PaMILO: A Solver for Multi-objective Mixed Integer Linear Optimization and Beyond

Fritz Bökler (), Levin Nemesch () and Mirko H. Wagner ()
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Fritz Bökler: Universität Osnabrück
Levin Nemesch: Universität Osnabrück
Mirko H. Wagner: Universität Osnabrück

Chapter Chapter 20 in Operations Research Proceedings 2022, 2023, pp 163-170 from Springer

Abstract: Abstract In multi-objective optimization, several potentially conflicting objective functions need to be optimized. Instead of one optimal solution, we look for the set of so called non-dominated solutions. An important subset is the set of non-dominated extreme points. Finding it is a computationally hard problem in general. While solvers for similar problems exist, there are none known for multi-objective mixed integer linear programs (MOMILPs) or multi-objective mixed integer quadratically constrained quadratic programs (MOMIQCQPs). We present PaMILO, the first solver for finding non-dominated extreme points of MOMILPs and MOMIQCQPs. It can be found on github under https://github.com/FritzBo/PaMILO . PaMILO provides an easy-to-use interface and is implemented in C++17. It solves occurring subproblems employing either CPLEX or Gurobi. PaMILO adapts the Dual-Benson algorithm for multi-objective linear programming (MOLP). As it was previously only defined for MOLPs, we describe how it can be adapted for MOMILPs, MOMIQCQPs and even more problem classes in the future.

Keywords: Software; Multi-objective; Non-dominated extreme points; Mixed integer linear programming; Mixed integer quadratically constrained quadratic programming; Dual-Benson (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-24907-5_20

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