可再生资源优化中的随机产量系数的偶然性约束和偶然性最大化

IF 1.5 4区农林科学 Q2 FORESTRY

Forest Science Pub Date : 2024-04-10 DOI:10.1093/forestscience/38.2.305

John G. Hof, Brian M. Kent, James B. Pickens

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引用次数: 0

摘要

本文讨论了在可再生资源优化模型中考虑具有已知均值和方差的随机产量系数的各种方法。首先讨论了一般公式，然后以一个林业案例为例，演示了可再生资源应用中的公式和由此产生的最优解。通过模拟对近似正态累积密度函数的不同方法进行了评估。For.科学》38（2）：305-323。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Chance Constraints and Chance Maximization with Random Yield Coefficients in Renewable Resource Optimization

This paper treats a variety of approaches to account for random yield coefficients with known means and variances in renewable resource optimization models. General formulations are discussed first, followed by a forestry case example that demonstrates the formulations and resulting optimal solutions in a renewable resource application. Different approaches to approximating the normal cumulative density function are evaluated using simulation. For. Sci. 38(2):305-323.

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来源期刊

Forest Science 农林科学-林学

CiteScore

2.80

自引率

7.10%

发文量

审稿时长

3 months

期刊介绍： Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management. Forest Science is published bimonthly in February, April, June, August, October, and December.