Integrated Supply Chain Analysis and Decision Support

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Nov 30, 2013 (3 years and 6 months ago)

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Integrated Supply Chain Analysis and Decision Support

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Gordon Berkstresser, Trevor J. Little (NC State, Textiles); Shu
-
Cherng Fang, Russell E. King,

Henry L.W. Nuttle, James R. Wilson (NC State, Engineering)


TEAM LEADER: Russell E. King


WEB
SITE: www.ie.ncsu.edu/fangroup/NTCpage/NTCI98S1.html


PROJECT GOALS


A softgoods supply chain involves the activity and interaction of many entities. Usually each of these
entities knows how to make locally optimal decisions when the situation is clear. Un
fortunately many
decisions must be made in settings involving vagueness and uncertainty. Furthermore successful

supply chain operation requires coordination of the decisions of the individual entities while the level of
uncertainty is amplified as informa
tion is passed through the chain. Even in the emerging data rich
environment with current information technology (EDI, Internet, data mining), lack of fundamental
knowledge about supply chain operation in a vague and uncertain environment is still a key pr
oblem
faced by the industry. The goals of this project are to explore and demonstrate the use of fuzzy
mathematics, neural networks, and other soft computing technologies in addressing critical softgoods
supply chain integration and decision support proble
ms. The research is intended to enhance the
capability of the U.S. softgoods industry to be globally competitive.


ABSTRACT


The effort in this project has been directed to learning the state
-
of
-
the
-
art supply chain management
technology, creating and demo
nstrating a fuzzy and other soft computing based approaches to capacity
allocation, delivery date assignment, and creating and demonstrating a fuzzy
-
neural soft computing
framework for supply chain modeling and optimization. The latter has required the dev
elopment of fuzzy
system identification procedures, a method for constructing membership functions for fuzzy sets, and a
flexible supply chain simulation capability. We also developed and tested a method for generating
confidence intervals on outputs fro
m neural network decision surface models.


I. BACKGROUND AND PROJECT OBJECTIVES


Understanding capacity/cost tradeoffs and coordinated operation of a softgoods supply chain operating in
a vague and uncertain environment is essential for success in the hig
hly competitive global market.


To date there has been no rigorous theoretical treatment of supply chain operation in vague and uncertain
environments. Nor are there reliable, fully disclosed, science
-
based decision support tools. Existing
approaches for
coordinating the activities in a supply chain require the specification of precise quantities

such as capacity levels and customers' desired delivery dates. However the true nature of the problem
involves data and objectives which are often vague and impr
ecise.


For example, many customers of an apparel manufacturer will be able to tolerate delivery somewhat later
than their nominal order due
-
date. Thus order due
-
dates are somewhat flexible (vague). The manufacturer
has a "fuzzy capacity" in that there ar
e options to schedule overtime, subcontract locally, or even go
offshore. Management wants a “high” level of service but at the same time “low” inventories.

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In order to make good decisions the apparel manufacturer needs to coordinate local activities with

those
of upstream suppliers and downstream customers
-

with uncertainty and imprecision present on all fronts.
Other entities in the chain are faced with a similar problem. The coordination of numerous activities,

particularly when different firms are inv
olved, requires negotiation and compromise. This requires an
approach which can be flexible enough to accommodate imprecise linguistic data as well as precise
numerical data and which yields solutions that will provide compromise among different parties'
o
bjectives.


To provide intelligent, responsive knowledge for decision support which can accommodate these
characteristics, we are using fuzzy mathematics, neural networks, genetic algorithms, and other efficient
soft computing methodologies.


In spite of t
he name, fuzzy mathematics is a rigorous discipline, more general than standard mathematics.
In the past fuzzy mathematics has been used mainly for the control of machinery and processes while
neural networks have been used primarily for pattern recognitio
n and prediction. In this project we are
bringing fuzzy mathematics and neural network technology into the arena of knowledge extraction and
application for optimal decision making in a setting which involves the coordination among various
entities.


This

study will also provide the fundamental knowledge necessary to, for example, carry out coordinated
capacity allocation, scheduling, and delivery date assignment in a supply chain operating in a vague and
uncertain environment, incorporating the negotiatio
n and compromise that naturally exists in a

cooperative venture.


Specific objectives of the project include:


1.

