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An Improved Multi-objective Algorithm for Flow Allocations in Logistics Preference Paths
When many weights are stochastic, the flow allocation of paths is a key stage of the transportation network’s efficiency. Currently, a variety of methods (or heuristics) have been proposed to solve this complex optimization problem, with good results in some cases just with limitations in the special fields. On this basis, we develop an algorithm for model multi-objective that combines ideas under stochastic weight. The method performs well even when the order of magnitude and/or the range of the parameters were unknown. It refines iteratively a sequence of parameter distributions through preference combined with partial exampling from a historical prior defined over the support of all previous iterations. Using the simulated and real experimental data, we exemplify our method with multi-objective improved models-estimated the weight efficiently even in the absence of the weight.
flow allocation, multi-objective optimization, path preference.
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