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Risk management for forestry planning under uncertainty in demand and prices.

Antonio Alonso-Ayuso (antonio.alonso***at***urjc.es)
Laureano F. Escudero (laureano.escudero***at***urjc.es)
Monique Guignard (guignard_monique***at***yahoo.fr)
Andres Weintraub (aweintra***at***dii.uchile.cl)

Abstract: The forest-harvesting and road-construction planning problem basically consists of managing land designated for timber production and divided into harvest cells. For each time period in the given time horizon one must decide which cells to cut and what access roads to build in order to maximize expected net profit under a risk manageable scheme to control the negative impact of the solutions in the objective function value of the unwanted scenarios (i.e., the so-called black swans) on the objective function value. We have previously developed deterministic and risk neutral stochastic mixed 0-1 linear optimization models for similar problems. The stochastic version of the problem that we presented there enables the planner to make more robust decisions based on a range of timber price scenarios over time, maximizing the expected value instead of merely analyzing a single (e.g., average) scenario as performed in the deterministic version of the problem. The main contribution of the current work consists of introducing the so-called time consistent and time inconsistent Conditional Value-at-Risk (CVaR) risk-averse measures in forestry planning. They avoid the risk neutral optimal (expected profit) solutions with low probability high variability in the profit scenario. In particular, those risk-averse measures are compared computationally with the risk neutral one under different price, demand and probability of the scenarios under consideration, as well as with a risk-averse measure that is a mixture of both.

Keywords: OR in natural resources, forestry planning, optimization under uncertainty, risk management, consistency in risk-averse measures

Category 1: Applications -- OR and Management Sciences

Citation: 43 citations


Entry Submitted: 12/05/2016
Entry Accepted: 12/05/2016
Entry Last Modified: 08/27/2017

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