(Mixed) Integer Linear Programming

A novel Pareto-optimal cut selection strategy for Benders Decomposition

  • Lukas Glomb
  • Frauke Liers
  • Florian Rösel
    • Categories (Mixed) Integer Linear Programming Tags ,

    Decomposition methods can be used to create efficient solution algorithms for a wide range of optimization problems. For example, Benders Decomposition can be used to solve scenario-expanded two-stage stochastic optimization problems effectively. Benders Decomposition iteratively generates Benders cuts by solving a simplified version of an optimization problem, the so-called subproblem. The choice of the generated … Read more

    Resilient Relay Logistics Network Design: A k-Shortest Path Approach

  • Onkar Anand Kulkarni
  • Mathieu Dahan
    • Categories (Mixed) Integer Linear Programming, Facility Planning and Design, Production and Logistics Tags , , , ,

    Problem definition: We study the problem of designing large-scale resilient relay logistics hub networks. We propose a model of k-Shortest Path Network Design, which aims to improve a network’s efficiency and resilience through its topological configuration, by locating relay logistics hubs to connect each origin-destination pair with k paths of minimum lengths, weighted by their … Read more

    PaPILO: A Parallel Presolving Library for Integer and Linear Optimization with Multiprecision Support

  • Alexander Hoen
  • Ambros Gleixner
  • Leona Gottwald
    • Categories (Mixed) Integer Linear Programming, Integer Programming, Linear Programming Tags , , , ,

    Presolving has become an essential component of modern MIP solvers both in terms of computational performance and numerical robustness. In this paper we present PaPILO (https://github.com/scipopt/papilo), a new C++ header-only library that provides a large set of presolving routines for MIP and LP problems from the literature. The creation of \papilo was motivated by the … Read more

    ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

  • Patrick Vossler
  • Sina Aghaei
  • Nathan Justin
  • Nathanael Jo
  • Andrés Gómez
  • Phebe Vayanos
    • Categories (Mixed) Integer Linear Programming, Optimization Software and Modeling Systems Tags , , , , , ,

    ODTLearn is an open-source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2019) and several of its extensions. The current version of the package provides implementations for learning optimal classification trees, optimal fair classification trees, … Read more

    Dual Conflict Analysis for Mixed-Integer Semidefinite Programs

  • Marc E. Pfetsch
    • Categories (Mixed) Integer Linear Programming, Semi-definite Programming Tags ,

    Conflict analysis originally tried to exploit the knowledge that certain nodes in a relaxation-based branch-and-bound are infeasible. It has been extended to derive valid constraints also from feasible nodes. This paper adapts this approach to mixed-integer semidefinite programs. Using dual solutions, the primal constraints are aggregated and the resulting inequalities can be used at different … Read more

    A Tutorial on Solving Single-Leader-Multi-Follower Problems using SOS1 Reformulations

  • Didier Aussel
  • Cecile Egea
  • Martin Schmidt
    • Categories (Mixed) Integer Linear Programming, Complementarity and Variational Inequalities, Game Theory Tags , , ,

    In this tutorial we consider single-leader-multi-follower games in which the models of the lower-level players have polyhedral feasible sets and convex objective functions. This situation allows for classic KKT reformulations of the separate lower-level problems, which lead to challenging single-level reformulations of MPCC type. The main contribution of this tutorial is to present a ready-to-use … Read more

    Essays on Average Incremental Cost Pricing for Independent System Operators

  • Richard O'Neill
  • Yonghong Chen
  • Brent Eldridge
    • Categories (Mixed) Integer Linear Programming Tags ,

    In a series of essays, we introduce average incremental cost (AIC) pricing and present examples to help understand its advantages. In non-convex markets, AIC pricing is the rough equivalent to marginal cost pricing in convex markets. We present a qualitative comparison of current ISO pricing methods and the AIC approach. We argue that AIC better … Read more

    One-Pass Average Incremental Cost Pricing

  • Richard O'Neill
  • Yonghong Chen
    • Categories (Mixed) Integer Linear Programming Tags ,

    Since the inception of ISOs, Locational Marginal Prices (LMPs) alone were not incentive compatible because an auction winner who offered its avoidable costs could lose money at the LMP. ISOs used make-whole payments to ensure market participants did not lose money. Make-whole payments were not public creating transparency issues. Over time, the ISOs employed methods … Read more

    Structured Pruning of Neural Networks for Constraints Learning

  • Matteo Cacciola
  • Andrea Lodi
  • Antonio Frangioni
    • Categories (Mixed) Integer Linear Programming, Combinatorial Optimization, Optimization in Data Science Tags , ,

    In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this domain, the combination of ML and OR often relies on representing the ML model output using Mixed Integer Programming (MIP) formulations. Numerous studies … Read more

    Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks

  • Ritesh Ojha
  • Reem Khir
  • Alan L. Erera
    • Categories (Mixed) Integer Linear Programming, Production and Logistics, Transportation Tags , ,

    Citation Ojha, R., Chen, W., Zhang, H., Khir, R., Erera, A. & Van Hentenryck, P. (2023). Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks. Article Download View Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks