Dynamic And Stochastic Multi Project Planning

Author: Philipp Melchiors
Publisher: Springer
ISBN: 3319045407
Size: 17.63 MB
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Stochastic dynamic programming and the control of queueing systems (Wiley
series in probability and statistics). New York: Wiley. Slotnick, S., & Morton, T. (
2007). ... Exact algorithms for a generalization of the order acceptance and
scheduling problem in a single-machine environment. Computers and
Operations Research, 38(1), 367–378. ... Approximate dynamic programming for
networks: Fluid models and constraint reduction. Technical report, Gordon
College. Veatch, M. H. ...

Advances In Stochastic Dynamic Programming For Operations Management

Author: Frank Schneider
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832536337
Size: 10.80 MB
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In all three chapters we considered complex stochastic planning problems, in
which our objective was to provide some kind of service with defined
characteristics at minimum cost. ... This is the core discipline of operations
management; operating production and service processes optimally. While in
Chapter ... The field of self-learning algorithms, to which approximate dynamic
programming belongs, provides relatively new approaches to cope with highly
complex decision problems.

Re Engineering Manufacturing For Sustainability

Author: Andrew Y. C. Nee
Publisher: Springer Science & Business Media
ISBN: 9814451487
Size: 66.44 MB
Format: PDF, Mobi
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We assume this threshold policy is also applicable to the multi-period problem.
Since the last period of the multi-period problem is actually a single period
problem, we can start from the last period and take advantage of the threshold
levels obtained previously. 4.1 An Approximate Dynamic Programming Approach
In the multi-period two-product recovery problem, returns arrive at the beginning
of each period. Production and recovery decisions are made and the inventories
of ...

Encyclopedia Of Optimization

Publisher: Springer Science & Business Media
ISBN: 0792369327
Size: 32.36 MB
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diagrams in facility location; Warehouse location problem) production functions [
49-XX, 60Jxx, 65Ux, 91B32, 92D30, 93-XX] (see: Resource allocation for
epidemic control) production planning [90-01, 90B30, 90B50, 91B32, 91B52,
91B74J (see; Bilevel programming in management) production realizing with
minimal social cost [90C33] (see: Order complementarity) production set [90B30,
90B50, 90C05, 91B82] (see. Data envelopment analysis) productivity see:
maximization of ...

Introduction To Stochastic Programming

Author: John R. Birge
Publisher: Springer Science & Business Media
ISBN: 1461402379
Size: 20.83 MB
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426 10.4 Multistage Sampling and Decomposition Methods . . . . . . . . . . . . . . . 432
10.5 Approximate Dynamic Programming and Special Cases . . . . . . . . . . 436 a.
Network revenue management . . . . . . . . . . . . . . . . . . . . . . . . . 438 b. Vehicle
allocation problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 c. Piecewise-linear
separable bounds . . . . . . . . . . . . . . . . . . . . . . 441 d. Nonlinear bounds and a
production planning example . . xviii Contents.

Decision Theory Models For Applications In Artificial Intelligence Concepts And Solutions

Author: Sucar, L. Enrique
Publisher: IGI Global
ISBN: 160960167X
Size: 23.39 MB
Format: PDF
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Problems where this situation arises include capacity planning, production
planning, transportation and logistics, financial management, and others (
Wallace & Ziemba, 2005). ... where complex objectives and potentially conflicting
requirements have to be taken into account — seems to be growing in the
machine learning community, as indicated by a series of advances in
approximate dynamic programming motivated by such applications (Powell,
2007; Csáji & Monostori, 2008).

Production Planning By Mixed Integer Programming

Author: Yves Pochet
Publisher: Springer Science & Business Media
ISBN: 0387299599
Size: 19.27 MB
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Exercise 7.8 Use the multicommodity reformulation of LS-U, introduced in Section
4.1.1, to construct an approximate reformulation with similar properties to those of
the approximate shortest path formulation of Section 7.6. ... Section 7.3 In [188],
Wagner and Whitin presented an O(n2) dynamic programming algorithm for LS-U
. In the early 1990s, several ways of solving LS-U in O(nlogn) were developed,
see Aggarwal and Park [3], Federgrün and Tzur [63], and Wagelmans et al. [187].

Deterministic Lotsizing Models For Production Planning

Author: Marc Salomon
Publisher: Springer Science & Business Media
ISBN: 3642516491
Size: 22.15 MB
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Chapter 5 The Multi-item DLSP 5.1 Introduction In this chapter we consider
algorithms for solving the multi-item DLSP. In Section 5.2 solution procedures are
discussed, based on straightforward application of dynamic programming.
Although these algorithms give exact solutions, the computation times and the
required memory grow rapidly with the size of the problem. Therefore, alternative
(approximation) algorithms are considered, that give acceptable solutions in
reasonable ...

Stochastic Programming

Author: Gerd Infanger
Publisher: Springer Science & Business Media
ISBN: 9781441916426
Size: 70.56 MB
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Chapter 14 (by Higle and Kempf) develops a multistage stochastic programming
model for production planning, where both supplies and demands are
considered uncertain and driven by stochastic processes represented by Markov
chains. Chapter 15 on analyzing climate ... Another approximation is approximate
dynamic programming, where the recourse (or value-to-go) function of a dynamic
program is approximated via a linear combination of basis functions. Value
function ...