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Hojung Shin, D. Collier, Darryl Wilson (2000)
Supply management orientation and supplier/buyer performanceJournal of Operations Management, 18
G. Jahanshahloo (2011)
Data Envelopment Analysis with Imprecise Data
Chen-Tung Chen, Ching-Torng Lin, Sue-Fn Huang (2006)
A fuzzy approach for supplier evaluation and selection in supply chain managementInternational Journal of Production Economics, 102
W. Ip, K. Yung, Dingwei Wang (2004)
A branch and bound algorithm for sub-contractor selection in agile manufacturing environmentInternational Journal of Production Economics, 87
篠原 正明 (2002)
William W.Cooper,Lawrence M.Seiford,Kaoru Tone 著, DATA ENVELOPMENT ANALYSIS : A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers, 2000年, 318頁, 47
Y. Smirlis, E. Maragos, D. Despotis (2006)
Data envelopment analysis with missing values: An interval DEA approachAppl. Math. Comput., 177
C. Kahraman, U. Cebeci, Z. Ulukan (2003)
Multi‐criteria supplier selection using fuzzy AHPLogistics Information Management, 16
L. Forker, David Mendez (2001)
An analytical method for benchmarking best peer suppliersInternational Journal of Operations & Production Management, 21
F. Liu, H. Hai (2005)
The voting analytic hierarchy process method for selecting supplierInternational Journal of Production Economics, 97
R. Saen (2007)
A new mathematical approach for suppliers selection: Accounting for non-homogeneity is importantAppl. Math. Comput., 185
C. Weber, J. Current, Anand Desai (2000)
An optimization approach to determining the number of vendors to employSupply Chain Management, 5
C. Sarrico (2001)
Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver SoftwareJournal of the Operational Research Society, 52
R. Vokurka, J. Choobineh, L. Vadi (1996)
A prototype expert system for the evaluation and selection of potential suppliersInternational Journal of Operations & Production Management, 16
N.-E. Dahel (2003)
Vendor selection and order quantity allocation in volume discount environmentsSupply Chain Management, 8
Yingming Wang, R. Greatbanks, Jianbo Yang (2005)
Interval efficiency assessment using data envelopment analysisFuzzy Sets Syst., 153
Ge Wang, Samuel Huang, J. Dismukes (2004)
Product-driven supply chain selection using integrated multi-criteria decision-making methodologyInternational Journal of Production Economics, 91
Manoj Kumar, P. Vrat, R. Shankar (2004)
A fuzzy goal programming approach for vendor selection problem in a supply chainComput. Ind. Eng., 46
K. Choy, W. Lee, V. Lo (2002)
An intelligent supplier management tool for benchmarking suppliers in outsource manufacturingExpert Syst. Appl., 22
R. Ohdar, P. Ray (2004)
Performance measurement and evaluation of suppliers in supply chain: an evolutionary fuzzy‐based approachJournal of Manufacturing Technology Management, 15
Mohamed Youssef, M. Zairi, Bidhu Mohanty (1996)
Supplier selection in an advanced manufacturing technology environment: an optimization model, 3
C. Kwong, W. Ip, Joseph Chan (2002)
Combining scoring method and fuzzy expert systems approach to supplier assessment: a case studyIntegrated Manufacturing Systems, 13
Khurrum Bhutta, F. Huq (2002)
Supplier selection problem: a comparison of the total cost of ownership and analytic hierarchy process approachesSupply Chain Management, 7
Weijun Xia, Zhi-ming Wu (2007)
Supplier selection with multiple criteria in volume discount environmentsOmega-international Journal of Management Science, 35
Anthony Ross, Cornelia Dröge (2002)
An integrated benchmarking approach to distribution center performance using DEA modelingJournal of Operations Management, 20
Sheng-Lin Chang, Reay-Chen Wang, S. Wang (2006)
Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycleInternational Journal of Production Economics, 100
Chun-Wei Lin, Hong-Yi Chen (2004)
A fuzzy strategic alliance selection framework for supply chain partnering under limited evaluation resourcesComput. Ind., 55
R. Lasch, Christian Janker (2005)
Supplier selection and controlling using multivariate analysisInternational Journal of Physical Distribution & Logistics Management, 35
S. Talluri, R. Narasimhan (2003)
Vendor evaluation with performance variability: A max-min approachEur. J. Oper. Res., 146
S. Talluri, R. Narasimhan, A. Nair (2006)
Vendor Performance With Supply Risk: A Chance-Constrained DEA ApproachInternational Journal of Production Economics, 100
S. Talluri, R. Baker (2002)
A multi-phase mathematical programming approach for effective supply chain designEur. J. Oper. Res., 141
(2005)
Supplier selection using dual-matrix approach
Jian Liu, Fong-Yuen Ding, Vinod Lall (2000)
Using data envelopment analysis to compare suppliers for supplier selection and performance improvementSupply Chain Management, 5
M. Braglia, A. Petroni (2000)
A quality assurance‐oriented methodology for handling trade‐offs in supplier selectionInternational Journal of Physical Distribution & Logistics Management, 30
S. Ghodsypour, C. O’Brien (2001)
The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraintInternational Journal of Production Economics, 73
F. Çebi, D. Bayraktar (2003)
An integrated approach for supplier selectionLogistics Information Management, 16
C. Weber (1996)
A data envelopment analysis approach to measuring vendor performanceSupply Chain Management, 1
Yannis Hajidimitriou, A. Georgiou (2002)
A goal programming model for partner selection decisions in international joint venturesEur. J. Oper. Res., 138
Birsen Karpak, Erdoǧan Kumcu, Rammohan Kasuganti (2001)
Purchasing materials in the supply chain: managing a multi-objective taskEuropean Journal of Purchasing & Supply Management, 7
(2005)
Supplier selection using dual - matrix approach in a JIT system ”
D. Sha, Z. Che (2006)
Supply chain network design: partner selection and production/distribution planning using a systematic modelJournal of the Operational Research Society, 57
Purpose – The purpose of this paper is to propose a straightforward model for selecting slightly non‐homogeneous vendors. Design/methodology/approach – In this paper the use of the interval data envelopment analysis (DEA) is suggested. The bounds of intervals are constant and can be obtained by various estimation techniques. The interval DEA model provides for the decision making units (DMUs) with missing values a lower and an upper bound of their efficiency score corresponding to their most favorable and unfavorable option. Findings – Employing the proposed method for selecting slightly non‐homogeneous vendors largely reduced practical difficulties for vendor selection. This method does not exclude any vendor from the selection problem. For all the vendors it provides bounds of the efficiency scores depended on the particular data values that the vendors with missing data assign within the intervals so to maximize their efficiency score. Practical implications – The proposed model considers a slightly non‐homogeneous situation for vendor selection. The proposed approach is driven by multiple criteria. The joint consideration of multiple criteria in a slightly non‐homogeneous environment helps managers select vendors using a comprehensive approach that goes beyond just purchase costs. Originality/value – This paper is believed to be the first to discuss the problem of slightly non‐homogeneous vendor selection with respect to interval mathematics.
Journal of Advances in Management Research – Emerald Publishing
Published: Aug 28, 2009
Keywords: Modelling; Vendors; Selection; Data analysis; Decision making
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