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The entrepreneurship literature has recently become aware of the phenomenal promise of efficiency evaluation techniques for gauging one of its key concepts - arbitrage opportunities. Unfortunately, the use of DEA, the dominant efficiency evaluation approach, for this purpose is limited by some of the properties of the method. In this paper we develop an alternative method that could be used to assess opportunities for imitation (arbitrage) available to entrepreneurial firms. We adapt the minimum performance inefficiency technique to the Cobb-Douglas production function, compare the new method to the dominant efficiency estimation techniques that could be used to measure arbitrage opportunity, and run a Monte-Carlo experiment to explore its applicability to alternative types of production functions typically tackled with data envelopment analysis. We show that the new method may provide more accurate results than the mainstream approaches, and demonstrate a real-life application of the technique in the publishing industry setting.

Mobile power sources (MPSs) have promising potential for spatiotemporal flexibility exchange in power distribution systems (DS). They can be strategically employed to enhance the network resilience when facing the aftermath of high-impact low-probability (HILP) events. This article proposes a novel service restoration formulation that models and accounts for the endogenous uncertainty in the MPSs routing and scheduling decision making. The proposed restoration model is formulated as a mixed-integer nonlinear programming (MINLP) problem with nonconvex continuous relaxation. We derive computationally tractable linearization procedures to reformulate the MINLP model as an equivalent mixed-integer linear programming (MILP) problem. Case studies on the IEEE 33-node and 123-node test systems demonstrate the role of incorporating endogenous uncertainties in the decision-making process and the effectiveness of the proposed restoration scheme in boosting the DS resilience.