Modelling of Vendor Selection Problem for Radial Drilling Column by Fuzzy Inference Decision Support System

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 682
Modelling of Vendor Selection Problem for Radial Drilling Column by
Fuzzy Inference Decision Support System
Prof. Nital P. Nirmal
Assistant Professor
Department of Production Engineering
Shantilal Shah Engineering College, Bhavnagar, Gujarat
Abstract— Like many complex supply chain problems,
vendor selection problems are not so well defined which can
be handed over completely to computers, whereas many
human characteristics are also essential to the issues. In this
paper attention is given to the fuzzy System helps Vendor
Selection Problem (VSP) for Radial Drilling Column
(RDC). It required expert’s view, conversion it into fuzzy
term, making 8 rule base Model with implementing Fuzzy
System using MATLAB. At ending point, conclusions and
likely areas of Fuzzy in selecting vendors are present.
Key words: Vendor Selection, Fuzzy Inference System,
Decision Support System
I. INTRODUCTION
The vendor selection process has undergone significant
changes during the past twenty years. These include
increased quality guidelines, improved computer
communications, and increased technical capabilities,
essential changes in the vendor selection process. Here, the
tabular form of literature survey on VSP.
Approach Research work
Linear
Weighting
Method
Vendor selection decision is the most
common way of rating different vendors
on the performance criteria for their quota
allocations [1].
Selection
Criteria
Proposed multiple criteria vendor service
factor ratings and an overall vendor
performance index reference number, as
in [2].
LP (Linear
Programming
Method)
Formulated the vendor selection decision
as a LP problem to minimize the total
purchasing and storage costs [3, 4, 5, 6].
LP Model
A single item LP model to minimize the
aggregate price under constraints of
quality; service level and lead-time [7].
MIP (Mix
Integer
Programming)
MIP approach with the objective of
minimizing purchasing, inventory and
transportation related costs without any
specific mathematical formulation and
demonstrated it through selecting the
vendors [8].
GP ( Goal
Programming)
Also proposed the use of GP for price,
quality and delivery objectives [9].
DSS
By integrating the analytical hierarchy
process with linear programming [10].
Five-stage model for vendor evaluation
[11].
VSP
Fuzzy mixed integer goal programming
[12].
Presented a data envelopment analysis
method for a VSP with multiple objectives
[13].
Used the analytical hierarchical process to
generate weights for VSP reference
number, as in [14], [15].
Table. 1: Literature survey on VSP
II. FUZZY INFERENCE DECISION SUPPORT SYSTEM
It requires conversion of expert’s view, Fuzzification, Rule
Construction, Defuzzification in at the end surface views for
DSS [16].
A. Fuzzy Ranges (Converted Through Experts View)
Na
me
of
the
Ven
dor
Quality of Material Delivery of Material
Pri
ce
Expe
rt-1
Expe
rt-2
Expe
rt-3
A
V
G
Ex
p-1
E-
2
E-
3
A
V
G
A
V
G
V-1
(0.9-
1)
(0.7-
0.9)
(0.3-
0.8)
(0.
3-
1)
(0.
9-
1)
(0.
7-
0.
9)
(0.
3-
0.
8)
(0.
3-
1)
(0.
3-
1)
V-2
(0.2-
0.7)
(0.1-
0.3)
(0-
0.3)
(0-
0.7
)
(0.
2-
0.7
)
(0.
1-
0.
3)
(0-
0.
3)
(0-
0.7
)
(0-
0.7
)
Table 2: Fuzzy conversion of experts view for RDC
B. Fuzzification And The Membership Functions
Figure 1 and 2 show the Fuzzification of QOM and Net-
Rating respectively. This same procedure is repeated for
other two inputs variable “DOM and Price”.

Modelling of Vendor Selection Problem for Radial Drilling Column by Fuzzy Inference Decision Support System
(IJSRD/Vol. 3/Issue 10/2015/146)
Fig. 1: Fuzzification of QOM (RDC)
Fig. 2: Fuzzification of Net- rating (RDC)
C. The Rule Viewer
As shown in Figure 3, the Rule viewer displays the fuzzy
process. Each row of plots corresponds to one rule and each
Column of plots corresponds to either an input variable
(Yellow) or an output variable (Blue). Here, putting the
input (0.5 0.5 0.2).
Fig. 3: Rule Viewer for RDC -Input [0.5 0.5 0.2]
D. The Surface Viewe
The surface view of DOM v/s QOM v/s Net- rating (Price=
0.8), QOM v/s Price v/s Net- rating (DOM= 0.5) and DOM
v/s Price v/s Net- rating (QOM= 0.5) are in figure. 4, 5, 6
respectively.

