[agents] Cfp for Edited Book: COMPUTATIONAL INTELLIGENCE FOR SUPPLY CHAIN MANAGEMENT AND DESIGN: ADVANCED METHODS

[Georgios Dounias]

*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.dounias at aegean.gr; 
n.ampazis at 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.)?

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*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*

*(*vzeimp at fme.aegean.gr <mailto:vzeimp at fme.aegean.gr>*)*

* *