An introduction to genetic algorithms
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Proceedings of the third international conference on Genetic algorithms
Exact and approximate solutions of the container ship stowage problem
Proceedings of the 15th annual conference on Computers and industrial engineering
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Container ship stowage problem: complexity and connection to the coloring of circle graphs
Discrete Applied Mathematics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Vehicle Routing with Time and Capacity Constraints Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A decomposition heuristics for the container ship stowage problem
Journal of Heuristics
Computers and Operations Research
An Integer Linear Programming for Container Stowage Problem
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Development of an engine crankshaft in a framework of computer-aided innovation
Computers in Industry
Generating optimal stowage plans for container vessel bays
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
An evolutionary algorithm for the block stacking problem
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Fast generation of near-optimal plans for eco-efficient stowage of large container vessels
ICCL'11 Proceedings of the Second international conference on Computational logistics
Automated stowage planning for large containerships with improved safety and stability
Proceedings of the Winter Simulation Conference
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The purpose of this study is to develop an efficient heuristic for solving the stowage problem. Containers on board a container ship are stacked one on top of the other in columns, and can only be unloaded from the top of the column. A key objective of stowage planning is to minimize the number of container movements. A genetic algorithm technique is used for solving the problem. A compact and efficient encoding of solutions is developed, which reduces significantly the search space. The efficiency of the suggested encoding is demonstrated through an extensive set of simulation runs and its flexibility is demonstrated by successful incorporation of ship stability constraints.