Fig. 4: 3D - Surface for DOM v/s QOM v/s Net- rating with Price = 0.8 (RDC)
Fig. 5: 3D - Surface for QOM v/s Price v/s Net- rating (RDC) with DOM= 0.5
Fig. 6: 3D- Surface for DOM v/s Price v/s Net- rating (RDC) with QOM = 0.5

III. CONCLUSION & DISCUSSION
Based on the present study following conclusions are made.
1) For selecting the potential vendor one can consider at
least three selection criteria Delivery of Material,
Quality of Material, Price as input variables to a Fuzzy
Inference System (FIS).
2) Experts’ view plays an important role hence they must
be considered before Fuzzification in FIS modelling.
Many factors as suggested by experts may be
considered in arriving at a final decision for a
particular vendor.
3) Fuzzy Inference System may be easily incorporated in
decision making by involving input variables,
membership function to obtain subsequent Net-rating
after Diffuzzification.
4) Fuzzy Inference System offers 3D-Surface view,
which distinguishes potential vendor from others thus,
may be incorporated in selection mechanism.
REFERENCES
[1] Wind, Y. and Robinson, P. The determinants of vendor
selection: The evaluation function approach, Journal of
Purchasing and Materials Management, Fall, 29-41,
1968.
[2] Monozka, R. and Trecha, S. Cost-based supplier
performance evaluation, Journal of Purchasing and
Materials Management, Spring, 2-7, 1988.
[3] Moore, D. and Fearon, H. Computer-assisted decision-
making in purchasing, Journal of Purchasing, 9(4), 5-
25, 1972.
[4] Oliveria, R. and Lourenco, J. A Multi-criteria model
for assigning new orders to service suppliers, European
Journal of Operational Research, 139, 390-399, 2002.
[5] Sharma, D., Benton, W. and Srivastava, R.
Competitive strategy and purchasing decisions, In:
Proceedings of the 1989 International Conference of
the Decision Sciences Institute, 1088-1090, 1989.
[6] Anthony, T. and Buffa, F. Strategic purchase
scheduling, Journal of Purchasing and Materials
Management, Fall, 27-31, 1977.
[7] Pan, A. Allocation of order quantity among suppliers,
Journal of Purchasing and Materials Management, Fall,
36-39, 1989.
[8] Bendor, P., Brown, R., Issac, M. and Shapiro, J.
Improving purchasing productivity at IBM with a
normative decision support system, Interfaces, 15, 106-
115, 1985.
[9] Buffa, F. and Jackson, W. A goal programming model
for purchase planning Journal of Purchasing and
Materials Management, Fall, 27—34, 1983.
[10]Ghodsypour, S. and O'Brien, C. Decision support
system for supplier selection using an integrated
analytic hierarchy process and linear programming,
International Journal of Production Economics, 56-57,
199-212, 1998.
[11]Lau, H., Pang, W. and Wong, W. Methodology for
monitoring supply chain performance: A fuzzy logic
approach, Journal of Logistics Information
Management 15(4), 271-281, 2002.
[12]Kumar, M., Vrat, P. and Shankar, R. A fuzzy goal
programming approach for vendor selection problem in
a supply chain, 70-72, 2003.
[13]Weber, C., Current, J. and Desai, A. An optimization
approaches to determining the number of vendors to
employ, Supply Chain Management: an International
Journal, 2(5), 90—98, 2000.
[14]Narsimhan, R. An analytical approach to supplier
selection, Journal of Purchasing and Materials
Management, Winter, 27-32, 1983.
[15]Handfield, R., Walton, S., Sroufe, R. and Melnyk, S.
Applying environmental criteria to supplier
assessment: A Study in the Application of the
Analytical Hierarchy Process, European Journal of
Operational Research, 141, 70-87, 2002.
[16] N. Nirmal, H. Nirmal and R. Nirmal, “The Role of
Fuzzy for Selecting the Industrial Vendors: Framework
for Development”, Proceeding of National Conference
Emerging Trends in Mechanical Engineering pp 140-
146, 2011

Modelling of Vendor Selection Problem for Radial Drilling Column by Fuzzy Inference Decision Support System

More Related Content

What's hot (20)

Similar to Modelling of Vendor Selection Problem for Radial Drilling Column by Fuzzy Inference Decision Support System (20)

More from IJSRD (20)

Recently uploaded (20)

Modelling of Vendor Selection Problem for Radial Drilling Column by Fuzzy Inference Decision Support System