Development of models of information and material flow between entities in a softgoods supply chain
in the framework of fuzzy mathematics and n
eural networks.


2.

Development of mathematical models for specific capacity allocation, scheduling, and delivery date
assignment scenarios involving both linguistic and numerical data.


3.

Development of soft computing approaches for supply chain design and opt
imization.


4.

Development of prototype decision support systems to demonstrate (2) and (3).


5. Enhancement of the Neural network based CEO decision surface modeling capability developed in an


earlier project.



II. ACCOMPLISHMENTS TO DATE


We have

developed and prototyped a soft computing framework for supply chain modeling and
optimization. In conjunction with this activity, we have created a flexible supply chain simulation
capability, developed an efficient approach for constructing the membersh
ip functions needed to model
imprecise quantities with fuzzy sets and developed and tested new procedures for knowledge extraction
from operational data. We have conceived and prototyped several versions of decision support tools for
interactive due
-
date n
egotiation. Our earlier neural network based decision surface modeling tool is now
included in Version 2.0 of the DAMA project's Sourcing Simulator distributed by [TC]
2
. We have also
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developed and tested "jackknifing" techniques to determine confidence in
tervals on the decision surface
models (See annual report for 1998).


1. Supply Chain Modeling and Optimization Using Soft Computing Based Simulation


The term "supply chain" has been used since the 1980s to describe the whole spectrum of operations in
al
most every manufacturing industry; from purchasing of raw material, through transformation production
processes, to distribution of the finished inventory to customers. As the complexity increases a supply
chain is well depicted as a network of suppliers,
manufacturers and customers. In the softgoods industry
the overall supply chain includes fiber, textile, cut and sew, retail, and consumer.


In order to provide a vehicle for softgoods supply chain modeling, analysis, and optimization
incorporating the unc
ertainty and imprecision inherent in real systems, we are developing a soft
computing guided simulation system.


While simulation can help the decision maker to understand better the supply chain, many possible
combinations and lines of action are possible

to improve the whole system. It is typical that the simulation
analysts and experts have to spend a considerable amount of time trying to change the original system
searching for a good design and balancing several conflicting objectives simultaneously. T
his trial and
error procedure can be avoided by coupling soft computing (i.e., fuzzy logic, evolutionary programs and
neural networks) with the simulation of the supply chain
.


A schematic of the soft computing guided simulation approach is given in Figur
e 1.


Supply Chain
Configuration
Simulation
Activate Fuzzy
Rules/Logic
Goals met?
Stop
Input
-
Performance
Data
Fuzzy
System / Relationship
Identification
Knowledge
Extraction
Soft Computing
Guided Simulation
Supply Chain
Configuration
Simulation
Activate Fuzzy
Rules/Logic
Goals met?
Stop
Input
-
Performance
Data
Fuzzy
System / Relationship
Identification
Knowledge
Extraction
Soft Computing
Guided Simulation


Figure 1. Soft computing guided simulation system


The system has two major components, the soft computing guided simulation procedure (on left) and a
knowledge extraction procedure (on right). The simulation procedure is executed iteratively, be
ginning
with a supply chain structure (manufacturers, suppliers, customers, etc.), a specific set of operational
parameter settings (inventory levels, production capacities, lead
-
times, etc.) and specific management
goals (such as “we want customer service

to be HIGH and inventories to be LOW”). The operation of the
system is simulated for a period of time and performance measures calculated. Observed performance is
then compared with stated goals. If the supply chain objectives are not yet achieved, the sy
stem will
check with its fuzzy knowledge base having its latest system performance measures on hand. After this
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dialog, fuzzy rules contained in the knowledge base will be activated to adjust parameters in the
simulation model. This process is repeated unt
il the system objectives are met to a high degree.


The knowledge extraction procedure is used to create the initial rule base and/or revise a current rule base
based on observed simulation results.


Three of the key components of the overall system are th
e simulator, the fuzzy system /relationship

identification procedure, and a mechanism for constructing fuzzy set membership functions. These are
described in more detail in the next three sections.


To test the validity of our approach, a simulation model

for a simple four
-
stage supply chain such as that
illustrated in Figure 2 was created. Each stage of the chain has parallel processing units and limited
inventory buffer capacity. The controllable parameters are the number of processing units and buffer
c
apacity at each stage.






