Integer and combinatorial optimization
Integer and combinatorial optimization
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
The guilty net for the traveling salesman problem
Computers and Operations Research - Special issue on neural networks and operations research
Easing the conscience of the guilty net
Computers and Operations Research
Algorithms for the vehicle routing problems with time deadlines
American Journal of Mathematical and Management Sciences - Special issue: vehicle routing 2000: advances in time windows, optimality, fast bounds, & multi-depot routing
Computers and Operations Research
Self-organizing maps
Heuristics for the multi-vehicle covering tour problem
Computers and Operations Research
Vision and Neural Control for an Orange Harvesting Robot
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
An argument for abandoning the travelling salesman problem as a neural-network benchmark
IEEE Transactions on Neural Networks
An Effective Traveling Salesman Problem Solver Based on Self-Organizing Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Approximate solution of the multiple watchman routes problem with restricted visibility range
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Inspection planning in the polygonal domain by Self-Organizing Map
Applied Soft Computing
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The double traveling salesman problem is a variation of the basic traveling salesman problem where targets can be reached by two salespersons operating in parallel. The real problem addressed by this work concerns the optimization of the harvest sequence for the two independent arms of a fruit-harvesting robot. This application poses further constraints, like a collision-avoidance function.The proposed solution is based on a self-organizing map structure, initialized with as many artificial neurons as the number of targets to be reached. One of the key components of the process is the combination of competitive relaxation with a mechanism for deleting and creating artificial neurons. Moreover, in the competitive relaxation process, information about the trajectory connecting the neurons is combined with the distance of neurons from the target. This strategy prevents tangles in the trajectory and collisions between the two tours. Results of tests indicate that the proposed approach is efficient and reliable for harvest sequence planning. Moreover, the enhancements added to the pure self-organizing map concept are of wider importance, as proved by a traveling salesman problem version of the program, simplified from the double version for comparison.