Applications – OR and Management Sciences

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

    Incorporating Holding Costs in Continuous-TimeService Network Design: New Model, Relaxation, and Exact Algorithm

  • Shengnan Shu
  • Zhou Xu
  • Roberto Baldacci
    • Categories Applications - OR and Management Sciences, Integer Programming, Network Optimization Tags , ,

    The continuous-time service network design problem (CTSNDP) occurs widely in practice. It aims to minimize the total operational cost by optimizing the schedules of transportation services and the routes of shipments for dispatching, which can occur at any time point along a continuous planning horizon. In order to be cost effective, shipments often wait to … Read more

    Adjustable robust optimization with objective uncertainty

  • Boris Detienne
  • Henri Lefebvre
  • Enrico Malaguti
  • Michele Monaci
    • Categories Applications - OR and Management Sciences, Convex Optimization, Robust Optimization Tags , , , , ,

    In this work, we study optimization problems where some cost parameters are not known at decision time and the decision flow is modeled as a two-stage process within a robust optimization setting. We address general problems in which all constraints (including those linking the first and the second stages) are defined by convex functions and … Read more

    Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach

  • Dimitris Bertsimas
  • Ryan Cory-Wright
  • Nicholas A. G. Johnson
    • Categories Applications - OR and Management Sciences, Data-Mining, Statistics Tags , , , ,

    We study the Sparse Plus Low-Rank decomposition problem (SLR), which is the problem of decomposing a corrupted data matrix into a sparse matrix of perturbations plus a low-rank matrix containing the ground truth. SLR is a fundamental problem in Operations Research and Machine Learning which arises in various applications, including data compression, latent semantic indexing, … Read more

    Interval Scheduling with Economies of Scale

  • Christopher Muir
  • Alejandro Toriello
    • Categories Dynamic Programming, Integer Programming, Scheduling Tags , , ,

    Motivated by applications in cloud computing, we study interval scheduling problems exhibiting economies of scale. An instance is given by a set of jobs, each with start time, end time, and a function representing the cost of scheduling a subset of jobs on the same machine. Specifically, we focus on the max-weight function and non-negative, … Read more

    Approximate Dynamic Programming for Crowd-shipping with In-store Customers

  • Kianoush Mousavi
  • Merve Bodur
  • Mucahit Cevik
  • Matthew J. Roorda
    • Categories Civil and Environmental Engineering, Dynamic Programming, Transportation Tags , , , ,

    Crowd-shipping has gained significant attention as a last-mile delivery option over the recent years. In this study, we propose a variant of dynamic crowd-shipping model with in-store customers as crowd-shippers to deliver online orders within few hours. We formulate the problem as a Markov decision process and develop an approximate dynamic programming (ADP) policy using … Read more