Order queue
Order queue
Order queue
Order queue
Cutting
Sewing
Pressing
Packaging
Shipping




Order queue
Order queue
Order queue
Order queue
Cutting
Sewing
Pressing
Packaging
Shipping

Figure 2. Simple four
-
stage supply chain


0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
1
2
3
4
5
6
7
8
9
10
Iteration
Membership in HIGH customer
service

Figure 3. Control path for overall work
-
in
-
process


The graph in Figure 3 illustrates how the soft computing guided simulation system is able to quickly
adjus
t supply chain parameters to obtain settings yielding a HIGH customer service level in very few
iterations.


2
.
Supply Chain Simulator


In order to quickly create a flexible supply chain simulation capability, we have developed a interactive
simulator wri
tten in C++ with a Visual Basic interface. Figure 4 illustrates the interface.

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Figure 4. Supply chain simulator interface


With this simulator a user can specify a supply chain structure (entities and interactions), demand
characteristics, inventory
control and reorder policies, production capacities, order lead
-
time and fill
-
rate
characteristics, and various cost parameters. From here the fuzzy rule base and simulation are executed
iteratively until specified performance levels are achieved to a high

degree.


During the last year have enhanced the simulator with a more flexible interface and to include products
with arbitrary bills of materials. The simulator is also proving useful in ongoing research with the
furniture industry.


3.

Knowledge Extract
ion from Simulated Operational Data


To identify underlying system dynamics in order to generate (or modify) the fuzzy rules used to guide the
operational parameter adjustment, we have developed and tested new methods for extracting knowledge
from input
-
pe
rformance data from an (in this case simulated) operational system.


"System identification" involves identifying that model within a class which may be regarded as
equivalent to a target operational system with respect to input
-
performance data pairs. The

identified
model can then be used to explain and modify the behavior of the target system. In our case the target
system is the (hopefully small) set of rules which will enable rapid operational parameter adjustment in
the simulated supply chain to provid
e a high level of satisfaction of stated performance goals.


Our first approach consists of two phases. The first phase provides a baseline fuzzy model of the
operational system. This is implemented by integrating the subtractive clustering method with the

fuzzy
c
-
means clustering algorithm. The second phase uses steepest descent and recursive least
-
squares
estimation methods to fine tune the parameters of the baseline design to provide a better match with the
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target system. This approach has been able to s
uccessfully identify small sets of rules which provide a
high level of performance on test problems from the literature.


Since Phase 2 turned out to be computationally slower than hoped, we examined an alternative clustering
approach in for Phase 1 with t
he objective of reducing the required Phase 2 effort. This proved quite
successful. In fact with the same test problems the second phase was not required at all in order to achieve
comparable performance.


In addition to using fuzzy clustering methods we
have done some preliminary work in applying neural
networks to provide the fuzzy rule base. Application to the same test problems suggests that the neural
network approach can provide rules with a higher level of performance based on small amounts of
train
ing data. However, with additional data, the clustering
-
based approach’s performance rises to exceed
that of the neural network approach.



4. Membership Function Construction


In our system in Figure 1, imprecise concepts such as HIGH customer service and

LOW work
-
in
-
process
inventory level are modeled as fuzzy sets. Figure 5 illustrates a possible fuzzy set representation of
MEDIUM machine utilization. In this case, utilization levels around 50% are regarded as definitely
“medium” and thus have membership

values at or close to 1. On the other hand utilization levels below
10% and above 75% are definitely not “medium” and thus have membership values of 0. Points in
between have memberships which rise toward 1 the closer they are to 50%.

0
0.2
0.4
0.6
0.8
1
0
20
40
60
80
Utilization (%)
Membership in MEDIUM machine
utilization

Figure 5. Membership function for MEDIUM machine utilization


In current practice, modelers choose the shape of the membership function from a pool of commonly used
parameterized families including triangular, trapezoidal, Gaussian, sigmoid, a
nd S
-
shaped. After a shape
is selected, the parameters are manipulated to tune the shape. In contrast, we have developed an approach
which employees Bezier curves which, with the aid of control points (the black dots in Figure 5), can be
used to produce th
e membership of almost any imprecise concept.


This new flexible and interactive way of building and tuning membership functions can be leveraged by
using a graphical user interface (GUI). The implemented GUI helps the modeler add, move, delete control
po
ints to obtain the desired membership function.



