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We study an organization’s one-time capacity investment in a renewable energy-producing technology with supply intermittency and net metering compensation. The renewable technology can be coupled with conventional technologies to form a capacity portfolio that is used to meet stochastic demand for energy. The technologies have different initial investments and operating costs, and the operating costs follow different stochastic processes. We show how to reduce this problem to a single-period decision problem and how to estimate the joint distribution of the stochastic factors using historical data. Importantly, we show that data granularity for renewable yield and electricity demand at a fine level, such as hourly, matters: Without energy storage, coarse data that does not reflect the intermittency of renewable generation may lead to an overinvestment in renewable capacity. We obtain solutions that are simple to compute, intuitive, and provide managers with a framework for evaluating the trade-offs of investing in renewable and conventional technologies. We illustrate our model using two case studies: one for investing in a solar rooftop system for a bank branch and another for investing in a solar thermal system for water heating in a hotel, along with a conventional natural gas heating system.

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BackgroundAccurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many computational approaches have been developed for predicting drug-target associations on a large scale.ResultsIn this paper, we proposed an deep learning-based method to predict DTIs only using the information of drug structures and protein sequences. The final results showed that our method can achieve good performance with the accuracies up to 92.0%, 90.0%, 92.0% and 90.7% for the target families of enzymes, ion channels, GPCRs and nuclear receptors of our created dataset, respectively. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded an accuracy of 0.9015 and an AUC value of 0.9557.ConclusionIt was elucidated that our model shows improved performance in comparison with other state-of-the-art computational methods on the common benchmark datasets. Experimental results demonstrated that our model successfully extracted more nuanced yet useful features, and therefore can be used as a practical tool to discover new drugs.Availabilityhttp://deeplearner.ahu.edu.cn/web/CnnDTI.htm.

Consider a retailer using sponsored search marketing together with dynamic pricing. The retailer's bid on a search keyword affects the retailer's rank among the search results. The higher the rank, the higher the customer traffic and the customers' willingness to pay will be. Thus, the question arises: When a retailer bids higher to attract more customers, should the accompanying price also decrease (to strengthen the bid's effect on demand) or increase (to take advantage of higher willingness to pay)? We find that the answer depends on how fast the retailer increases its bid. In particular, as the end of the season approaches, the optimal bid exhibits smooth increases followed by big jumps. The optimal price increases only when the optimal bid increases sharply, including the instances where the bid jumps up. Such big jumps arise, for example, when the customer traffic is an S-shaped function of the retailer's bid. This paper was accepted by Serguei Netessine, operations management.

http://www2.ahu.edu.cn/pchen/web/HotspotEC.htm .

With the recent focus on sustainability, firms making adjustments to their production or distribution capacity levels often have the option of investing in newer technologies with lower carbon footprints and/or energy consumption. These more sustainable technologies typically require a higher up-front investment but have lower operating (fuel or energy) costs. What complicates this decision is the fact that the projected dollar savings from the more sustainable technologies fluctuate considerably due to uncertainty in fuel prices, and the total capacity may not be utilized at 100% because of fluctuations in the demand for the product. We consider the firm's capacity adjustments over time given a portfolio of technology options when the demand and the fuel costs are stochastic and possibly dependent. Our model also allows for usage-based capacity deterioration. We provide the analytical structure of the optimal policy, which assigns different control limits for investing, staying put, and disinvesting in the capacities of the competing technology choices for each realization of demand and fuel costs at each period. We also present an application of our model to the problem of designing a delivery truck fleet for a beverage distributor.

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