|
Soft Computing in Manufacturing, Part II
A Special Issue of the Journal
Intelligent Manufacturing
Vol. xx, No. x, 2002
T. Warren Liao and Evangelos Triantaphyllou
Guest Editors
|
TABLE OF CONTENTS
A Fuzzy
Mid-term Single-fab Production Planning Model
by Toly Chen (See Abstract)
A
Case-Based Expert Support System For Due-Date Assignment In A Wafer
Fabrication Factory
by Chaochang Chiu, Pei-Chann Chang, And Nan-Hsing Chiu
(See
Abstract)
Modeling and Analysis for Multi-period, Multi-product
and Multi-resource Production Scheduling
by Hsiao-Fan Wang and Kuang-Yao Wu (See Abstract)
Soft Computing For Scheduling With Batch Setup Times
And Earliness-Tardiness Penalties On Parallel Machines
by Y. Yi And D.W. Wang (See
Abstract)
Finding Multiple Solutions In Job
Shop Scheduling By Niching Genetic Algorithms
by E. Pérez,
F. Herrera, and C. Hernández (See Abstract)
Real Time Fuzzy Scheduling Rules
in FMS
by Felix T. S. Chan, H. K. Chan
and A. Kazerooni (See Abstract)
A Modified
Genetic Algorithm for Distributed Scheduling Problems
by H.Z. Jia, A.Y.C. Nee, J.Y.H. Fuh and Y.F. Zhang
(See Abstract)
A
Problem Space Genetic Algorithm in Multiobjective
Optimization
by Ayten
Turkcan
and
M. Selim Akturk
(See
Abstract)
Identification
of Process Disturbance Using SPC/EPC and Neural Networks
by Chih-Chou Chiu, Yuehjen E. Shao, Tian-Shyug Lee and Ker-Ming Lee(See
Abstract)
ABSTRACTS:
- A
Fuzzy Mid-term Single-fab Production Planning Model
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by Toly Chen
Department of Information Management
Chaoyang University of Technology,
Taichung County, Taiwan 413, R. O. C.
Email: tolychen@ms37.hinet.net
ABSTRACT:
Production planning is a complicated task for a semiconductor fabrication
plant because of the uncertainties in demand, product prices,
cycle times, and product yields. Traditionally, mid-term
production planning for a semiconductor fabrication plant is
handled with MRP systems or optimized by solving LP or FLP
problems. In this study, the philosophy of prioritizing demand
classes with higher certainties as proposed by Leachman (1993) is
applied to the FLP model of Chen and Wang (1998), and a new FLP
model for planning the mid-term production of single wafer
fabrication plant is constructed. Parameters in this model are
given in the form of trapezoidal fuzzy numbers. Fuzzy comparison
is adopted in dealing with the fuzzy objective function and
expanding inequalities. The outputs are projected using Chen and
Wang’s fuzzy dynamic production function. The uncertain demand
is classified and satisfied with four successively optimized FLP
submodels according to their ascending uncertainties. Chen and
Wang’s example is adopted to demonstrate the proposed
methodology and to make some comparisons. By moving more capacity
to demand classes with higher certainties that are usually nearer
and have larger discounted revenues, the proposed methodology
achieves a higher value of the discounted cash flows than the two
referenced modelstomotive speedometer design case study is included to
demonstrate the methodology.
KEY WORDS: Production planning, fuzzy linear programming, wafer fabrication
- A
Case-Based Expert Support System For Due-Date Assignment In A
Wafer Fabrication Factory
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by Chaochang Chiu1, Pei-Chann Chang2, And Nan-Hsing Chiu1
1Department
of Information Management, Yuan Ze University,
Taoyuan, Taiwan, R.O.C.
2Industrial
Engineering and Management, Yuan Ze University,
Taoyuan,
Taiwan, R.O.C.
ABSTRACT:
Owing to the complexity of wafer fabrication, the
traditional human approach to assigning due-date is imprecise and
very prone to failure, especially when the shop status is
dynamically changing. Therefore, assigning a due date to each
order becomes a challenge to the production planning and
scheduling staff. Since most production orders are similar to
those previously manufactured, the case based reasoning (CBR)
approach provides a suitable means for solving the due-date
assignment problem. This research proposes a CBR approach that
employs the k-nearest neighbors concept with dynamic feature
weights and non-linear similarity functions. The test results show that the proposed approach can more
accurately predict order due dates than other approaches.