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5. Due
-
Date Negotiation Tools


How to negotiate order due
-
dates which are acceptable to both a manufacturer and its customers is an
important issue in the make
-
to
-
order manufacturing systems, since sales
normally depend on both the cost
and delivery date. Traditionally, a customer negotiates a required due
-
date with a salesperson who relies
on the
sales management

module of a Manufacturing Resource Planning (MRP
-
II) system. However,
since the sales managem
ent module is not normally linked with the
production planning

module of the
MRP
-
II system, the salesperson is not able to get detailed information relative to the availability of
various manufacturing resources. Therefore, in practice, a customer tends to

ask for the earliest possible
due
-
date, and, to get the order, a salesperson tends to promise the customer a due
-
date without adequate
consideration of the availability of production capacity. This often results in tardy deliveries, unhappy
customers, and

low utilization of manufacturing facilities.


Since Supply Chain Management (SCM) first attracted the attention of researchers and managers, a
number of commercial software packages have been developed and implemented in actual manufacturing
enterprises.

Although some packages include functions, such as ATP (Available
-
To
-
Promise) and CTP
(Capacity
-
To
-
Promise) to support the manufacturer's order acceptance/rejection decision, how to support
the negotiation between manufacturers and their customers has not
been properly addressed from either
an academic or practical perspective.


To support such negotiation, we have developed and prototyped two approaches. The first is a due
-
date
bargaining method which uses fuzzy modeling to capture the imprecision inherent

in "shop capacity" and
customer specified due
-
dates. The method also uses a genetic algorithm along with fuzzy logic for
capacity allocation. For testing and demonstration we have implemented the method in a prototype
computer software package which is or
iented to apparel manufacturing enterprises. We call it the “Multi
-
Customer Due
-
Date Bargainer" (See our annual report for 1999).


The second approach provides the manufacturer with greater flexibility in exploring alternatives. A real
-
time due
-
date assign
ment approach is combined with MRP
-
II based on the concept of integrating the due
-
date assignment process with the production planning process. Potential new customer orders are
dynamically inserted into a rough
-
cut capacity plan which details the implied
time

phased work load on
each key resource and the associated estimated order completion dates. First, leaving the plan for
currently active orders undisturbed, earliest possible completion times for new orders that do not overload
production resources ar
e determined. If the resulting estimated completion times satisfy the customers’
requested delivery dates, the order promise dates can be quoted as requested. However, in many instances
some of these estimated completion dates may not meet customers’ requ
irements. In this case the
prototype software allows the manufacturer to determine the impact on the plan selectively scheduling
overtime on one or more resources and/or of forcing the loading of one or more orders to meet specific
delivery dates. Explorin
g a number of options permits the manufacturer to make informed delivery date
quotations. While exploring such alternatives, the loading for selected customer orders can be left
undisturbed.


In the prototype, data on customers, products, orders, bill of m
aterials manufacturing resources (e.g.,
cutting, sewing, pressing, packaging), and shop calendar can be viewed and edited on one of five tabs on
an input form. Then a C++ "DLL" implements the procedure introduced above.


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Figure 6. D
ue
-
Date Negotiator output report


Figure 6 illustrates the screen that is presented to the user upon the completion of a loading run. The due
-
dates in the second column are those requested by the customer while the dates in the last column are
those obtain
ed with the last loading run. The “Schedule Option” section indicates that a another loading
run will be made in an effort to obtain the specified target due dates for orders 3 and 10, and in doing so
the dates for four orders will be fixed at the current
values while those for the remaining three orders may
be adjusted as necessary.


The prototype software is currently being enhanced to function in a wide range of manufacturing process
structures. This tool is also being applied in ongoing research with th
e furniture industry.


III. RESOURCE MANAGEMENT AND TECHNOLOGY TRANSFER


The research team is drawn from the Department of Textile and Apparel Management in the College of
Textiles and from Industrial Engineering and Operations Research in the College of
Engineering bringing
together a wide array of expertise.


