Applications – OR and Management Sciences

Finite-Sample Optimality and Constraint Satisfaction: Learning-Based Optimal Control in Dynamic Dispatch Networks

  • Moustafa Rashidy
    • Categories Data Science Theory, Production and Logistics, Transportation Tags , ,

    Dynamic dispatch networks in logistics and transportation require real-time, constraint-aware decision-making under stochastic demand. This paper bridges mathematical optimization, optimal control theory, and reinforcement learning by establishing non-asymptotic theoretical guarantees for learning-based optimal control in constrained stochastic dispatch systems. We formulate the problem as a constrained Markov decision process, enforce feasibility via a projection-based policy … Read more

    Rethinking Last-Mile Routing at Scale: Near-Linear Planning on Commodity Hardware

  • Martin Vizzolini
    • Categories Optimization Software and Modeling Systems, Production and Logistics, Supply Chain Management Tags , , , , , , , , , , , , , ,

    This paper presents a practical architecture for last-mile delivery routing at scales reaching one million stops under realistic operational conditions, including vehicle capacity, package volume, route stop limits, and time windows. Unlike conventional systems that require pre-partitioning or large-scale infrastructure, the proposed system addresses the full fleet planning problem through a single coherent planning pipeline … Read more

    Reverse stress testing for supply chains

  • Busra Gultekin
  • Frans J.C.T. de Ruiter
  • Meike Reusken
  • Sander de Leeuw
    • Categories Applications - OR and Management Sciences, Integer Programming, Supply Chain Management Tags , , ,

    This study introduces reverse stress testing for supply chains, designed to identify the minimal deviations from normal operations that would drive supply chains to a predefined performance failure. First, we present a framework for reverse stress testing with the purpose of assessing supply chain vulnerabilities. The framework involves six steps: selecting risk variables, defining baselines, … Read more

    Pricing Discrete and Nonlinear Markets With Semidefinite Relaxations

  • Cheng Guo
  • Lauren Henderson
  • Ryan Cory-Wright
  • Boshi Yang
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Semi-definite Programming Tags , , , ,

    Nonconvexities in markets with discrete decisions and nonlinear constraints make efficient pricing challenging, often necessitating subsidies. A prime example is the unit commitment (UC) problem in electricity markets, where costly subsidies are commonly required. We propose a new pricing scheme for nonconvex markets with both discreteness and nonlinearity, by convexifying nonconvex structures through a semidefinite … Read more

    An Interpretable Ensemble Heuristic for Principal-Agent Games with Machine Learning

  • I. Esra Buyuktahtakin Toy
    • Categories Applications - OR and Management Sciences, Game Theory, Optimization in Data Science

    This paper addresses the challenge of enhancing public policy decision-making by efficiently solving principal-agent models (PAMs) for public-private partnerships, a critical yet computationally demanding problem. We develop a fast, interpretable, and generalizable approach to support policy decisions under these settings. We propose an interpretable ensemble heuristic (EH) that integrates Machine Learning (ML), Operations Research (OR), … Read more

    On the Single-Multi-Commodity Gap: Lifting Single- to Multicommodity Flow Instances

  • Felix P. Broesamle
  • Stefan Nickel
    • Categories Applications - OR and Management Sciences, Linear Programming, Network Optimization Tags , , , , ,

    Benchmark instances for multicommodity flow problems frequently lack the structural nuances of real-world networks or fail to maintain a rigorous mathematical relationship with their single-commodity counterparts. This paper introduces a formal meta-generation framework that addresses these limitations by lifting single-commodity minimum-cost flow instances into the multicommodity space while strictly preserving the underlying network topology, capacity … Read more

    On vehicle routing problems with stochastic demands — Scenario-optimal recourse policies

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

    Two-Stage Vehicle Routing Problems with Stochastic Demands (VRPSDs) form a class of stochastic combinatorial optimization problems where routes are planned in advance, demands are revealed upon vehicle arrival, and recourse actions are triggered whenever capacity is exceeded. Following recent works, we consider VRPSDs where demands are given by an empirical probability distribution of scenarios. Existing … Read more

    Time-of-Use Pump Scheduling for Flow Transmission

  • Yuzhen Feng
    • Categories Energy, Scheduling

    We study time-of-use pump scheduling to deliver a required volume using a finite set of pump combinations with empirical flow–power performance, subject to per-shift caps on pump switches. We prove a structural theorem: partitioning the horizon into maximal intervals with constant tariff and shift (atoms), there always exists an optimal schedule with at most one … Read more

    A Newsvendor Model for Last-Mile Fleet Sizing

  • Benjamin Rojas
  • Homero Larrain
  • Mathias A. Klapp
  • Dipayan Banerjee
    • Categories Applications - OR and Management Sciences, Production and Logistics, Transportation Tags , , ,

    We study the tactical problem of determining a last-mile delivery fleet size while accounting for day-to-day uncertainty in the number and location of customer requests. An optimally sized fleet must balance the cost of contracting vehicles against the penalty costs of unserved customers: a larger fleet reduces the risk of unserved demand, but a smaller … Read more

    Finding Minimal Discretizations in Dynamic Discretization Discovery for Continuous-Time Service Network Design

  • Tom Wüllner
  • Alexander Helber
    • Categories Combinatorial Optimization, Network Optimization, Transportation Tags , , ,

    The dynamic discretization discovery framework is a powerful tool for solving network design problems with a temporal component by iteratively refining a time-discretized model. Existing approaches refine the time discretization in ways that guarantee eventual termination. However, refinement choices are not unique, and better choices can yield smaller and easier-to-solve time-discretized models. We pose the … Read more