32,103 research outputs found
Stochastic utility-efficient programming of organic dairy farms
Opportunities to make sequential decisions and adjust activities as a season progresses and more information becomes available characterise the farm management process. In this paper, we present a discrete stochastic two-stage utility efficient programming model of organic dairy farms, which includes risk aversion in the decision maker’s objective function as well as both embedded risk (stochastic programming with recourse) and non-embedded risk (stochastic programming without recourse). Historical farm accountancy data and subjective judgements were combined to assess the nature of the uncertainty that affects the possible consequences of the decisions. The programming model was used within a stochastic dominance framework to examine optimal strategies in organic dairy systems in Norway
K-Adaptability in Two-Stage Distributionally Robust Binary Programming
We propose to approximate two-stage distributionally robust programs with binary recourse decisions by their associated K-adaptability problems, which pre-select K candidate secondstage policies here-and-now and implement the best of these policies once the uncertain parameters have been observed. We analyze the approximation quality and the computational complexity of the K-adaptability problem, and we derive explicit mixed-integer linear programming reformulations. We also provide efficient procedures for bounding the probabilities with which each of the K second-stage policies is selected
Stochastic Utility-Efficient Programming of Organic Dairy Farms
Opportunities to make sequential decisions and adjust activities as a season progresses and more information becomes available characterize the farm management process. In this paper, we present a discrete stochastic two-stage utility efficient programming model of organic dairy farms, which includes risk aversion in the decision maker's objective function as well as both embedded risk (stochastic programming with resource) and non-embedded risk (stochastic programming without recourse). Historical farm accountancy data and subjective judgments were combined to assess the nature of the uncertainty that affects the possible consequences of the decisions. The programming model was used within a stochastic dominance framework to examine optimal strategies in organic dairy systems in Norway.agriculture, risk analysis, stochastic programming, stochastic dominance, organic farming, Livestock Production/Industries, Q12, C61,
Commitment and Dispatch of Heat and Power Units via Affinely Adjustable Robust Optimization
The joint management of heat and power systems is believed to be key to the
integration of renewables into energy systems with a large penetration of
district heating. Determining the day-ahead unit commitment and production
schedules for these systems is an optimization problem subject to uncertainty
stemming from the unpredictability of demand and prices for heat and
electricity. Furthermore, owing to the dynamic features of production and heat
storage units as well as to the length and granularity of the optimization
horizon (e.g., one whole day with hourly resolution), this problem is in
essence a multi-stage one. We propose a formulation based on robust
optimization where recourse decisions are approximated as linear or
piecewise-linear functions of the uncertain parameters. This approach allows
for a rigorous modeling of the uncertainty in multi-stage decision-making
without compromising computational tractability. We perform an extensive
numerical study based on data from the Copenhagen area in Denmark, which
highlights important features of the proposed model. Firstly, we illustrate
commitment and dispatch choices that increase conservativeness in the robust
optimization approach. Secondly, we appraise the gain obtained by switching
from linear to piecewise-linear decision rules within robust optimization.
Furthermore, we give directions for selecting the parameters defining the
uncertainty set (size, budget) and assess the resulting trade-off between
average profit and conservativeness of the solution. Finally, we perform a
thorough comparison with competing models based on deterministic optimization
and stochastic programming.Comment: 31 page
Electricity Market Equilibrium under Information Asymmetry
We study a competitive electricity market equilibrium with two trading
stages, day-ahead and real-time. The welfare of each market agent is exposed to
uncertainty (here from renewable energy production), while agent information on
the probability distribution of this uncertainty is not identical at the
day-ahead stage. We show a high sensitivity of the equilibrium solution to the
level of information asymmetry and demonstrate economic, operational, and
computational value for the system stemming from potential information sharing
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Software tools for stochastic programming: A Stochastic Programming Integrated Environment (SPInE)
SP models combine the paradigm of dynamic linear programming with
modelling of random parameters, providing optimal decisions which hedge
against future uncertainties. Advances in hardware as well as software
techniques and solution methods have made SP a viable optimisation tool.
We identify a growing need for modelling systems which support the creation
and investigation of SP problems. Our SPInE system integrates a number of
components which include a flexible modelling tool (based on stochastic
extensions of the algebraic modelling languages AMPL and MPL), stochastic
solvers, as well as special purpose scenario generators and database tools.
We introduce an asset/liability management model and illustrate how SPInE
can be used to create and process this model as a multistage SP application
PRODUCTION PRACTICE ALTERNATIVES FOR INCOME AND SUITABLE FIELD DAY RISK MANAGEMENT
Production risk includes yield and days suitable for fieldwork variability. Both were modeled using biophysical simulation and a mean-variance, chance-constrained mathematical programming formulation representing a Kentucky corn, soybean, and wheat producer. While crop diversification, planting date, and maturity group can be used to reduce the types of risk considered, interaction between the two influences how production practices are used to manage risk. For the conditions studied, plant population alterations were less effective for risk reduction of either component. The study provides evidence of the importance of the consideration of both elements of production risk in whole farm planning.days suitable for fieldwork, mathematical programming, risk management, Crop Production/Industries, Risk and Uncertainty,
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