Abstract
From a viewpoint of real engineering applications, a new and important concept of neighborhood is proposed for the solution of COP like VRP or TSP, which has been re-formulated with fuzzy set theory. Since the known cost information between the elements of COP is normalized into the real number [0,1], A concept of neighborhood degree is also proposed to measure the scale from the nearest (smallest cost) to the farthest (biggest cost) with fuzzy evaluation. A total neighborhood measure is proposed for estimating the quality of the solution in dynamic exploring process, and a partial neighborhood measure is also proposed, where the inferior portion of the worst solution can be detected and improved in the next operation. The properties and main role of the whole neighborhood measure are shown by the TSP (Traveling Salesman Problem) experiments, which the various tour data (benchmark, random and fractal type) are used and the scales are adjustable at 30-5000. Another experiment with dispatch and delivery problem for real trucks is also performed, where the poor tour (for vehicles) is detectable by the whole neighborhood measure and the poor trip (sub-tour for delivery job) can be caught through partial neighborhood measure. The algorithm using above strategy can avoid unnecessary parameter setting and press the useless and duplicate operation down. It is confirmed that the whole computational process is 15-30% faster than usual evolutionary method.
