Decision-making support for transportation and logistics combining rough set fuzzy logic algorithm

Abstract

To lower the operating cost of an enterprise, improving the efficiency of logistics, or reducing the cost of circulation is one of the crucial means for the enterprise to cut the costs. In this paper, combined with the rough set fuzzy logic algorithm, in response to the features of the large-scale vehicle scheduling problem, the data collected by monitoring in real time provided by the tracking technology, in conjunction with the data mining technology, are used to support the decision making based on the reverse logistics mode and establish a vehicle scheduling optimization model with the purpose to support the decision making in transportation logistics.

Operational Rule Extraction and Construction Based on Task Scenario Analysis

“…Intelligent autonomous decision-making is mainly through mathematical optimization, artificial intelligence and other methods to build a mapping from naval operational situation to operational commands. According to the different ideas of solving the mapping, there are mainly: influence diagram [2][3][4], genetic algorithm [5,6], fuzzy logic [7,8], neural network [9] and other methods. In response to different scenarios, these methods have been used to solve the problem of autonomous decision-making, and many results have been achieved [10][11][12].…”