{"title":"A simple, efficient and versatile objective space algorithm for multiobjective integer programming","authors":"Kerstin Dächert, Tino Fleuren, Kathrin Klamroth","doi":"10.1007/s00186-023-00841-0","DOIUrl":null,"url":null,"abstract":"<p>In the last years a multitude of algorithms have been proposed to solve multiobjective integer programming problems. However, only few authors offer open-source implementations. On the other hand, new methods are typically compared to code that is publicly available, even if this code is known to be outperformed. In this paper, we aim to overcome this problem by proposing a new state-of-the-art algorithm with an open-source implementation in <span>C++</span>. The underlying method falls into the class of objective space methods, i.e., it decomposes the overall problem into a series of scalarized subproblems that can be solved with efficient single-objective IP-solvers. It keeps the number of required subproblems small by avoiding redundancies, and it can be combined with different scalarizations that all lead to comparably simple subproblems. Our algorithm bases on previous results but combines them in a new way. Numerical experiments with up to ten objectives validate that the method is efficient and that it scales well to higher dimensional problems.</p>","PeriodicalId":49862,"journal":{"name":"Mathematical Methods of Operations Research","volume":"276 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Methods of Operations Research","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00186-023-00841-0","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
In the last years a multitude of algorithms have been proposed to solve multiobjective integer programming problems. However, only few authors offer open-source implementations. On the other hand, new methods are typically compared to code that is publicly available, even if this code is known to be outperformed. In this paper, we aim to overcome this problem by proposing a new state-of-the-art algorithm with an open-source implementation in C++. The underlying method falls into the class of objective space methods, i.e., it decomposes the overall problem into a series of scalarized subproblems that can be solved with efficient single-objective IP-solvers. It keeps the number of required subproblems small by avoiding redundancies, and it can be combined with different scalarizations that all lead to comparably simple subproblems. Our algorithm bases on previous results but combines them in a new way. Numerical experiments with up to ten objectives validate that the method is efficient and that it scales well to higher dimensional problems.
在过去几年中,人们提出了许多算法来解决多目标整数编程问题。然而,只有少数作者提供了开源实现。另一方面,新方法通常都是与公开的代码进行比较,即使这些代码已知性能优于新方法。在本文中,我们提出了一种新的先进算法,并用 C++ 进行了开源实现,旨在克服这一问题。其基本方法属于目标空间方法,即把整个问题分解成一系列标量化子问题,这些子问题可以用高效的单目标 IP 求解器求解。它通过避免冗余来减少所需子问题的数量,并可与不同的标度化方法相结合,从而得到相当简单的子问题。我们的算法以之前的成果为基础,但以一种新的方式将它们结合起来。多达十个目标的数值实验验证了该方法的高效性,并且可以很好地扩展到更高维度的问题。
期刊介绍:
This peer reviewed journal publishes original and high-quality articles on important mathematical and computational aspects of operations research, in particular in the areas of continuous and discrete mathematical optimization, stochastics, and game theory. Theoretically oriented papers are supposed to include explicit motivations of assumptions and results, while application oriented papers need to contain substantial mathematical contributions. Suggestions for algorithms should be accompanied with numerical evidence for their superiority over state-of-the-art methods. Articles must be of interest for a large audience in operations research, written in clear and correct English, and typeset in LaTeX. A special section contains invited tutorial papers on advanced mathematical or computational aspects of operations research, aiming at making such methodologies accessible for a wider audience.
All papers are refereed. The emphasis is on originality, quality, and importance.