Heuristic Search Methods for Large Scale Optimization Problems in
Industry
Evolutionary Computation Journal, MIT Press
http://ecj.lri.fr/
EXTENDED DEADLINE: August 31, 2010.
Guest Editors:
Andreas Ernst, CSIRO Mathematical and Information Science,
andreas.ernst@csiro.au
Zbigniew Michalewicz, University of Adelaide,
zbigniew.michalewicz@adelaide.edu.au
Frank Neumann, Max Planck Institute for Informatics, fne@mpi-inf.mpg.de
Description:
This special issue aims to provide a forum for researchers working on
large applied optimization problems arising in industry. Such problems
often defy solution by exact approaches such as integer and constraint
programming. Particular challenges of large applied problems include the
presence of complex objectives containing a mixture of real costs and
soft constraints which can be time consuming to evaluate. In addition
the search space of problems is often so large that traversing it with
neighbourhood-move, crossover or mutation operators can take too long to
allow effective exploration. This special issue solicits novel
high-quality contributions on heuristic methods for large applied
optimization problems that have been used in practice. While papers on
any aspect of solving large scale optimization problems in industry are
welcome, of special interest are submissions on:
-Scheduling/planning problems solved for particular organizations
-Novel methods for exploring very large search spaces
-Decomposition and parallel computing techniques for solving large
optimization problems
-Hybrid heuristics for real world optimization problems
-Methods for dealing with large data sets in formulating and solving
large optimization problems, including issues around data consistency
and completeness.
-Case studies of successful (or unsuccessful) implementations of large
scale heuristic optimization algorithms in a business and lessons
learned from these.
Authors are encouraged to make available (de-identified) versions of
some of their real world data sets to support the development of more
sophisticated methods to deal with some of these challenging problems.
This special issue will not consider the solution of large abstract
problems (eg classical VRP or job shop scheduling problems) nor papers
that only test algorithms on randomly generated data sets. Authors are
invited to submit original work on topics relevant for this special
issue. The publication of the special issue is tentatively scheduled for
Summer 2011.
Submission:
Authors should submit their manuscripts to the Evolutionary Computation
Editorial Manager at http://ecj.lri.fr/.
When submitting a paper, please send at the same time also an
email to Frank Neumann (fne@mpi-inf.mpg.de) and a copy to ecj@lri.fr
mentioning the special issue, the
paper title, and author list to inform about the submission.
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