Results 41 to 50 of about 58,165 (63)

An approximation algorithm for indefinite mixed integer quadratic programming

Mathematical programming, 2022
In this paper, we give an algorithm that finds an ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}
Alberto Del Pia
semanticscholar   +1 more source

Presolve Reductions in Mixed Integer Programming

INFORMS journal on computing, 2020
Mixed integer programming has become a very powerful tool for modeling and solving real-world planning and scheduling problems, with the breadth of applications appearing to be almost unlimited. A critical component in the solution of these mixed integer
Tobias Achterberg, R. Bixby, Zonghao Gu, E. Rothberg, Dieter Weninger   +4 more
semanticscholar   +1 more source

Multistage distributionally robust mixed-integer programming with decision-dependent moment-based ambiguity sets

Mathematical programming, 2020
We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages.
Xian Yu, Siqian Shen
semanticscholar   +1 more source

Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming

Mathematical programming
Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. The main engine for solving MILPs is the branch-and-bound algorithm.
Lara Scavuzzo, K. Aardal, Andrea Lodi, Neil Yorke-Smith   +3 more
semanticscholar   +1 more source

Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem

Computers & industrial engineering, 2020
This paper intends to address the distributed flexible job shop scheduling problem (DFJSP) with minimizing maximum completion time (makespan). In order to solve this problem, we propose four mixed integer linear programming (MILP) models as well as a ...
Leilei Meng, Chaoyong Zhang, Yaping Ren, Biao Zhang, Chang Lv   +4 more
semanticscholar   +1 more source

Serial and parallel two-column probing for mixed-integer programming

Mathematical Programming Computation
Probing in mixed-integer programming (MIP) is a technique of temporarily fixing variables to discover implications that are useful to branch-and-cut solvers.
Yongzheng Dai, Chen Chen
semanticscholar   +1 more source

A Criterion Space Method for Biobjective Mixed Integer Programming: The Boxed Line Method

INFORMS journal on computing, 2020
Despite recent interest in multiobjective integer programming, few algorithms exist for solving biobjective mixed integer programs. We present such an algorithm: the boxed line method.
Tyler A. Perini, N. Boland, Diego Pecin, M. Savelsbergh   +3 more
semanticscholar   +1 more source

Detecting and handling reflection symmetries in mixed-integer (nonlinear) programming and beyond

Mathematical Programming Computation
Symmetries in mixed-integer (nonlinear) programs (MINLP), if not handled appropriately, are known to negatively impact the performance of (spatial) branch-and-bound algorithms.
Christopher Hojny
semanticscholar   +1 more source

Mixed integer programming for dynamic tower crane and storage area optimization on construction sites

, 2020
Construction site layout planning, the arrangement of temporary facilities and equipment on site, is a fundamental part of construction preparation. In construction, the operation of tower cranes has a great impact on construction process execution and ...
Katrin Riga, K. Jahr, Clemens Thielen, A. Borrmann   +3 more
semanticscholar   +1 more source

On tractability, complexity, and mixed-integer convex programming representability of distributionally favorable optimization

Mathematical programming
Distributionally Favorable Optimization (DFO) is a framework for decision-making under uncertainty, with applications spanning various fields, including reinforcement learning, online learning, robust statistics, chance-constrained programming, and two-
Nan Jiang, Weijun Xie
semanticscholar   +1 more source