Anticipatory Optimization for Dynamic Decision Making by Stephan Meisel

Posted by

By Stephan Meisel

The availability of today’s on-line info structures speedily raises the relevance of dynamic choice making inside of numerous operational contexts. each time a series of interdependent judgements happens, creating a unmarried determination increases the necessity for anticipation of its destiny effect at the whole choice strategy. Anticipatory help is required for a large number of dynamic and stochastic determination difficulties from diverse operational contexts reminiscent of finance, power administration, production and transportation. instance difficulties contain asset allocation, feed-in of electrical energy produced via wind energy in addition to scheduling and routing. a lot of these difficulties entail a chain of selections contributing to an total aim and happening during a definite time period. all of the judgements is derived via resolution of an optimization challenge. therefore a stochastic and dynamic selection challenge resolves right into a sequence of optimization difficulties to be formulated and solved by way of anticipation of the rest selection process.

However, truly fixing a dynamic determination challenge through approximate dynamic programming nonetheless is a massive medical problem. many of the paintings performed to this point is dedicated to difficulties taking into account formula of the underlying optimization difficulties as linear courses. challenge domain names like scheduling and routing, the place linear programming usually doesn't produce an important profit for challenge fixing, haven't been thought of to date. accordingly, the call for for dynamic scheduling and routing remains to be predominantly happy via basically heuristic ways to anticipatory selection making. even if this can paintings good for sure dynamic choice difficulties, those ways lack transferability of findings to different, similar problems.

This booklet has serves significant purposes:

‐ It offers a finished and designated view of anticipatory optimization for dynamic determination making. It totally integrates Markov selection techniques, dynamic programming, information mining and optimization and introduces a brand new viewpoint on approximate dynamic programming. furthermore, the ebook identifies diverse levels of anticipation, allowing an review of particular ways to dynamic selection making.

‐ It exhibits for the 1st time find out how to effectively clear up a dynamic car routing challenge via approximate dynamic programming. It elaborates on each construction block required for this type of method of dynamic car routing. Thereby the booklet has a pioneering personality and is meant to supply a footing for the dynamic car routing community.

Show description

Read or Download Anticipatory Optimization for Dynamic Decision Making PDF

Similar decision-making & problem solving books

Prospect Theory: For Risk and Ambiguity

Prospect concept: For possibility and Ambiguity presents the 1st entire and available textbook therapy of ways judgements are made either after we have the statistical chances linked to doubtful destiny occasions (risk) and after we lack them (ambiguity). The e-book offers versions, basically prospect thought, which are either tractable and psychologically lifelike.

Discount Business Strategy: How the New Market Leaders are Redefining Business Strategy

What everyone is announcing approximately company Strategy:"Michael Andersen and Flemming Poulfelt offer a provocative dialogue of the quickly starting to be function of discounters throughout a number of industries: how they function; how they bring about forte; and the way they could smash price for incumbents. realizing the categorical strikes and instruments that the authors examine may be priceless for attackers and incumbents alike.

Supply Chain Optimization through Segmentation and Analytics

We are living in a global the place we attempt to resolve comparable difficulties in structurally an identical method. yet they only aren't optimally solved all of the related. offer Chain Optimization via Segmentation and Analytics addresses the problem of optimizing the making plans and scheduling approach and asks the query; "Is there a ‘one measurement suits all’ answer for making plans and scheduling?

Additional info for Anticipatory Optimization for Dynamic Decision Making

Example text

12 by means of a Robbins Monro algorithm is given by definition of a function f (Vtπ ) = Vtπ − E[Vtπ − Z] . Applying f (Vtπ ) = Vtπ results in E[Vtπ − Z] = 0. Note that definition of a function g with g(Vtπ ) = E 12 (Vtπ − Z)2 implies E[Vtπ − Z] = ∇g(Vtπ ) = 0 . It is now straightforward to establish a stochastic approximation method with M = 1 that leads to the value Vtπ satisfying ∇g(Vtπ ) = 0. Provided a number of sample realizations zi the resulting Robbins Monro algorithm is Vˆtπ ,n+1 := (1 − γ )Vˆ π ,n + γ zi .

1 The interaction of actor and critic. often characterized as actor-critic methods. The notions of “actor” and “critic” reflect the idea of an actor following a policy to make decisions and a critic gradually evaluating the current policy. 7 The critic takes into account the state transitions triggered by the actor’s decisions and estimates the value function of the actor’s policy. From time to time, the critic’s current estimates Vˆ π ,n are communicated to the actor, causing an update of the policy used for decision making.

The most important difference between the two, apart from the fact that Q-Factors are estimated instead of a value function V π (s), consists in the way a decision is made. While an actor-critic method needs to evaluate the expectation of the value of the successor state, Q-Learning simply derives a decision by solution of dt = arg max Qˆ tn−1 (st , dt ) . 24 converges to the optimal Q-Factors. Note that in the presence of optimal Q-Factors an anitcipatory decision no longer requires solution of a stochastic optimization problem Pt (cf.

Download PDF sample

Rated 4.90 of 5 – based on 27 votes