Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Tra-ditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load sce-narios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessi-tates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation method-ology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit com-mitment solvers, as described in the companion paper. The scenarios generated by our method are available as an online supplement to this paper, as part of a novel, publicly available large-scale stochastic unit commitment benchmark. Abstract Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Traditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load scenarios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessitates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation methodology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit commitment solvers, as described in the companion paper. The scenarios generated Disciplines Industrial Engineering | Systems Engineering

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Artículo de publicación ISIA new methodology is laid out for the modeling of commodity prices, it departs from the ‘standard’ approach in that it makes a definite distinction between the analysis of the short term and long term regimes. In particular, this allows us to come up with an explicit drift term for the short-term process whereas the long-term process is primarily driftless due to inherent high volatility of commodity prices excluding an almost negligible mean reversion term. Not unexpectedly, the information used to build the short-term process relies on more than just historical prices but takes into account additional information about the state of the market. This work is done in the context of copper prices but a similar approach should be applicable to wide variety of commodities although certainly not all since commodities come with very distinct characteristics. In addition, our model also takes into account inflation which leads us to consider a multi-dimensional system for which one can generate explicit solutions

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We analyse how to deal with the uncertainty before solving a stochastic optimization problem and we apply it to a forestry management problem. In particular, we start from historical data to build a stochastic process for wood prices and for bounds on its demand. Then, we generate scenario trees considering different numbers of scenarios and different scenario-generation methods, and we describe a procedure to compare the solutions obtained with each approach. Finally, we show that the scenario tree used to obtain a candidate solution has a considerable impact in our decision modelComplex Engineering Systems Institute ICM:P-05-004-F CONICYT: FBO16 AND under Fondecyt 112031

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Motivated by recent initiatives to increase transparency in procurement, we study the effects of disclosing information about previous purchases in a setting where an organization delegates its purchasing decisions to its employees. When employees can use their own discretion, which may be influenced by personal preferences, to select a supplier, the incentives of the employees and the organization may be misaligned. Disclosing information about previous purchasing decisions made by other employees can reduce or exacerbate this misalignment, as peer effects may come into play. To understand the effects of transparency, we introduce a theoretical model that compares employees’ actions in two settings: one where employees cannot observe each other’s choices and one where they can observe the decision previously made by a peer before making their own. Two behavioral assumptions are central to our model: that employees are heterogeneous in their reciprocity toward their employer, and that they experience peer effects in the form of income inequality aversion toward their peer. As a result, our model predicts the existence of negative spillovers as a reciprocal employee is more likely to choose the expensive supplier (which gives the employee a personal reward) when the employee observes that a peer did so. A laboratory experiment confirms the existence of negative spillovers and the main behavioral mechanisms described in our model. A surprising result not predicted by our theory is that employees whose decisions are observed by their peers are less likely to choose the expensive supplier than the employees in the no-transparency case. We show that observed employees’ preferences for compliance with the social norm of appropriate purchasing behavior explain our data well. This paper was accepted by Yuval Victor Martinez de Albeniz, operations management.