Call for Papers

IEEE Intelligent Systems

Special Issue on “Advanced Heuristics in Transportation and Logistics”

Submissions due 4 February 2005

In today’s transportation and logistics environment, industries are
calling for immediate action on developing computational and simulation-
based methods that will lead to faster transactions, reduced operating
costs, and improved performance and customer service. They also want these
methods to help provide more control and flexibility in their operations
such as production and location planning, warehousing, distribution, and

Transportation and logistics organizations often face large-scale
combinatorial problems on both operational and strategic levels. In such
problems, all possible combinations of decisions and variables must be
examined to find a solution; consequently, no partial enumeration-based
exact algorithm can consistently solve them. This occurs because sharp
lower bounds on the objective value are hard to derive, thus causing a
slow convergence rate. By exploiting problem-specific characteristics,
classical heuristic methods aim at a relatively limited exploration of the
search space, thereby producing acceptable-quality solutions in modest
computing times. As a major departure from a classical heuristic, a
metaheuristic method implies a higher-level strategy controlling a lower-
level heuristic method. Metaheuristics exploit not only the problem
characteristics but also ideas based on artificial intelligence rationale,
such as different types of memory structures and learning mechanisms, as
well as the analogy with other optimization methods found in nature.
Solutions produced by metaheuristics typically are of much higher quality
than those obtained with classical heuristic approaches. Evidently,
metaheuristic approaches’ success arises from intelligent exploitation of
the problem structure and a good deal of insight achieved by the effective
interplay between intensification (concentrating the search into a
specific region of the search space) and diversification (elaborating
various diverse regions in the solution space) mechanisms.

This special issue ( of IEEE
Intelligent Systems will feature original, high-quality submissions that
address all aspects of metaheuristic methods as applied to transportation
and logistics. Applications-oriented papers will be extremely welcome, as
well as papers addressing computational performance of metaheuristic
methods on well-known benchmark instances. The methods of interest
include, but are not limited to,

*  Tabu search
*  Annealing-based algorithms
*  Evolutionary algorithms
*  Adaptive memory procedures
*  GRASP (greedy randomized adaptive search procedures)
*  Ant systems
*  Neural networks
*  Variable neighbourhood search
*  Guided local search
*  Scatter search and Path relinking
*  Simulation-based algorithms

Special Issue Guest Editors
*  Christos D. Tarantilis, Athens University of Economics & Business
*  Diomidis D. Spinellis, Athens University of Economics & Business
*  Michel Gendreau, Université de Montréal

Important Dates
*  Submissions due for review: 4 February 2005
*  Initial decision letter sent: 4 April 2005
*  Final version submitted: 6 May 2005

Submission Guidelines
Submissions should be 3,000 to 7,500 words (counting a standard figure or
table as 200 words) and should follow the magazine’s style and
presentation guidelines (see
References should be limited to 10 citations.
To submit a manuscript for peer-reviewed consideration, please access the
IEEE Computer Society Web-based system, Manuscript Central, at