Inventory Based Bi-Objective Flow Shop Scheduling Model and Its Hybrid Genetic Algorithm

Flow shop scheduling problem is a typical NP-hard problem, and the researchers have established many different multi-objective models for this problem, but none of these models have taken the inventory capacity into account. In this paper, an inventory based bi-objective flow shop scheduling model was proposed, in which both the total completion time and the inventory capacity were as objectives to be optimized simultaneously. To solve the proposed model more effectively, we used a tailor-made crossover operator, and mutation operator, and designed a new local search operator, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed. The computer simulations were made on a set of benchmark problems, and the results indicated the effectiveness of the proposed algorithm

Research on Traditional Mongolian-Chinese Neural Machine Translation Based on Dependency Syntactic Information and Transformer Model

Neural machine translation (NMT) is a data-driven machine translation approach that has proven its superiority in large corpora, but it still has much room for improvement when the corpus resources are not abundant. This work aims to improve the translation quality of Traditional Mongolian-Chinese (MN-CH). First, the baseline model is constructed based on the Transformer model, and then two different syntax-assisted learning units are added to the encoder and decoder. Finally, the encoder’s ability to learn Traditional Mongolian syntax is implicitly strengthened, and the knowledge of Chinese-dependent syntax is taken as prior knowledge to explicitly guide the decoder to learn Chinese syntax. The average BLEU values measured under two experimental conditions showed that the proposed improved model improved by 6.706 (45.141–38.435) and 5.409 (41.930–36.521) compared with the baseline model. The analysis of the experimental results also revealed that the proposed improved model was still deficient in learning Chinese syntax, and then the Primer-EZ method was introduced to ameliorate this problem, leading to faster convergence and better translation quality. The final improved model had an average BLEU value increase of 9.113 (45.634–36.521) compared with the baseline model at experimental conditions of N = 5 and epochs = 35. The experiments showed that both the proposed model architecture and prior knowledge could effectively lead to an increase in BLEU value, and the addition of syntactic-assisted learning units not only corrected the initial association but also alleviated the long-term dependence between words

Research on Traditional Mongolian-Chinese Neural Machine Translation Based on Dependency Syntactic Information and Transformer Model

Neural machine translation (NMT) is a data-driven machine translation approach that has proven its superiority in large corpora, but it still has much room for improvement when the corpus resources are not abundant. This work aims to improve the translation quality of Traditional Mongolian-Chinese (MN-CH). First, the baseline model is constructed based on the Transformer model, and then two different syntax-assisted learning units are added to the encoder and decoder. Finally, the encoder’s ability to learn Traditional Mongolian syntax is implicitly strengthened, and the knowledge of Chinese-dependent syntax is taken as prior knowledge to explicitly guide the decoder to learn Chinese syntax. The average BLEU values measured under two experimental conditions showed that the proposed improved model improved by 6.706 (45.141–38.435) and 5.409 (41.930–36.521) compared with the baseline model. The analysis of the experimental results also revealed that the proposed improved model was still deficient in learning Chinese syntax, and then the Primer-EZ method was introduced to ameliorate this problem, leading to faster convergence and better translation quality. The final improved model had an average BLEU value increase of 9.113 (45.634–36.521) compared with the baseline model at experimental conditions of N = 5 and epochs = 35. The experiments showed that both the proposed model architecture and prior knowledge could effectively lead to an increase in BLEU value, and the addition of syntactic-assisted learning units not only corrected the initial association but also alleviated the long-term dependence between words

An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia

HotSpot is an algorithm that can directly mine association rules from real data. Aiming at the problem that the support threshold in the algorithm cannot be set accurately according to the actual scale of the dataset and needs to be set artificially according to experience, this paper proposes a dynamic optimization algorithm with minimum support threshold setting: S_HotSpot algorithm. The algorithm combines simulated annealing algorithm with HotSpot algorithm and uses the global search ability of simulated annealing algorithm to dynamically optimize the minimum support in the solution space. Finally, the Inner Mongolia sandstorm dataset is used for experiment while the wine quality dataset is used for verification, and the association rules screening indicators are set for the mining results. The results show that S_HotSpot algorithm can not only dynamically optimize the selection of support but also improve the quality of association rules as it is mining reasonable number of rules