Provider: Ingenta Connect
Database: Ingenta Connect
Content: application/x-research-info-systems
TY - ABST
AU - Hof, John G.
AU - Pickens, James B.
TI - Chance-Constrained and Chance-Maximizing Mathematical Programs in Renewable Resource Management
JO - Forest Science
PY - 1991-03-01T00:00:00///
VL - 37
IS - 1
SP - 308
EP - 325
KW - nonlinear programming
KW - random right-hand sides
KW - risk and uncertainty
KW - stochastic models
KW - Linear programming
N2 - This paper discusses a broad range of approaches to optimizing natural resource allocation in the situation where amounts of available input(s) and/or amounts of desired output(s) are random. The paper begins by reviewing, the classic approach to this class of problems, "chance-constrained programming," followed by a discussion of "joint probability chance-constrained programming," and a new alternative "total probability chance-constrained programming." The paper then develops three "chance-maximizing" counterparts. Specific formulations for executing these approaches in natural resource management problems are developed that explicitly include the cumulative probability density functions. A forestry case example demonstrates .solution procedures and shows that the different approaches can yield substantially different results. Results from different solution procedures are also compared. For. Sci. 37(1):308-325.
UR - http://www.ingentaconnect.com/content/saf/fs/1991/00000037/00000001/art00023
ER -