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If there is the uncertain travel time in the material supply of a JIT assembly line, a shortage of material in the station will be caused, which has an important impact on production. In this paper, the uncertain travel time is represented by the interval number according to the actual situation. By using robust optimization method and introducing a route-dependent the uncertain parameter, the uncertain travel time is considered into the material supply optimization model for minimizing the largest possible vehicle trip time. A genetic algorithm is designed to solve this model. Through the analysis of the relationship between the line stoppage probability and the route-dependent the uncertain parameter, by the Monte Carlo simulation, the final solution can be obtained. The material supply planning for an engine assembly line is taken an example. The solution which can resist the uncertain travel time is gained by using the proposed method; hence the effectiveness of the method is proved.

Overall Equipment Effectiveness (OEE), which is commonly used for production performance monitoring, can mislead decision makers with low accuracy when a large amount of losses remains unclassified. This paper is to study the critical factors and potential pitfalls when trying to automatically estimate the OEE of a manufacturing system, considering uncertainty. Two methods based on fuzzy arithmetic and interval arithmetic respectively are proposed to manage the uncertainty in estimating the production speed, the stoppage duration, and the quality losses. Datasets from real-world settings are used to illustrate the concepts and the benefits of the methods in practice.

Background The HER2-positive breast cancer accounts for 20-30% of all breast cancers. The overexpression of HER2 is associated with malignant proliferation and invasiveness in several cancers. Some HER2 targeting drugs are widely used in clinic to block HER2 or its tyrosine kinase and improve the prognosis of patients. However, drug resistance and side effects are still a big challenge worldwide. Our previous studies have demonstrated that JWA down-regulates HER2 expression and inhibits cell proliferation and migration in gastric cancer cells. Methods The association of JWA with HER2 were analyzed by IHC in 90 tumor sample tissues and paired non-cancerous adjacent normal tissues. The impact of JAC1 on HER2-positive breast cancer cells was studied using colony formation assay. The effect of JAC1 on the localization of HER2 was detected by immunofluorescence microscopy assay. Western blotting, RT-PCR and immunoprecipitation assay were utilized to investigate the mechanisms of JWA regulating HER2. Finally, xenograft mouse models using BT474 cells were established to confirm the effect of JAC1 on tumor growth. Results In this study, we found that JAC1, a small molecule agonist of JWA gene, dose-dependently suppressed HER2-positive breast cancer proliferation in vitro and in vivo . Importantly, the inhibitory roles of JAC1 was due to the degradation of HER2 overexpression. The mechanistic evidences showed JAC1 increased the ubiquitination of HER2 at the K716 through the E3 ubiquitin ligase SMURF1; further, the activation of SMURF1 was due to reduced expression of NEDD4, an E3 ubiquitin ligase for SMURF1. Finally, we identified that the role of JAC1 on NEDD4 was realized through JWA-p38-GATA-1-NEDD4 axis. Conclusions These findings suggest that JAC1 may as a novel therapeutic agent to breast cancer through JWA/p38/GATA-1/NEDD4/SMURF1/HER2 signaling axis.