Two masters and six doctoral students have participated in the research. Masters theses entitled "Robust
Confidence Interval Estimation for Neural Network Decision Surfaces" and "An Automated Proced
ure
for Input Modeling with Bezier Distributions" and Ph.D. dissertations entitled “A New Approach to
Fuzzy system Identification”and “ Simulation Optimization Using Soft Computing” have been submitted
and approved. One of the students presented a paper
entitled "Multi
-
Customer Due
-
Date Bargaining with
Soft Computing" at the Fourth Joint Conference on Information Sciences in October 1998. Four papers
related to work in this project were presented (August 1999) at and appear in the
Proceedings of the
Eight
h International Fuzzy Systems Association World Congress
. Five papers have been accepted for
publication in scholarly journals while two others are currently under review.


The initial due
-
date negotiation prototype described above was developed in collab
oration with Professor
Dingwei Wang of the Department of Systems Engineering of Northeastern University in P.R. China. Two
joint papers appear in
IEEE Transactions on Man, Machine, and Cybernetics

while a third appears in a
special issue on soft computing
of the
Journal of the Chinese Institute of Industrial Engineering.

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Various aspects of this research has been discussed with and demonstrated to personnel from a number of
companies, including Burlington Industries and Milliken & Company. The technology

developed in this
project is also being used in a project with the U.S. furniture industry.


Relevant research papers authored by the project team are listed below.


OTHER CONTRIBUTORS

Students: Shyh
-
Huei Chen, Hao Cheng, Saowanee Lertworasirikul ,Yi
Liao
, and Andres Medaglia (NC State, Engineering).


FOR FURTHER INFORMATION
:

Donovan, M.E., "An Automated Procedure for Input Modeling with Bezier Distributions," Masters' Thesis,
Dept. of Industrial Engineering, North Carolina State University, Raleigh, NC,
1998.


Donovan, M.E., D
-
W Wang, S
-
C. Fang, H.L.W. Nuttle, and J.R. Wilson, " Multi
-
Customer Due
-
Date
Bargaining with Soft Computing,"
Proceedings of the Fourth Joint Conference on Information Sciences
,
October, 1998.


Guan, S., S
-
C.Fang., D.Wang, and J. S
eyed, "A Fuzzy Mixed Integer Linear Programming Model for
Production and Capacity Planning with Seasonal Demand", Technical Report, Industrial Engineering and
Operations Research, NC State University, 1998.


Hung, T
-
W, “A New Approach to Fuzzy System Ident
ification,” Ph.D. Dissertation, Graduate Program in
Operations Research, North Carolina State University, Raleigh, NC, 1999.


Hung, T
-
W, S
-
C. Fang, and H.L.W. Nuttle, “A Two
-
Phased Approach to Fuzzy System Identification,” under
review by
Fuzzy Sets and Sy
stems
, 1999.


Hung, T
-
W, S
-
C. Fang, and H.L.W. Nuttle, “A Bi
-
Objective Fuzzy C
-
Means Cluster Analysis Approach to
Fuzzy System Identification,” in the
Proceedings of the 8th Bellman Continuum
, December 2000.


Hung, T
-
W, S
-
C Fang, and H.L.W. Nuttle, "A Clu
stering
-
Based Approach to Fuzzy System Identification",
Proceedings of the Eighth International Fuzzy Systems Association World Congress
, Vol.1, 415
-
419, Taipei,
Taiwan, August 1999.


Hung, T
-
W, S
-
C. Fang, H.L.W. Nuttle, and R.E. King, "A Fuzzy
-
Control
-
Ba
sed Quick Response Reorder
Scheme for the Retailing of Seasonal Apparel,"
Proceedings of the 2nd International conference on
Computational Intelligence and Neuroscience,

Vol. 2, 300
-
303, 1997.


Hung, T
-
W, J.R. Wilson, and P. Wu, "Confidence Intervals for E
stimated Decision Surfaces", working paper,
Department of Industrial Engineering, NC State University, 1997.


Medaglia, A.L., S
-
C. Fang and H.L.W. Nuttle, “Fuzzy Controlled Simulation Optimization,” to appear in
Fuzzy Sets and Systems,
2001.


Medaglia, A.L.
, S
-
C. Fang, H.L.W. Nuttle, and J.R.Wilson, “An Efficient, Flexible Mechanism for
Constructing Membership Functions”, to appear in
European Journal of Operations Research
, 2001.


