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Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization
We revisit an approximate stochastic dynamic programming method that we proposed earlier for the optimization of multireservoir problems. The method...
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A robust stochastic possibilistic programming model for dynamic supply chain network design with pricing and technology selection decisions
This work considers a multi-period multi-echelon multi-product dynamic supply chain network design problem for both strategic and tactical decisions....
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Approximation of multistage stochastic programming problems by smoothed quantization
We present an approximation technique for solving multistage stochastic programming problems with an underlying Markov stochastic process. This...
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Dynamic Programming
Dynamic Programming, or dynamic optimization, is an optimization approach that simplifies complex problems by breaking them into smaller,...
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A Novel Approach to Solve Multi-objective Fuzzy Stochastic Bilevel Programming Using Genetic Algorithm
A bilevel programming is a two-level optimization problem, namely, the upper level (leaders) and the lower level (followers). The two level’s...
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Incorporating convex risk measures into multistage stochastic programming algorithms
Over the last two decades, coherent risk measures have been well studied as a principled, axiomatic way to characterize the risk of a random...
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Parallel and distributed computing for stochastic dual dynamic programming
We study different parallelization schemes for the stochastic dual dynamic programming (SDDP) algorithm. We propose a taxonomy for these parallel...
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A tutorial on value function approximation for stochastic and dynamic transportation
This paper provides an introductory tutorial on Value Function Approximation (VFA), a solution class from Approximate Dynamic Programming. VFA...
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Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach
To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of...
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Modelling medical oxygen supply chain network under demand uncertainty using stochastic programming
Supply chains are becoming more and more uncertain. It is more relevant now than ever to plan and model supply chains to handle such uncertainties....
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The dynamic stochastic container drayage problem with truck appointment scheduling
In this work, a stochastic dynamic version of the container drayage problem is studied. The presented model incorporates uncertainty in the form of...
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On the optimization of pit stop strategies via dynamic programming
Pit stops are a key element of racing strategy in several motor sports. Typically, these stops involve decisions such as in which laps to stop, and...
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Multi-stage scenario-based stochastic programming for managing lot sizing and workforce scheduling at Vestel
This study proposes a multi-stage stochastic production planning approach for a joint lot sizing and workforce scheduling problem under demand...
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A fix and optimize method based approximate dynamic programming approach for the strategic fleet sizing and delivery planning problem
Logistics related costs constitute a major part in total cost of a product in general. Considering a company that delivers goods to its customers...
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Single cut and multicut stochastic dual dynamic programming with cut selection for multistage stochastic linear programs: convergence proof and numerical experiments
We introduce a variant of Multicut Decomposition Algorithms, called CuSMuDA (Cut Selection for Multicut Decomposition Algorithms), for solving...
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Balancing resources for dynamic vehicle routing with stochastic customer requests
We consider a service provider performing pre-planned service for initially known customers with a fleet of vehicles, e.g., parcel delivery. During...
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Anticipatory scheduling of synchromodal transport using approximate dynamic programming
We study the problem of scheduling container transport in synchromodal networks considering stochastic demand. In synchromodal networks, the...
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Predictive stochastic programming
Several emerging applications call for a fusion of statistical learning and stochastic programming (SP). We introduce a new class of models which we...
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Optimal chance-constrained pension fund management through dynamic stochastic control
We apply a dynamic stochastic control (DSC) approach based on an open-loop linear feedback policy to a classical asset-liability management problem...
