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A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization
Journal of Heuristics
A neural network algorithm for the traveling salesman problem with backhauls
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Ant Colony Optimization
Very large-scale vehicle routing: new test problems, algorithms, and results
Computers and Operations Research
Solving the vehicle routing problem with adaptive memory programming methodology
Computers and Operations Research
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Transportation and logistics organizations often face large-scale combinatorial problems on both operational and strategic levels. By exploiting problem-specific characteristics, classical heuristic methods--such as constructive and iterative local search methods--aim at a relatively limited exploration of the search space, thereby producing acceptable-quality solutions in modest computing times. In a major departure from a classical heuristic, a metaheuristic method exploits not only the problem characteristics but also ideas based on artificial intelligence methodologies, such as different types of memory structures and learning mechanisms, as well as analogies with optimization methods found in nature. Solutions produced by metaheuristics typically are of a much higher quality than those obtained with classical heuristic approaches.This article is part of a special issue on advanced heuristics in transportation and logistics.