Stochastic Programming

A Sequential Quadratic Programming Method for Optimization with Stochastic Objective Functions, Deterministic Inequality Constraints and Robust Subproblems

  • Songqiang Qiu
  • Vyacheslav Kungurtsev
    • Categories Constrained Nonlinear Optimization, Other Topics, Stochastic Programming Tags , , ,

    In this paper, a robust sequential quadratic programming method of Burke and Han (Math Programming, 1989)  for constrained optimization is generalized to problem with stochastic objective function, deterministic equality and inequality constraints. A stochastic line search scheme in Paquette and Scheinberg (SIOPT, 2020) is employed to globalize the steps. We show that in the case … Read more

    Bilevel optimization with a multi-objective lower-level problem: Risk-neutral and risk-averse formulations

  • Tommaso Giovannelli
  • Griffin D. Kent
  • Luis Nunes Vicente
    • Categories Data-Mining, Multi-Criteria Optimization, Stochastic Programming Tags , , ,

    In this work, we propose different formulations and gradient-based algorithms for deterministic and stochastic bilevel problems with conflicting objectives in the lower level. Such problems have received little attention in the deterministic case and have never been studied from a stochastic approximation viewpoint despite the recent advances in stochastic methods for single-level, bilevel, and multi-objective … Read more

    Stochastic programming for an integrated assignment, routing, and scheduling problem

  • Syu-Ning Johnn
  • Andrés Miniguano-Trujillo
  • Yiran Zhu
  • Akshay Gupte
  • Joerg Kalcsics
    • Categories Applications - OR and Management Sciences, Combinatorial Optimization, Stochastic Programming Tags , , ,

    We study a two-stage stochastic combinatorial optimization problem that integrates fleet-sizing, assignment, routing, and scheduling problems. Although this problem has wide applicability, it arises in particular in the home healthcare industry where a service team of caregivers have to be assigned to patients and put in vehicle fleet that have to be routed amongst the … Read more

    ALSO-X#: Better Convex Approximations for Distributionally Robust Chance Constrained Programs

  • Nan Jiang
  • Weijun Xie
    • Categories Stochastic Programming Tags , , ,

    This paper studies distributionally robust chance constrained programs (DRCCPs), where the uncertain constraints must be satisfied with at least a probability of a prespecified threshold for all probability distributions from the Wasserstein ambiguity set. As DRCCPs are often nonconvex and challenging to solve optimally, researchers have been developing various convex inner approximations. Recently, ALSO-X has … Read more

    Two-stage and Lagrangian Dual Decision Rules for Multistage Adaptive Robust Optimization

  • Maryam Daryalal
  • Ayse Nur Arslan
  • Merve Bodur
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    In this work, we design primal and dual bounding methods for multistage adaptive robust optimization (MSARO) problems motivated by two decision rules rooted in the stochastic programming literature. From the primal perspective, this is achieved by applying decision rules that restrict the functional forms of only a certain subset of decision variables resulting in an … Read more

    Stochastic Dynamic Lot-sizing with Supplier-Driven Substitution and Service Level Constraints

  • Narges Sereshti
  • Merve Bodur
  • James R. Luedtke
    • Categories Production and Logistics, Stochastic Programming Tags , , ,

    We consider a multi-stage stochastic lot-sizing problem with service level constraints and supplier-driven product substitution. A firm has multiple products and it has the option to meet demand from substitutable products at a cost. Considering the uncertainty in future demands, the firm wishes to make ordering decisions in every period such that the probability that … Read more

    Data-Driven Stochastic Dual Dynamic Programming: Performance Guarantees and Regularization Schemes

  • Hyuk Park
  • Zhuangzhuang Jia
  • Grani A. Hanasusanto
    • Categories Stochastic Programming Tags , , , ,

    We propose a data-driven scheme for multistage stochastic linear programming with Markovian random parameters by extending the stochastic dual dynamic programming (SDDP) algorithm. In our data-driven setting, only a finite number of historical trajectories are available. The proposed SDDP scheme evaluates the cost-to-go functions only at the observed sample points, where the conditional expectations are … Read more

    Distributionally robust chance-constrained Markov decision processes

  • Hoang Nam Nguyen
  • Abdel Lisser
  • Vikas Vikram Singh
    • Categories Stochastic Programming Tags , , , , ,

    Markov decision process (MDP) is a decision making framework where a decision maker is interested in maximizing the expected discounted value of a stream of rewards received at future stages at various states which are visited according to a controlled Markov chain. Many algorithms including linear programming methods are available in the literature to compute … Read more

    A comparison of different approaches for the vehicle routing problem with stochastic demands

  • Matheus Jun Ota
  • Ricardo Fukasawa
    • Categories Branch and Cut Algorithms, Production and Logistics, Stochastic Programming Tags , ,

    The vehicle routing problem with stochastic demands (VRPSD) is a well studied variant of the classic (deterministic) capacitated vehicle routing problem (CVRP) where the customer demands are given by random variables. Two prominent approaches for solving the VRPSD model it either as a chance-constraint program (CC-VRPSD) or as a two-stage stochastic program (2S-VRPSD). In this … Read more

    On Generalization and Regularization via Wasserstein Distributionally Robust Optimization

  • Jonathan Yu-Meng Li
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    Wasserstein distributionally robust optimization (DRO) has found success in operations research and machine learning applications as a powerful means to obtain solutions with favourable out-of-sample performances. Two compelling explanations for the success are the generalization bounds derived from Wasserstein DRO and the equivalency between Wasserstein DRO and the regularization scheme commonly applied in machine learning. … Read more