Approximate Dynamic Programming: A Melting Pot of Methods
نویسنده
چکیده
Warren Powell is Professor of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981. He is director of CASTLE Laboratory which specializes in the solution of large-scale stochastic optimization, with considerable experience in freight transportation. This work led to the development of methods to integrate mathematical programming and simulation within the framework of approximate dynamic programming, summarized in his book Approximate Dynamic Programming: Solving the Curses of Dimensionality [Wiley, 2007].
منابع مشابه
Approximate Incremental Dynamic Analysis Using Reduction of Ground Motion Records
Incremental dynamic analysis (IDA) requires the analysis of the non-linear response history of a structure for an ensemble of ground motions, each scaled to multiple levels of intensity and selected to cover the entire range of structural response. Recognizing that IDA of practical structures is computationally demanding, an approximate procedure based on the reduction of the number of ground m...
متن کاملOPTIMIZATION OF A PRODUCTION LOT SIZING PROBLEM WITH QUANTITY DISCOUNT
Dynamic lot sizing problem is one of the significant problem in industrial units and it has been considered by many researchers. Considering the quantity discount in purchasing cost is one of the important and practical assumptions in the field of inventory control models and it has been less focused in terms of stochastic version of dynamic lot sizing problem. In this paper, stochastic dyn...
متن کاملExpected Duration of Dynamic Markov PERT Networks
Abstract : In this paper , we apply the stochastic dynamic programming to approximate the mean project completion time in dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in ac...
متن کاملOn Sequential Optimality Conditions without Constraint Qualifications for Nonlinear Programming with Nonsmooth Convex Objective Functions
Sequential optimality conditions provide adequate theoretical tools to justify stopping criteria for nonlinear programming solvers. Here, nonsmooth approximate gradient projection and complementary approximate Karush-Kuhn-Tucker conditions are presented. These sequential optimality conditions are satisfied by local minimizers of optimization problems independently of the fulfillment of constrai...
متن کاملBankruptcy Prediction: Dynamic Geometric Genetic Programming (DGGP) Approach
In this paper, a new Dynamic Geometric Genetic Programming (DGGP) technique is applied to empirical analysis of financial ratios and bankruptcy prediction. Financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms’ impending failure for investors, creditors, borrowing firms, and governments. By the time, several methods have been attempted in...
متن کامل