7,976 research outputs found
THE REAL OPTIONS PUZZLE FOR MICHIGAN TART CHERRY PRODUCERS
Capital budgeting decisions faced by tart cherry producers often challenge our traditional valuation techniques. Real Options Valuation (ROV) methods may be useful but assumptions of existing ROV approaches are restrictive and, in some cases, unrealistic. In this paper we assert that use of existing option pricing methods can not be justified. Instead, dynamic programming approach is more appropriate. We develop a multi-period model and use it to obtain an optimal orchard replacement policy. The model is applied to an example farm from Northwestern Michigan and the results provide the following messages. First, flexibility options can be estimated for individual tart cherry producers using the DP approach albeit, indirectly. Second, a farmer who uses the DP approach to develop contingency optimal replacement rules will be better off than one who uses an ad hoc standard replacement rule. Third, if the SW climate scenario shifts to NW Michigan, tart cherry orchard values may fall substantially with implications on the future of tart cherry production in that region, unless compensating price increases follow.Farm Management,
Neur2RO: Neural Two-Stage Robust Optimization
Robust optimization provides a mathematical framework for modeling and
solving decision-making problems under worst-case uncertainty. This work
addresses two-stage robust optimization (2RO) problems (also called adjustable
robust optimization), wherein first-stage and second-stage decisions are made
before and after uncertainty is realized, respectively. This results in a
nested min-max-min optimization problem which is extremely challenging
computationally, especially when the decisions are discrete. We propose
Neur2RO, an efficient machine learning-driven instantiation of
column-and-constraint generation (CCG), a classical iterative algorithm for
2RO. Specifically, we learn to estimate the value function of the second-stage
problem via a novel neural network architecture that is easy to optimize over
by design. Embedding our neural network into CCG yields high-quality solutions
quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital
budgeting. For knapsack, Neur2RO finds solutions that are within roughly
of the best-known values in a few seconds compared to the three hours of the
state-of-the-art exact branch-and-price algorithm; for larger and more complex
instances, Neur2RO finds even better solutions. For capital budgeting, Neur2RO
outperforms three variants of the -adaptability algorithm, particularly on
the largest instances, with a 5 to 10-fold reduction in solution time. Our code
and data are available at https://github.com/khalil-research/Neur2RO
Optimal enterprise risk management and decision making with shared and dependent risks
Includes bibliographical references (pages 27-29).Published as: Journal of Risk and Insurance, vol. 84, no. 4, December 2017, pp. 1127–1169. https://doi.org/10.1111/jori.12140.Dynamic enterprise risk management (ERM) entails holistic decision-making for critical corporate functions such as capital budgeting and risk management. The interplay across business divisions, however, is complicated due to their natural interactions through the shared and dependent risk exposures within an intricate corporate structure. This paper develops an integrated optimization framework via a copula-based decision tree interface to facilitate ERM decision making to meet the specified enterprise goal in a multi-period setting. We illustrate our model and provide managerial insights with a case study for a financial services company engaged in both banking and insurance businesses
Machine Learning for K-adaptability in Two-stage Robust Optimization
Two-stage robust optimization problems constitute one of the hardest
optimization problem classes. One of the solution approaches to this class of
problems is K-adaptability. This approach simultaneously seeks the best
partitioning of the uncertainty set of scenarios into K subsets, and optimizes
decisions corresponding to each of these subsets. In general case, it is solved
using the K-adaptability branch-and-bound algorithm, which requires exploration
of exponentially-growing solution trees. To accelerate finding high-quality
solutions in such trees, we propose a machine learning-based node selection
strategy. In particular, we construct a feature engineering scheme based on
general two-stage robust optimization insights that allows us to train our
machine learning tool on a database of resolved B&B trees, and to apply it
as-is to problems of different sizes and/or types. We experimentally show that
using our learned node selection strategy outperforms a vanilla, random node
selection strategy when tested on problems of the same type as the training
problems, also in case the K-value or the problem size differs from the
training ones
Risk Manage Capital Investment Decisions: A Lease vs. Purchase Illustration
This paper demonstrates how to build risk into capital investment decisions. We illustrate how to combine distribution theory, technology, and a business professional’s skills and insight into a capital investment analysis. In addition, we show how management can approximate the risk of each cash flow estimate and display the overall capital investment results. This framework is extended by showing how a mutually exclusive decision can be improved, using a lease versus purchase example.[1] An Excel template is readily available from the authors allowing a hands-on application of the framework presented in this paper. In addition, this paper positions the reader to comfortably use more advanced analytics, such as Monte Carlo simulation, a tool that is readily available in commercial software applications.This paper focuses on the application of net present value. The advantage of using net present value in a capital budgeting decision is that it shows the potential stakeholder wealth creation and wealth destruction. An internal rate of return analysis is intentionally left out of this paper. According to Brealey, Myers and Allen, Principles of Corporate Finance, New York, NY: McGraw-Hill/Irwin 2006, pp. 91-99, internal rate of return should not be used to evaluate mutually exclusive capital investments. 
Economic Analysis of Cellulosic Feedstock for Bioenergy in the Texas Rio Grande Valley
Farm Management,
A methodology for the selection of new technologies in the aviation industry
The purpose of this report is to present a technology selection methodology to
quantify both tangible and intangible benefits of certain technology
alternatives within a fuzzy environment. Specifically, it describes an
application of the theory of fuzzy sets to hierarchical structural analysis and
economic evaluations for utilisation in the industry. The report proposes a
complete methodology to accurately select new technologies. A computer based
prototype model has been developed to handle the more complex fuzzy
calculations. Decision-makers are only required to express their opinions on
comparative importance of various factors in linguistic terms rather than exact
numerical values. These linguistic variable scales, such as ‘very high’, ‘high’,
‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it
becomes more meaningful to quantify a subjective measurement into a range rather
than in an exact value. By aggregating the hierarchy, the preferential weight of
each alternative technology is found, which is called fuzzy appropriate index.
The fuzzy appropriate indices of different technologies are then ranked and
preferential ranking orders of technologies are found. From the economic
evaluation perspective, a fuzzy cash flow analysis is employed. This deals
quantitatively with imprecision or uncertainties, as the cash flows are modelled
as triangular fuzzy numbers which represent ‘the most likely possible value’,
‘the most pessimistic value’ and ‘the most optimistic value’. By using this
methodology, the ambiguities involved in the assessment data can be effectively
represented and processed to assure a more convincing and effective decision-
making process when selecting new technologies in which to invest. The prototype
model was validated with a case study within the aviation industry that ensured
it was properly configured to meet the
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