ROBUSTNESS OF VARIOUS PRODUCTION CONTROL POLICIES IN SEMICONDUCTOR MANUFACTURING

This paper compares several different production control policies in terms of their robustness to random disturbances such as machine failures, demand fluctuations, and system parameter changes. Simulation models based on VLSI wafer fabrication facilities are utilized to test the performance of the policies. Three different criteria, namely, the average total WIP, the average backlog, and a cost function combining these measures, are used to evaluate performance. Among the policies tested, the Two‐Boundary Control policy outperforms the others.     

Explainable deep learning powered building risk assessment model for proactive hurricane response

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities’ preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings’ risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.     

网络嵌入、吸收能力与企业绩效——基于甘宁青老字号企业的实证研究

吸收能力的强弱直接影响到老字号企业利用社会网络资本获得有价资源和信息所创造价值的大小。通过对甘宁青地区的32家老字号企业实地调查和实证分析,结果显示:网络嵌入对企业绩效有显著正向影响,吸收能力在网络嵌入与企业绩效之间起到中介作用,本文研究成果对老字号企业适应消费市场变化具有重要启示。     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

Sensation seeking predicts risky driving behavior: The mediating role of difficulties in emotion regulation

The purpose of this study was to explore the mediating effect of difficulties in emotion regulation on the relationship between sensation seeking and driving behavior based on the dual-process model of aberrant driving behavior. A sample of 299 drivers in China completed the Difficulties in Emotion Regulation Scale, the Driver Behavior Questionnaire, and the Sensation Seeking Scale V (SSS). The relationships among sensation seeking, difficulties in emotion regulation, and driving behavior were investigated using pathway analysis. The results showed that (1) disinhibition and boredom susceptibility are positively and significantly related to difficulties in emotion regulation and risky driving behaviors; (2) difficulties in emotion regulation are positively and significantly associated with risky driving behaviors; (3) difficulties in emotion regulation mediate the effect of sensation seeking on driving behaviors, supporting the dual-process model of driving behavior; and (4) professional drivers score higher in terms of difficulties in emotion regulation and risky driving behaviors than nonprofessional drivers. The findings of this study could provide valuable insights into the selection of suitable drivers and the development of certain programs that benefit road safety.