Robust Optimization

Effective Scenarios in Multistage Distributionally Robust Optimization with a Focus on Total Variation Distance

  • Hamed Rahimian
  • Güzin Bayraksan
  • Tito Homem-de-Mello
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    We study multistage distributionally robust optimization (DRO) to hedge against ambiguity in quantifying the underlying uncertainty of a problem. Recognizing that not all the realizations and scenario paths might have an “effect” on the optimal value, we investigate the question of how to define and identify critical scenarios for nested multistage DRO problems. Our analysis … Read more

    Data-Driven Distributionally Preference Robust Optimization Models Based on Random Utility Representation in Multi-Attribute Decision Making

  • Jian Hu
  • Huifu Xu
  • Dali Zhang
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    Preference robust optimization (PRO) has recently been studied to deal with utility based decision making problems under ambiguity in the characterization of the decision maker’s (DM) preference. In this paper, we propose a novel PRO modeling paradigm which combines the stochastic utility theory with distributionally robust optimization technique. Based on the stochastic utility theory, our … Read more

    Pareto Robust Optimization on Euclidean Vector Spaces

  • Dennis Adelhütte
  • Christian Biefel
  • Martina Kuchlbauer
  • Jan Hendrik Rolfes
    • Categories Robust Optimization Tags , ,

    Pareto efficiency for robust linear programs was introduced by Iancu and Trichakis. We generalize their approach and theoretical results to robust optimization problems in Euclidean spaces with affine uncertainty. Additionally, we demonstrate the value of this approach in an exemplary manner in the area of robust semidefinite programming (SDP). In particular, we prove that computing … Read more

    Appointment Scheduling for Medical Diagnostic Centers considering Time-sensitive Pharmaceuticals: A Dynamic Robust Optimization Approach

  • Aleida Braaksma
  • Mohammad Mahdavi Mazdeh
  • Mahdi Heydari
  • Mohammad Namakshenas
    • Categories Applications - OR and Management Sciences, Convex and Nonsmooth Optimization, Robust Optimization Tags , , , , ,

    This paper studies optimal criteria for the appointment scheduling of outpatients in a medical imaging center. The main goal of this study is to coordinate the assignments of radiopharmaceuticals and the scheduling of outpatients on imaging scanners. We study a special case of a molecular imaging center that offers services for various diagnostic procedures for … Read more

    Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery

  • Said Dabia
  • Angelos Georghiou
  • Rosario Paradiso
  • Denise Tönissen
    • Categories Applications - OR and Management Sciences, Robust Optimization Tags , , , , , ,

    Uncertain optimization problems with decision dependent information discovery allow the decision maker to control the timing of information discovery, in contrast to the classic multistage setting where uncertain parameters are revealed sequentially based on a prescribed filtration. This problem class is useful in a wide range of applications, however, its assimilation is partly limited by … Read more

    Robust Integration of Electric Vehicles Charging Load in Smart Grid’s Capacity Expansion Planning

  • Sajad Aliakbari-Sani
  • Olivier Bahn
  • Erick Delage
  • Rinel Foguen Tchuendom
    • Categories Energy, Game Theory, Robust Optimization Tags , , ,

    Battery charging of electric vehicles (EVs) needs to be properly coordinated by electricity producers to maintain network reliability. In this paper, we propose a robust approach to model the interaction between a large fleet of EV users and utilities in a long-term generation expansion planning problem. In doing so, we employ a robust multi-period adjustable … Read more

    Adjustable Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets

  • Haolin Ruan
  • Zhi Chen
  • Chin Pang Ho
    • Categories Robust Optimization Tags , , ,

    We study adjustable distributionally robust optimization problems where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such an ambiguity set has great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists … Read more

    Practicable Robust Stochastic Optimization under Divergence Measures

  • Douglas Alem
  • Aakil Caunhye
    • Categories Robust Optimization, Stochastic Programming Tags , , , , ,

    We seek to provide practicable approximations of the two-stage robust stochastic optimization (RSO) model when its ambiguity set is constructed with an f-divergence radius. These models are known to be numerically challenging to various degrees, depending on the choice of the f-divergence function. The numerical challenges are even more pronounced under mixed-integer rst-stage decisions. In … Read more

    Robust Epidemiological Prediction and Optimization

  • Chenyi Fu
  • Melvyn Sim
  • Minglong Zhou
    • Categories Applications - OR and Management Sciences, Robust Optimization Tags , , ,

    The COVID-19 pandemic has brought many countries to their knees, and the urgency to return to normalcy has never been greater. Epidemiological models, such as the SEIR compartmental model, are indispensable tools for, among other things, predicting how pandemic may spread over time and how vaccinations and different public health interventions could affect the outcome. … Read more

    RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy

  • Peng Xiong
  • Zhi Chen
    • Categories Modeling Languages and Systems, Robust Optimization Tags , , ,

    We develop a Python package called RSOME for modeling a wide spectrum of robust and distributionally robust optimization problems. RSOME serves as a modeling platform for formulating various optimization problems subject to distributional ambiguity in a highly readable and mathematically intuitive manner. Compared with the MATLAB version, RSOME in Python is more versatile and well … Read more