China
Africa
Explore chapters and articles related to this topic
Petri Net Modeling and Scheduling of Automated Manufacturing Systems
Published in Cornelius Leondes, Optimization Methods for Manufacturing, 2019
MengChu Zhou, Huanxin Henry Xiong
Shih and Sekiguchi (19) presented a timed Petri net and beam search method to schedule an FMS. Beam search is an artificial intelligence technique for efficient searching in decision trees. When a transition in a timed Petri net is enabled, if any of its input places is a conflicted input place, the scheduling system calls for a beam search routine. The beam search routine then constructs partial schedules within the beam depth. Based on the evaluation function, the quality of each partial schedule is evaluated and the best is returned. The cycle is repeated until a complete schedule is obtained. This method based on partial schedules does not guarantee global optimization.
English Translation proofreading System based on Information Technology: Construction of semantic Ontology Translation Model
Published in Applied Artificial Intelligence, 2023
In NEURAL network machine translation decoding, beam search is usually used, which can find the local optimal target in each step of the search. However, due to the calculation of only one step forward, beam search usually cannot output the globally optimal target translation. In 2017, (Deng 2021; He, Wu, and Li 2021) and other scholars proposed the idea of using value network to improve neural machine translation for this problem (Galan-Manas 2011). This method adopts the circular structure of value network and uses bilingual data to train parameters bidirectionally. In the testing process, the neural network machine translation model will calculate the conditional probability function, and the value network will predict the long-term value. According to the conditional probability function and long-term value, decoding words can be obtained. Experimental results show that this method can significantly improve the accuracy of multi-language translation.
Retrieval sequencing in autonomous vehicle storage and retrieval systems
Published in International Journal of Production Research, 2023
Yugang Yu, Jingjing Yang, Xiaolong Guo
Beam search is generally divided into two steps: search all possible states and select a limited number of the best states. For each stage (or step), beam search orders all partial states (or nodes) according to some rules. The inferior nodes are pruned, and several best partial states are kept. The beam search then explores a graph by expanding the most promising states in a limited set to reduce the search space and the computation time. The steps of the beam search heuristic are as follows. Step 1. Find all partial states.Step 2. Order these partial states and select the best partial states.
An efficient optimisation method based on weighted AND-OR trees for concurrent reconfigurable product design and reconfiguration process planning
Published in International Journal of Production Research, 2023
From the sub-nodes with OR relation in design (OR-D) in a generic design AND-OR tree or OR relation in operation (OR-O) in a generic reconfiguration process AND-OR tree, at least one node should be selected. For the remaining nodes, some are pruned based on the importance weights of these nodes through a modified beam search method to reduce the search space. The beam width parameters at different levels in the tree are selected based on the numbers of nodes at these levels in the tree and the computation efficiency requirement. Initial ranking of candidates through heuristics