Applications – OR and Management Sciences

European Gas Infrastructure Expansion Planning: An Adaptive Robust Optimization Approach

  • Iegor Riepin
  • Matthew Schmidt
  • Luis Baringo
  • Felix Müsgens
    • Categories Finance and Economics, Robust Optimization Tags , , ,

    The European natural gas market is undergoing fundamental changes, fostering uncertainty regarding both supply and demand. This uncertainty is concentrated in the value of strategic infrastructure investments, e.g., projects of common interest supported by European Union public funds, to safeguard security of supply. This paper addresses this matter by suggesting an adaptive robust optimization framework … Read more

    An Overview of Nested Decomposition for Multi-Level Optimization Problems

  • Stephen J. Maher
  • Ibrahim Muter
    • Categories Applications - OR and Management Sciences, Combinatorial Optimization, Integer Programming Tags , , , ,

    Nested multi-level structures are frequently encountered in many real-world optimization problems. Decomposition techniques are a commonly applied approach used to handle nested multi-level structures; however, the typical problem-specific focus of such techniques has led to numerous specialized formulations and solution methods. This lack of generalized results for nested multi-level optimization problems is addressed in this … Read more

    Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks

  • Jannis Kurtz
  • Bubacarr Bah
    • Categories Applications - OR and Management Sciences, Integer Programming, Robust Optimization Tags ,

    Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and computational demands. In this work we study deep neural networks with binary activation functions and continuous or integer weights (BDNN). We show that the BDNN can be reformulated as a … Read more

    Differential Privacy in Multi-Party Resource Sharing

  • Utku Karaca
  • Ş. İlker Birbil
  • Nursen Aydin
  • Gizem Mullaoğlu
  • Sinan Yıldırım
    • Categories Applications - OR and Management Sciences Tags , , ,

    This study examines a resource-sharing problem involving multiple parties that agree to use a set of capacities together. We start with modeling the whole problem as a mathematical program, where all parties are required to exchange information to obtain the optimal objective function value. This information bears private data from each party in terms of … Read more

    An Integrated Rolling Horizon and Adaptive-Refinement Approach for Disjoint Trajectories Optimization

  • Benno Hoch
  • Frauke Liers
    • Categories Applications - OR and Management Sciences Tags , , , ,

    Planning of trajectories, i.e. paths over time, is a challenging task. Thereby, the trajectories for involved commodities often have to be considered jointly as separation constraints have to be respected. This is for example the case in robot motion or air traffic management. Involving these discrete separation constraints in the planning of best possible continuous … Read more

    A Unifying Framework for the Capacitated Vehicle Routing Problem under Risk and Ambiguity

  • Shubhechyya Ghosal
  • Chin Pang Ho
  • Wolfram Wiesemann
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming Tags , ,

    We propose a generic model for the capacitated vehicle routing problem (CVRP) under demand uncertainty. By combining risk measures, satisficing measures or disutility functions with complete or partial characterizations of the probability distribution governing the demands, our formulation bridges the popular but often independently studied paradigms of stochastic programming and distributionally robust optimization. We characterize … Read more

    Screening with Limited Information: A Dual Perspective and A Geometric Approach

  • Zhi Chen
  • Zhenyu Hu
  • Ruiqin Wang
    • Categories Applications - OR and Management Sciences, Robust Optimization Tags , , ,

    Consider a seller seeking a selling mechanism to maximize the worst-case revenue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. For a generic convex ambiguity set, we show via the minimax theorem that strong duality holds between the problem of finding the optimal robust mechanism and a minimax pricing problem … Read more

    Robust CARA Optimization

  • Li Chen
  • Melvyn Sim
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming Tags , ,

    We propose robust optimization models and their tractable approximations that cater for ambiguity-averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst-case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent factors with ambiguous marginals. To obtain … Read more

    Data-Driven Ranges of Near-Optimal Actions for Finite Markov Decision Processes

  • Wesley Marrero
  • Mariel S. Lavieri
  • Ambuj Tewari
  • Jeremy Sussman
  • Rodney A. Hayward
    • Categories Applications - OR and Management Sciences, Applications - Science and Engineering, Dynamic Programming Tags , , , , ,

    Markov decision process (MDP) models have been used to obtain non-stationary optimal decision rules in various applications, such as treatment planning in medical decision making. However, in practice, decision makers may prefer other strategies that are not statistically different from the optimal decision rules. To benefit from the decision makers’ expertise and provide flexibility in … Read more

    Optimization with Constraint Learning: A Framework and Survey

  • Adejuyigbe Fajemisin
  • Donato Maragno
  • Dick den Hertog
    • Categories Applications - OR and Management Sciences Tags , , ,

    Many real-life optimization problems frequently contain one or more constraints or objectives for which there are no explicit formulas. If data is however available, these data can be used to learn the constraints. The benefits of this approach are clearly seen, however there is a need for this process to be carried out in a … Read more