Transportation Service Quality Improvement through Closed Sequential Pattern Mining Approach

Huang, Haisong; Yao, Liguo; Tsai, Chieh-Yuan

doi:10.1515/cait-2016-0042

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Subsequently, it has been widely used for travel recommendations, movie recommendations, supply chain diagnosis, and many other practical problems [28]. Huang et al [29] used frequent closed sequence mining technology (ClaSP) to analyze cargo transportation data to help transportation companies to discover potential factors that reduce the quality of cargo during transportation. The results showed that this method can be used to determine the causes of low-quality transportation services.…”

Section: Applications Of Sequential Pattern Mining Algorithmmentioning

confidence: 99%

Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules

2020

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Companies accumulate a large amount of production process data during product manufacturing. Sequence data from the mining production process can enable a company to evaluate the manufacturing process, to find the key factors affecting product quality, and to improve product quality. However, the production process mainly exists in the form of text. To solve this problem, we propose a novel frequent pattern mining algorithm (EABMC) based on the text context semantics and rules of the manufacturing process to remove redundant sequences and to obtain good mining results. In this algorithm, first, we use embeddings from language models (ELMo ) to improve the process of text similarity matching and to classify similar semantic processes into one class. Then, the manufacturing process unit (MPU) is proposed by extracting the characteristics of manufacturing process data according to the constraints of the manufacturing process and other conditions. The above two steps cause the complex manufacturing process sequence to merge and simplify. Once again, a frequent pattern mining algorithm (CloFAST) is used to explore the important manufacturing process relationships behind a large amount of manufacturing data. In addition, taking the data from a production enterprise in Guizhou Province as an example, the validity of the method is verified. Compared with other methods, this method is shown to have greater mining efficiency and better results and can find out the key factors that affect product quality, especially for text data.

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Section: Applications Of Sequential Pattern Mining Algorithmmentioning

confidence: 99%

Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules

2020

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Multilayer Network‐Based Evaluation of the Efficiency and Resilience of Network Flows

Rácz-Szabó,

Ruppert,

Abonyi

2024

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Supply chain optimization and resource allocation are challenging because of the complex dynamics of flows. We can classify these flows based on whether they perform value‐added or nonvalue‐added activities in our process. The aim of this article is to present a multilayered temporal network‐based model for the analysis of network flows in supply chain optimization and resource allocation. Implementation of a multilayered network distinguishes value‐added from nonvalue‐added resource flows, enabling a comprehensive view of the flow of resources in the system. Incorporating weighted edges representing the probabilities of time‐dependent flows identifies the resource needs and excesses at each supply site, addresses optimal transportation challenges for resource reallocation, and assesses the efficiency and robustness of the system by examining the overlaps in network layers. The proposed method offers a significant extension to the toolsets for network flow analysis, which has the potential to improve decision‐making processes for organizations dealing with complex resource management problems. The applicability of the proposed method is demonstrated by analyzing the temporal network extracted from taxi cab flows in New York City. With the application of the method, the results indicate that significant reductions in idle times are achievable.

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Transportation Service Quality Improvement through Closed Sequential Pattern Mining Approach

Cited by 2 publications

References 12 publications

Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules

Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules

Multilayer Network‐Based Evaluation of the Efficiency and Resilience of Network Flows

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