stochastic programming

Resource Allocation for Contingency Planning: An Inexact Bundle Method for Stochastic Optimization

  • Ricardo A. Collado
  • Somayeh Moazeni
    • Categories Nonsmooth Optimization, Production and Logistics, Stochastic Programming Tags , , , ,

    Resource allocation models in contingency planning aim to mitigate unexpected failures in supply chains due to disruptions with rare occurrence but disastrous consequences. This paper formulates this problems as a two-stage stochastic optimization with a risk-averse recourse function, and proposes a novel computationally tractable solution approach. The method relies on an inexact bundle method and … Read more

    Modeling Time-dependent Randomness in Stochastic Dual Dynamic Programming

  • Nils Löhndorf
  • Alexander Shapiro
    • Categories Stochastic Programming Tags , , ,

    We consider the multistage stochastic programming problem where uncertainty enters the right-hand sides of the problem. Stochastic Dual Dynamic Programming (SDDP) is a popular method to solve such problems under the assumption that the random data process is stagewise independent. There exist two approaches to incorporate dependence into SDDP. One approach is to model the … Read more

    New solution approaches for the maximum-reliability stochastic network interdiction problem

  • James R. Luedtke
  • Eli Towle
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Stochastic Programming Tags , ,

    We investigate methods to solve the maximum-reliability stochastic network interdiction problem (SNIP). In this problem, a defender interdicts arcs on a directed graph to minimize an attacker’s probability of undetected traversal through the network. The attacker’s origin and destination are unknown to the defender and assumed to be random. SNIP can be formulated as a … Read more

    Payment Mechanisms for Electricity Markets with Uncertain Supply

  • Ryan Cory-Wright
  • Andrew B. Philpott
  • Golbon Zakeri
    • Categories Finance and Economics, Game Theory, Stochastic Programming Tags , , ,

    This paper provides a framework for deriving payment mechanisms for intermittent, flexible and inflexible electricity generators who are dispatched according to the optimal solution of a stochastic program that minimizes the expected cost of generation plus deviation. The first stage corresponds to a pre-commitment decision, and the second stage corresponds to real-time generation that adapts … Read more

    Optimal scenario generation and reduction in stochastic programming

  • René Henrion
  • Werner Romisch
    • Categories Semi-infinite Programming, Stochastic Programming Tags , , ,

    Scenarios are indispensable ingredients for the numerical solution of stochastic optimization problems. Earlier approaches for optimal scenario generation and reduction are based on stability arguments involving distances of probability measures. In this paper we review those ideas and suggest to make use of stability estimates based on distances containing minimal information, i.e., on data appearing … Read more

    Learning Enabled Optimization: Towards a Fusion of Statistical Learning and Stochastic Optimization

  • Yunxiao Deng
  • Suvrajeet Sen
    • Categories Problem Solving Environments, Statistics, Stochastic Programming Tags , , ,

    Several emerging applications, such as “Analytics of Things” and “Integrative Analytics” call for a fusion of statistical learning (SL) and stochastic optimization (SO). The Learning Enabled Optimization paradigm fuses concepts from these disciplines in a manner which not only enriches both SL and SO, but also provides a framework which supports rapid model updates and … Read more

    Forecast-based scenario-tree generation method

  • Michal Kaut
    • Categories Stochastic Programming Tags ,

    Sometimes, the best available information about an uncertain future is a single forecast. On the other hand, stochastic-programming models need future data in the form of scenario trees. While a single forecast does not provide enough information to construct a scenario tree, a forecast combined with historical data does—but none of the standard scenario-generation methods … Read more

    Communication-Efficient Algorithms for Decentralized and Stochastic Optimization

  • Guanghui Lan
  • Soomin Lee
  • Yi Zhou
    • Categories Convex and Nonsmooth Optimization, Stochastic Programming Tags , , , , , ,

    We present a new class of decentralized first-order methods for nonsmooth and stochastic optimization problems defined over multiagent networks. Considering that communication is a major bottleneck in decentralized optimization, our main goal in this paper is to develop algorithmic frameworks which can significantly reduce the number of inter-node communications. We first propose a decentralized primal-dual … Read more

    Second-order cone programming formulation for two player zero-sum game with chance constraints

  • Abdel Lisser
  • Vikas Vikram Singh
    • Categories Game Theory, Stochastic Programming Tags , , , ,

    We consider a two player finite strategic zero-sum game where each player has stochastic linear constraints. We formulate the stochastic constraints of each player as chance constraints. We show the existence of a saddle point equilibrium if the row vectors of the random matrices, defining the stochastic constraints of each player, are elliptically symmetric distributed … Read more

    Combining Penalty-based and Gauss-Seidel Methods for solving Stochastic Mixed-Integer Problems

  • Eberhard Andrew
  • Brian Dandurand
  • Fabricio Oliveira
  • Jeffrey Christiansen
    • Categories Stochastic Programming Tags , , , ,

    In this paper, we propose a novel decomposition approach for mixed-integer stochastic programming (SMIP) problems that is inspired by the combination of penalty-based Lagrangian and block Gauss-Seidel methods (PBGS). In this sense, PBGS is developed such that the inherent decomposable structure that SMIPs present can be exploited in a computationally efficient manner. The performance of … Read more