We study the resource investment decision faced by a firm that offers two demandclasses (i.e., products, services), while incorporating the firm's pricing decision into the investment decision. For this purpose, we consider a monopolistic situation and model the demand curve of each demand-class as a downward sloping linear function of its own price.The firm can invest in dedicated resources, which can only satisfy a specific demand-class, and/or in a more expensive, flexible resource, which can satisfy both demand-classes.We consider a two-stage stochastic decision model: In the first stage, the firm determines the dedicated and flexible resource capacities to invest in under demand uncertainty.In the second stage, demand curves are realized and the firm optimizes its revenue through pricing and resource allocation decisions, constrained by its capacity investment decision in the first stage.Our analysis provides the structure of the firm's optimal resource investment strategy as a function of price elasticities and investment costs, and shows how the value of resource flexibility depends on these parameters and demand correlations. Based on our analysis, we provide principles on the firm's optimal resource investment strategy under uncertainty.We show that it can be optimal for the firm to invest in the flexible resource when demand patterns are perfectly positively correlated, while it is not always optimal to invest in the flexible resource when demand patterns are perfectly negatively correlated.

No abstract

No abstract

No abstract

In many countries, unreliable inputs, particularly those lacking storage, can significantly limit a firm's productivity. In the case of an increasing frequency of blackouts, a firm may change factor shares in a number of ways. It may decide to self generate electricity, to purchase intermediate goods that it used to produce directly, or to improve its technical efficiency. We examine how industrial firms responded to China's severe power shortages in the early 2000s. Fast-growing demand coupled with regulated electricity prices led to blackouts that varied in degree over location and time. Our data consist of annual observations from 1999 to 2004 for approximately 32,000 energy-intensive, enterprises from all industries. We estimate the losses in productivity due to factor-neutral and factor-biased effects of electricity scarcity. Our results suggest that enterprises re-optimize among factors in response to electricity scarcity by shifting from energy (both electric and non-electric sources) into materials-a shift from "make" to "buy." These effects are strongest for firms in textiles, timber, chemicals, and metals. Contrary to the literature, we do not find evidence of an increase in self generation. Finally, we find that these productivity changes, while costly to firms, led to small reductions in carbon emissions.

We present a novel end-to-end network, MANet, for light field depth estimation. MANet is a parameter-effective and efficient multi-scale aggregated network, which is about 3 times smaller and 3 times faster than the current top-performing method Epinet. The MANet architecture is performed for estimating depth from light field plenoptic cameras, and experimental results show that the proposed MANet outperforms state-of-the-art methods on HCI, CVIA-HCI and EPFL Lytro light field datasets.

No abstract

No abstract