Cfp for Edited Book: COMPUTATIONAL INTELLIGENCE FOR SUPPLY CHAIN MANAGEMENT AND DESIGN: ADVANCED METHODS
g.do...@gmail.com
*COMPUTATIONAL INTELLIGENCE FOR SUPPLY CHAIN MANAGEMENT AND DESIGN:
ADVANCED METHODS*
EDITED BOOK
IGI Global (former IDEA publishing)
*Book Editors:
*
*/I. Minis, V. Zeimpekis, G. Dounias, N. Ampazis/*
Department of Financial & Management Engineering
University of the Aegean
{i.minis, vzeimp}@fme.aegean.gr; g.do...@aegean.gr;
n.am...@fme.aegean.gr
*1. Synopsis*
This edited volume will focus on the contribution of Computational
Intelligence to Supply Chain Management. Computational Intelligence
(CI) is a term corresponding to a new generation of algorithmic
methodologies in artificial intelligence, which combines elements of
learning, adaptation, evolution and approximate (fuzzy) reasoning to
create programs that -in a way- can be considered intelligent. The
proposed edited volume will present CI methods addressing topics in
the entire spectrum of the supply chain i.e. from forecasting,
planning for production and distribution to actual implementation,
including production and inventory control, warehouse management,
management of distribution channels, and transportation. Emphasis will
be given to those CI methods and techniques that provide effective
solutions to complex supply chain problems, exhibiting superior
performance with respect to other methods of operations research. The
edited volume will also include integrated case studies that describe
the solution to actual problems of high complexity.
* *
* *
*2. Supply Chain and Computational Intelligence*
* *
The supply chain of both manufacturing and commercial enterprises
comprises a highly distributed environment, in which complex processes
evolve in a network of companies. Such processes include materials
procurement and storage, production of intermediate and final
products, warehousing, sales, and distribution (see Fig. 1). The role
of the supply chain in a company’s competitiveness is critical, since
the supply chain affects directly customer service, inventory and
distribution costs, and responsiveness to the ever changing markets.
Furthermore, this role becomes more critical in today’s distributed
manufacturing environment, in which companies focus on core
competencies and outsource supportive tasks, thus creating large
supply networks. Within this environment there are strong interactions
of multiple entities, processes, and data. For each process in
isolation, it is usually feasible to identify those decisions that are
locally optimal, especially in a deterministic setting. However,
decision making in supply chain systems should consider intrinsic
uncertainties, while coordinating the interests and goals of the
multitude of processes involved.
**
*Figure 1.* The flow of decisions and information in the supply chain
Most advances in the use of computational methods to support supply
chain operations have focused in low level operational decisions,
while little attention has been applied to more important areas of
supply chain management like product forecasting and strategic support
systems. In addition, many existing models focus on individual
components of the overall system, and thus ignore the integrated
approach. An integrated approach, however, is essential due to the
inherent trade-offs involved in all stages of the supply chain
operations.
Computational Intelligence has emerged as a rapid growing field in the
past few years. Its variety of intelligent techniques emulate human
intelligence and processes found in natural systems such as adaptation
and learning, planning under large uncertainty, coping with large
amounts of data, etc. Successful industrial applications of
intelligent systems usually deal with several of these aspects and it
is therefore natural to combine various technologies with different
capabilities within an integrated decision support system. Most of the
tasks required for effective management of logistics activities can be
achieved using methodologies from several areas of computational
intelligence.
For the purposes of this book computational intelligence methodologies
are generally classified into three major areas, according to the
nature of the methodology used to approach supply chain management
problems:
1. _Standard_ widely acknowledged and applied _intelligent
techniques_, such as neural networks (NN), fuzzy systems (FS),
genetic algorithms and genetic programming (GA/GP, and other
machine learning algorithms (ML). These methods manage to
successfully perform association, generalization, function
approximation, rule induction, etc. in difficult multivariate
domains of application. Methods belonging to this category could
be further divided into automated-learning computational
intelligence techniques, (NNs, GA/GP, other ML algorithms) and in
intelligent modeling approaches (where fuzzy systems and rough
sets could be included, as well as approaches related to fuzzy
decision analysis, intelligent multi-criteria decision making,
etc).