KEY WORDS: Due-date assignment, genetic algorithms, case-based reasoning
- Modeling
and Analysis for Multi-period, Multi-product and Multi-resource
Production Scheduling
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by Hsiao-Fan Wang* and Kuang-Yao Wu
Department of Industrial Engineering and
Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, R.O.C. 30043
ABSTRACT:
This study presents a framework for solving the multi-period,
multi-product and multi-resource production-scheduling (M3PS)
problem. Practically, the main concern for an M3PS
problem is how to satisfy two management policies: 1) each product is manufactured in a continuous manner so
that once the product is on a production line, it will complete
its production procedure without interruption, and 2) the number
of the product’s types is limited during one period. By defining
the decision variables and taking into account the machine’s
capacity and the customers’ demand, a mixed integer programming
(MIP) Model is formulated. To solve this MIP problem, a two-phase
approach is proposed. In phase 1, the search space of the MIP
Model is transformed into a preliminary pattern by a heuristic
mining algorithm so that a hyper assignment problem can be formed
as a reference model to be solved. In phase 2, a stochastic global
optimization procedure that incorporates a genetic algorithm with
neighborhood search techniques is designed to obtain the optimal
solution. A numerical experiment is presented with an
illustration, and it shows that the proposed model is adequate to
cope with complicate scheduling problems.
KEY WORDS: Production scheduling; Mixed integer programming; Hybrid Genetic
algorithm; Combinatorial optimization; Neighborhood search
- Soft
Computing For Scheduling With Batch Setup Times And
Earliness-Tardiness Penalties On Parallel Machines
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by Y. Yi1 And D.W. Wang2
1,2 School of Information Science and Engineering,
Northeastern University, Shenyang,
110006, People’s Republic of China
E-mail: 1yiyangsat@btamail.net.cn,2dwwang@mail.neu.edu.cn.
ABSTRACT:
A model for scheduling grouped jobs on identical parallel machines is
addressed in this paper. The model assumes that a set-up time is
incurred when a machine changes from processing one type of
component to a different type of component, and the objective is to
minimize the total earliness-tardiness penalties. In this paper, the
algorithm of soft computing, which is a fuzzy logic embedded Genetic
Algorithm is developed to solve the problem. The efficiency of this
approach is tested on several groups of random problems and shows
that the soft computing algorithm has potential for practical
applications in larger scale production systems.
KEY WORDS: soft computing, fuzzy logic, fuzzy decision, genetic algorithm, setup
times, scheduling with batch setup times on parallel machines
- Finding
Multiple Solutions In Job Shop Scheduling By Niching Genetic
Algorithms
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by E. Pérez*, F. Herrera**, and C. Hernández*
* Industrial Engineering Group
School of Industrial Engineering
University of Valladolid
47011 - Valladolid, Spain ;E:mail:elena,
cesareo@eis.uva.es
** Dept. of Computer Science and Artificial Intelligence
University of Granada
18071 - Granada, Spain
E-mail:herrera@decsai.ugr.es
ABSTRACT:
The
interest in multimodal optimization methods is increasing in the
last years. The objective is to find multiple solutions that allow
the expert to choose the solution that better adapts to the actual
conditions.
Niching
methods extend genetic algorithms to domains that require the
identification of multiple solutions. There are different niching
genetic algorithms: sharing, clearing, crowding and sequential,
etc.
The aim of this study is to
study the applicability and the behavior of several niching
genetic algorithms in solving job shop scheduling problems, by
establishing a criterion in the use of different methods according
to the needs of the expert. We will experiment with different
instances of this problem, analyzing the behavior of the
algorithms from the efficacy and diversity points of view.
KEY WORDS: Job shop scheduling problem,
multimodal optimization, genetic algorithms, niching methods.
- Real Time Fuzzy Scheduling Rules
in FMS
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by Felix T. S. Chan1,
H. K. Chan1, A. Kazerooni2
1Department
of Industrial and Manufacturing Systems Engineering
University
of Hong Kong, Pokfulam, Hong Kong Phone:
(852) 2859 7059; Fax: (852) 2858 6535; e-mail:
ftschan@hkucc.hku.hk
2School
of Advanced Manufacturing and Mechanical Engineering
University
of South Australia, The Levels Campus, Mawson Lakes,SA
5095 Australia
ABSTRACT:
This paper
presents a real-time fuzzy expert system to scheduling parts for a
flexible manufacturing system (FMS).
