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Beijing Technology and Business University Higher Education Garden, Liangxiang, Beijing.
As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment.
Hankun Zhang; Borut Buchmeister; Xueyan Li; Robert Ojstersek. Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment. Mathematics 2021, 9, 909 .
AMA StyleHankun Zhang, Borut Buchmeister, Xueyan Li, Robert Ojstersek. Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment. Mathematics. 2021; 9 (8):909.
Chicago/Turabian StyleHankun Zhang; Borut Buchmeister; Xueyan Li; Robert Ojstersek. 2021. "Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment." Mathematics 9, no. 8: 909.
The integrated pipe gallery, also known as urban lifeline, is a significant content of the smart city. While the video surveillance system is a crucial part of the integrated pipe gallery, which provides a basis for the construction of smart city. Due to the large amount of video data, manual monitoring is a time-consuming and laborious task. To address the above problems, we propose a neural network-based method that incorporates the concept of area under curve (AUC) with the multiple-instance learning (MIL) approach. We formulate the multiple-instance AUC (MIAUC) model that predicts high anomaly scores for anomalous segments. Furthermore, sparsity and temporal smoothness constraints are utilized in the loss function to better detect anomaly. To verify the effectiveness of our proposed method, a new database is established based on the video surveillance system, which consists of 110 real-world surveillance videos with a total length of 24 h. The experimental results on the real-world database show that our method achieves better performance as compared to the baselines methods. Moreover, we design a MIAUC-based video surveillance system and the practical effect reveals the prospect of utilizing the MIL method for person anomaly detection in the integrated pipe gallery.
Laisong Kang; Shifeng Liu; Hankun Zhang; Daqing Gong. Person anomaly detection-based videos surveillance system in urban integrated pipe gallery. Building Research & Information 2020, 49, 55 -68.
AMA StyleLaisong Kang, Shifeng Liu, Hankun Zhang, Daqing Gong. Person anomaly detection-based videos surveillance system in urban integrated pipe gallery. Building Research & Information. 2020; 49 (1):55-68.
Chicago/Turabian StyleLaisong Kang; Shifeng Liu; Hankun Zhang; Daqing Gong. 2020. "Person anomaly detection-based videos surveillance system in urban integrated pipe gallery." Building Research & Information 49, no. 1: 55-68.