2. _Hybrid and Adaptive Intelligence_ by which is meant any
efficient
combination of the above mentioned intelligent techniques, with
other intelligent or conventional methodologies for handling
complex problems. Usually one of the methods combined within a
hybrid or adaptive scheme, is used either to filter or to fine
tune special operations of another methodology, in an intelligent
manner and in a way that the total scheme performs superior to
simple standard or conventional approaches. Most popular hybrid
methodologies are neuro-fuzzy systems, evolving-fuzzy systems,
neuro-genetic approaches and genetic-fuzzy ones. There are also
applications in literature combining wavelets with intelligent
techniques, as well as standard intelligent techniques with
nature-inspired ones.
3. _Nature Inspired Intelligence_ (NII) in which are included
methodologies such as swarm intelligence, ant colony
optimization,
bee-algorithms, artificial immune systems etc., applied in
logistics and supply chain optimization problems. Usually these
methodologies represent simultaneous exploration and exploitation
of the search space in a smart manner (i.e. local and global
search), analogously to the way natural systems or societies
perform similar tasks (e.g. swarm flying or swimming, food search
and identification, etc.)
This edited volume will present CI methods addressing topics in the
entire spectrum of the supply chain i.e. from forecasting, planning
for production and distribution to actual implementation, including
production and inventory control, warehouse management, management of
sales and distribution channels, and transportation. Emphasis will be
given to those CI methods and techniques that provide effective
solutions to complex supply chain problems, exhibiting superior
performance with respect to other methods of operations research. The
edited volume will also include integrated case studies that describe
the solution to actual problems of high complexity.
It is our aim to include at least one intelligent methodology of each
of the above mentioned categories, applied to each of the five (5)
parts to which the book contents are divided. Furthermore, we
especially welcome contributions that address and discuss important
issues related to the application of computational intelligence to
supply chain and logistics, such as:
* Why computational intelligence is suitable for supply chain
optimization problems and in which cases?
* Which of the CI methodologies seems to be the method of choice
for
what kind of supply chain problem?
* Which are the main advantages of the most popular CI approaches
in
logistics domains and why?
* What are the best tasks to perform using CI when handling
optimization problems in supply chain (e.g. classification,
clustering, modelling, etc.)?
* *
* *
*3**. **Draft contents** **of** **book** *
The proposed edited volume will comprise 5 parts. The first 4 parts
will include chapters that focus on computational intelligence
applications to different functions of the supply chain. The fifth
chapter will focus on supply chain integration; i.e. it will include
chapters that present the use of computational intelligence in real-
life applications and case studies. A tentative table of contents is
presented below.
* *
*PART I: Procurement and Inventory management*
Potential topics include CI contributions in:
* Supplier selection
* Procurement
* Cost management
* Just-in-time procurement
* Inventory management
* The balance of customer service vs. cost in purchased goods
* Supplier collaboration
* Spend analysis
* Other topics in procurement and inventory management
*PART II: Production Planning and Scheduling*
Potential topics include CI contributions in:
* Hierarchical production planning
* Aggregate production planning
* Materials requirement planning
* Just in time productivity
* Lean production
* Production scheduling
* Order management
* Shop floor control
* Factory dynamics
* Other topics in production planning and scheduling
*PART III: Warehousing, Transportation and Distribution management*
* *
Potential topics include CI contributions in:
* Storage and handling decisions,
* Picking
* Stock control in view of competing orders
* Vehicle routing (planning, dynamic routing)
* Fleet management systems
* Distribution management
* Other topics in warehousing, transportation and distribution
management
* *
*PART IV: Forecasting, Sales and Customer Service*
* *
Potential topics include CI contributions in:
* Demand forecasting
* Defining optimum service levels and inventory costs
* Sales management
* Distribution channel management
* Other topics in forecasting, sales and customer service
* *
*PART V: Integration of Supply Chain *
* *
Includes integrated methods, novel system concepts and applications of
computational intelligence in improving a significant part of supply
chain activities.
* *
*4. Deadlines and Important Dates*
* *
The work schedule for the book is as follows:
- Extended abstracts of contributions (max 750 words): *October 15,
2008*
- Editorial responses to abstracts: *November 15, 2008*
- Firs drafts of chapters (max 6.000 words):* **January 30, 2009*
- Reviews due by: *March 31, 2009*
- Second draft of chapters: *April 30, 2009*
- Camera ready chapters: *June 30, 2009*
- Book published: *September 2009*
All draft chapters will be subject to a double-blind review by three
reviewers (not including the editorial review by the book editors).
*Extended abstracts and chapters should be sent to Dr. V. Zeimpekis*
*(*vze...@fme.aegean.gr <mailto:vze...@fme.aegean.gr>*)*
* *