Stochastic Depletion Problems: Effective Myopic Policies for a Class of Dynamic Optimization Problems
Stochastic Depletion Problems: Effective Myopic Policies for a Class of Dynamic Optimization Problems
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DOI：
10.1287/moor.1080.0364
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发表时间：
2008-01
期刊：
影响因子：
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通讯作者：
Carri W. Chan;V. Farias
中科院分区：
文献类型：
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作者：
Carri W. Chan;V. Farias
本文提出了一类通用的动态随机优化问题，我们称之为随机耗尽问题。许多具有实际意义的具有挑战性的动态优化问题都是随机消耗问题。无论从实用的计算角度还是从理论角度来看，此类问题的最佳解决方案都很难获得。因此，简单的启发式方法是可取的。我们分离出两个简单的属性，如果此类问题满足这些属性，则保证短视策略相对于该问题的最优自适应控制策略最多会导致 50% 的性能损失。我们能够验证这两个属性对于几个有趣的随机耗尽问题族来说是满足的，因此，我们为许多有趣的动态随机优化问题确定了计算有效的最优控制策略近似。
This paper presents a general class of dynamic stochastic optimization problems we refer to as stochastic depletion problems. A number of challenging dynamic optimization problems of practical interest are stochastic depletion problems. Optimal solutions for such problems are difficult to obtain, both from a pragmatic computational perspective as well as from a theoretical perspective. As such, simple heuristics are desirable. We isolate two simple properties that, if satisfied by a problem within this class, guarantee that a myopic policy incurs a performance loss of at most 50% relative to the optimal adaptive control policy for that problem. We are able to verify that these two properties are satisfied for several interesting families of stochastic depletion problems and, as a consequence, we identify computationally efficient approximations to optimal control policies for a number of interesting dynamic stochastic optimization problems.