First, some vagueness and uncertainties in scheduling rules
are indicated and then a fuzzy-logic approach is proposed to improve the system performance
by considering multiple performance measures.
This approach focuses on characteristics of the system’s
status, instead of parts, to assign priorities to the parts
waiting to be processed. Secondly,
a simulation model is developed and it has shown that the proposed
fuzzy logic-based decision making process keeps all performance
measures at a good level. The proposed approach provides a promising alternative
framework in solving scheduling problems in FMSs, in contrast to
traditional rules, by making use of intelligent tools.
KEY WORDS:
Flexible Manufacturing System (FMS), fuzzy, expert systems,
simulation.
- A
Modified Genetic Algorithm for Distributed Scheduling Problems
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by H.Z. Jia, A.Y.C. Nee, J.Y.H. Fuh*, and Y.F. Zhang
Department of Mechanical Engineering
National University of Singapore
10 Kent Ridge Crescent, Singapore 119260
e-mail: mpefuhyh@nus.edu.sg; fax:
(65) 779-1459
ABSTRACT:
Genetic Algorithms (GAs) have been widely applied to the scheduling and
sequencing problems due to its applicability to different domains
and the capability in obtaining near-optimal results. Many
investigated GAs are mainly concentrated on the traditional single
factory or single job-shop scheduling problems. However, with the
increasing popularity of distributed, or globalized production,
the previously used GAs are required to be further explored in
order to deal with the newly emerged distributed scheduling
problems. In this paper, a modified GA is presented, which is
capable of solving traditional scheduling problems as well as
distributed scheduling problems. Various scheduling objectives can
be achieved including minimizing makespan, cost and weighted
multiple criteria. The proposed algorithm has been evaluated with
satisfactory results through several classical scheduling
benchmarks. Furthermore, the capability of the modified GA was
also tested for handling the distributed scheduling problems.
KEY WORDS: Genetic Algorithms; Distributed Production;
Distributed Scheduling.
-
A
Problem Space Genetic Algorithm in Multiobjective
Optimization
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by Ayten
Turkcan and M. Selim Akturk
ABSTRACT:
KEY WORDS:
- Identification
of Process Disturbance Using SPC/EPC and Neural Networks
Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.
by Chih-Chou
Chiu1, Yuehjen E. Shao2, Tian-Shyug Lee3
and Ker-Ming Lee4
1 Institute of Commerce Automation and Management,
National Taipei University of
Technology,
Taipei, Taiwan, R.O.C.
2 Department
of Statistics
and Information Sciences, Fu-Jen Catholic University
Taipei, Taiwan, R.O.C.
3 Department
of Business Administration,
Fu-Jen Catholic University
Taipei, Taiwan, R.O.C.
4 Institute
of Applied Statistics, Fu-Jen Catholic University
Taipei, Taiwan, R.O.C.
ABSTRACT:
Since solely using statistical process control (SPC) and engineering
process control (EPC) cannot optimally control the manufacturing
process, lots of studies have been devoted to the integrated use
of SPC and EPC. The
majority of these studies have reported that the integrated
approach has better performance than that by using only SPC or EPC.
Almost all these studies have assumed that the assignable
causes of process disturbance can be identified and removed by SPC
techniques. However,
these techniques are typically time-consuming and thus make the
search hard to implement in practice.
In this paper, the EPC and neural network scheme were
integrated in identifying the assignable causes of the underlying
disturbance. For
finding the appropriate setup of the networks’ parameters, such
as the number of hidden nodes and the suitable input variables,
the all-possible-regression selection procedure is applied.
For comparison, two SPC charts, Shewhart and cumulative sum
(Cusum) charts were also developed for the same data sets.
As the results reveal, the proposed approaches outperform
the other methods and the shift of disturbance can be identified
successfully.
KEY WORDS: statistical process control; engineering process control; minimum mean
squared error control; neural networks; identification of process
disturbance
Dr. Liao's Homepage
Dr. Triantaphyllou's Homepage
Dr. Triantaphyllou's Books /
Special Issues web site
Send suggestions / comments to
Dr. E. Triantaphyllou (trianta@lsu.edu).
|