Nuttle, H.L.W., D
-
W Wang, S
-
C. Fang, and S
-
H. Chen, "Multi
-
Customer Due
-
Date

Bargaining with Soft
Computing'",
Proceedings of the Eighth International Fuzzy Systems Association World Congress
, Vol.1, 401
-
404, Taipei, Taiwan, August 1999.


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10


Nuttle, H.L.W., R.E. King, J. A. Wilson, N.A. Hunter, and S
-
C. Fang, "Simulation Modeling of
the Textile
Supply Chain, Part II
-

Results and Research Directions," to appear in
The Journal of the Textile Institute
,2001.


Stringer, F.B., "Robust Confidence Interval Estimation for Neural Network Decision Surfaces," Masters'
Thesis, Graduate Program i
n Operations Research, North Carolina State University, Raleigh, NC, 1998.


Wang, D
-
W., S
-
C. Fang, and H.L.W. Nuttle, Fuzzy Rule Quantification and Its Application in Manufacturing
Systems",
Journal of Chinese Institute of Industrial Engineering

(Special I
ssue on Softcomputing in Industrial
Engineering), Vol. 17, No. 5, 2000.


Wang, D
-
W., S
-
C. Fang, and H.L.W. Nuttle, "Soft Computing for Multi
-
Customer Due
-
Date Bargaining",
IEEE Transactions on Systems, Man, and Cybernetics
, Vol. 29, No.4, 1999.


Wang, D
-
W.
, S
-
C. Fang, and H.L.W. Nuttle, "Fuzzy Rule Quantification and Its Application in Fuzzy Due
-
Date Bargaining",
Proceedings of the Eighth International Fuzzy Systems Association World Congress
, Vol. 1,
377
-
380, Taipei, Taiwan, August 1999.


Wang, D
-
W., S
-
C.
Fang, and T.J. Hodgson, "A Fuzzy Due
-
Date bargainer for Make
-
to
-
Order Manufacturing
Systems,"
IEEE Transactions on Systems, Man, and Cybernetics
, 28, No. 3, 492
-
497, 1998.


Wu, P., "Neural Networks and Fuzzy Control with Applications to Textile Manufacturi
ng and Management",
Ph.D. Dissertation, Graduate Program in Operations Research, North Carolina State University, Raleigh, NC,
1997.


Wu, P., S
-
C. Fang, and H.L.W. Nuttle, "Curved Search Based Neural Network Learning Using Fuzzy
Control",
Proceedings of th
e Eighth International Fuzzy Systems Association World Congress
, Vol.1, 381
-
385,
Taipei, Taiwan, August 1999.


Wu, P., S
-
C. Fang, H.L.W. Nuttle, R.E. King, and J.R. Wilson, "Decision Surface Modeling of Textile
Spinning Operations Using Neural Network Tech
nology," In
Proceedings of the IEEE 1994 Annual Textile,
Fiber, and Film Industry Conference
, Institute of Electrical and Electronics Engineers, Piscataway, NJ, 1994.


Wu, P., S
-
C. Fang, H. L. W. Nuttle, R. E. King, and James R. Wilson, "Guided Neural Netw
ork Learning
Using a Fuzzy Controller with Applications to Textile Spinning,"
International Transactions in Operational
Research,

2, No. 3, 259
-
272, 1995


Wu, P., S.
-
C. Fang, H.L.W. Nuttle, and R.E. King, "Decision Surface Modeling of Textile Retail Operat
ions
Using Neural Networks," In
Proceedings of the Third Annual Fuzzy Theory and Technology

International Conference,

Duke University, Durham, NC, 312
-
315, 1994.


Wu, P., S.
-
C. Fang, H.L.W. Nuttle, and R.E. King, "Decision Surface Modeling of Apparel Retai
l Operations
Using Neural Network Technology,"
International Journal of Operations and Quantitative

Management
, 1, No. 1, 33
-
48, 1995.


Wu, P., S
-
C. Fang, and H.L.W. Nuttle "Efficient Neural Network Learning Using Second Order Information
with Fuzzy Contro
l," to appear in
Neurocomputing,
2001.


Wu, P., S
-
C. Fang, and H.L.W. Nuttle, “Enhanced Learning Neural Network Learning Using a Self
-
Tuning
Fuzzy Neuron Controller”, for Curved
-
Search Trained Neural Networks,” submitted to
Journal of Computers
and System
s Sciences
, 1999.