In order to build up the Benchmark platform for five types of typical routing problems in logistics distributi...
Yu Yang is with the Dept of Systems Engineering of Northeast University,Shenyang,PR China.Guan Jing is with the Dept of Systems Engineering of Northeast University,Shenyang,PR China.Tang Jiafu is with the Dept of Systems Engineering of Northeast University,and Key Laboratory of Integrated Automation of Process Industry of MOE of Northeast University,Shenyang,PR China.Liu Lili is with the Dept of Systems Engineering of Northeast University,Shenyang,PR China.Zhu Huabo is with and Key Laboratory of Integrated Automation of Process Industry of MOE of Northeast University,Shenyang,PR China
In this study a real-time distributed arrival time control system in a cell manufacturing system for JIT-orien...
Bin Gao is with the school of Information Science and Engineering,Key Lab of Integrated Automation of Process Industry of MOE,Northeastern University,Shenyang,110004,Jun Gong,Qian Li,Jiafu Tang
From the beginning of the 21century, fourth party logistics(4PL)has been attracting more and more attention in...
Yan Cui~(1,2)Min Huang~(1,2)Xingwei Wang~1 1.College of Information Science and Engineering,Northeastern University,Shenyang,Liaoning,110004,China 2.Key Laboratory of Integrated Automation of Process Industry(Northeastern University),Ministry of Education, Shenyang,Liaoning,110004,China
Many real-world problems are dynamic, requiring optimization algorithms being able to continuously track changing optima （optimum） over time. This paper proposes an improved differential evolutionary algorithm using the notion of the near-neighbor effect to determine one individuals neighborhoods, for tracking multiple optima in the dynamic environment. A new mutation strategy using the near-neighbor effect is also presented. It creates individuals by utilizing the stored memory point in its neighborhood, and utilizing the differential vector produced by the ＇near- neighbor-superior＇ and ＇near-neighbor-inferior＇. Taking inspirations from the biological immune system, an immune system based scheme is presented for rapidly detecting and responding to the environmental changes. In addition, a difference- related multidirectional amplification scheme is presented to integrate valuable information from different dimensions for effectively and rapidly finding the promising optimum in the search space. Experiments on dynamic scenarios created by the typical dynamic test instance--moving peak problem, have demonstrated that the near-neighbor and immune system based differential evolution algorithm （NIDE） is effective in dealing with dynamic optimization functions.
