopment analysis (DEA). In many real-world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a SBM-undesirable output model is developed to assist the decision makers to determine the most appropriate suppliers in the presence of both undesirable factors and stochastic data, and also its deterministic equivalent which is a non-linear programme is derived. Furthermore, it is shown that the deterministic equivalent of the stochastic SBM-undesirable output model can be converted into a quadratic programme. In addition, sensitivity analysis of the SBM-undesirable output model is discussed with respect to changes on parameters. A case study demonstrates the application of the proposed model. Copyright © 2012 Inderscience Enterprises Ltd

Developing an imprecise-WPF-SBM-undesirable model for supplier selection

Selection of appropriate suppliers is one of the important strategies for enhancing the quality of output of any company, which has a direct effect on the company's reputation. Worst practice frontier-slack-based measure (WPF-SBM) model is one of the new model in data envelopment analysis. In this paper, imprecise data envelopment analysis approach and undesirable outputs are used to develop WPF-SBM model. Then, the proposed model is applied for a supplier selection problem. A numerical example demonstrates the application of the proposed model. Copyright © 2012 Inderscience Enterprises Ltd

Developing a new theory of integer-valued data envelopment analysis for supplier selection in the presence of stochastic data

Supplier selection has a strategic importance for every company. Hybrid integer data is one of the models in data envelopment analysis (DEA). In many real world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a chance-constrained hybrid integer data envelopment analysis (CCHIDEA) model is developed and also its deterministic equivalent which is a nonlinear program is derived. Furthermore, it is shown that the deterministic equivalent of the CCHIDEA model can be converted into a quadratic program. In addition, sensitivity analysis of the CCHIDEA model is discussed with respect to changes on parameters. Finally, a case study demonstrates the application of the proposed model

Developing a nondiscretionary slacks-based measure model for supplier selection in the presence of stochastic data

Supplier selection has a strategic importance for every company. Nondiscretionary Slacks-based Measure (SBM) model is one of the models in Data Envelopment Analysis (DEA). In many real world applications, data are often stochastic. A successful approach to the address uncertainty in data is to replace deterministic data via random variables, leading to Chance-con strained DEA (CCDEA). In this study, the concept of chance-constrained programming approach is used to develop nondiscretionary SBM model in the presence of stochastic data and also its deterministic equivalent which is a nonlinear program is derived. Furthermore, it is shown that the deterministic equivalent of the stochastic nondiscretionary SBM model can be converted into a quadratic program. Finally, a numerical example demonstrates the application of the proposed model. © 2012 Academic Journals Inc

Developing a neutral slacks-based measure for production line selection

Production line selection has a strategic importance for every company. One of the techniques that can be used for selecting production line is data envelopment analysis (DEA). Traditional DEA models such as Charnes-Cooper-Rhodes and Banker-Charnes-Cooper can only measure radial efficiency (weak efficiency). To measure strong efficiency in DEA, Tone (2001) proposed slacks-based measure (SBM). This model deals directly with the input excesses and output shortfalls. The objective of this paper is to propose a neutral SBM for selecting the best production line. A case study demonstrates the application of the proposed model. Copyright © 2012 Inderscience Enterprises Ltd

Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data

The changing economic conditions have challenged many organisations to search for more efficient and effective ways to manage their supply chain. During recent years supplier selection decisions have received considerable attention in the supply chain management literature. There are four major decisions that are related to the supplier selection process: what product or services to order, from which suppliers, in what quantities and in which time periods? Data envelopment analysis (DEA) has been successfully used to select the most efficient supplier(s) in a supply chain. In this study, we introduce a novel supplier selection model using chance-constrained DEA with non-discretionary factors and stochastic data. We propose a deterministic equivalent of the stochastic non-discretionary model and convert this deterministic problem into a quadratic programming problem. This quadratic programming problem is then solved using algorithms available for this class of problems. We perform sensitivity analysis on the proposed non-discretionary model and present a case study to demonstrate the applicability of the proposed approach and to exhibit the efficacy of the procedures and algorithms. Copyright © 2011 Inderscience Enterprises Ltd