Quantum-inspired immune clonal algorithm for railway empty cars optimization based on revenue management and time efficiency

Abstract

By proposing the concept of timeline, transform dynamic vehicle scheduling problem into a series of static vehicle scheduling problems. With the objective function of benefit maximization, the cloud preference model of dynamic empty car scheduling is built considering empty car delay time constraint. The non-dominated antibodies are proportionally immune clonal according to their cloud preference, which are defined by their cloud application preferences. It is beneficial to enhance the forecasting accuracy of the immune gene manipulation, and to increase the speed of finding the optimal solution based on the application preference. Experimental results conclusively demonstrate the efficiency and effectiveness of the improving system availability, load balancing deviation and valid time brought by the proposed algorithm in cloud computing environments, conditions and that more close to the reality, empty car scheduling model for specific time was established.