EURO Journal on Computational Optimization最新文献_第10页

Multi-sourcing under supply uncertainty and Buyer's risk aversion 供给不确定性和买方风险规避下的多源采购

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2021-01-01 DOI: 10.1016/j.ejco.2021.100009

Prashant Chintapalli

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引用次数: 1

A Survey on Mixed-Integer Programming Techniques in Bilevel Optimization 双层优化中的混合整数规划技术综述

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2021-01-01 DOI: 10.1016/j.ejco.2021.100007

Thomas Kleinert , Martine Labbé , Ivana Ljubić , Martin Schmidt

{"title":"A Survey on Mixed-Integer Programming Techniques in Bilevel Optimization","authors":"Thomas Kleinert , Martine Labbé , Ivana Ljubić , Martin Schmidt","doi":"10.1016/j.ejco.2021.100007","DOIUrl":"https://doi.org/10.1016/j.ejco.2021.100007","url":null,"abstract":"<div><p>Bilevel optimization is a field of mathematical programming in which some variables are constrained to be the solution of another optimization problem. As a consequence, bilevel optimization is able to model hierarchical decision processes. This is appealing for modeling real-world problems, but it also makes the resulting optimization models hard to solve in theory and practice. The scientific interest in computational bilevel optimization increased a lot over the last decade and is still growing. Independent of whether the bilevel problem itself contains integer variables or not, many state-of-the-art solution approaches for bilevel optimization make use of techniques that originate from mixed-integer programming. These techniques include branch-and-bound methods, cutting planes and, thus, branch-and-cut approaches, or problem-specific decomposition methods. In this survey article, we review bilevel-tailored approaches that exploit these mixed-integer programming techniques to solve bilevel optimization problems. To this end, we first consider bilevel problems with convex or, in particular, linear lower-level problems. The discussed solution methods in this field stem from original works from the 1980’s but, on the other hand, are still actively researched today. Second, we review modern algorithmic approaches to solve mixed-integer bilevel problems that contain integrality constraints in the lower level. Moreover, we also briefly discuss the area of mixed-integer nonlinear bilevel problems. Third, we devote some attention to more specific fields such as pricing or interdiction models that genuinely contain bilinear and thus nonconvex aspects. Finally, we sketch a list of open questions from the areas of algorithmic and computational bilevel optimization, which may lead to interesting future research that will further propel this fascinating and active field of research.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"9 ","pages":"Article 100007"},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejco.2021.100007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92106930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 93

A branch-and-bound approach for a Vehicle Routing Problem with Customer Costs 考虑客户成本的车辆路径问题的分支定界方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2021-01-01 DOI: 10.1016/j.ejco.2020.100003

Franziska Theurich , Andreas Fischer , Guntram Scheithauer

引用次数: 13

Twenty years of continuous multiobjective optimization in the twenty-first century 二十一世纪二十年的连续多目标优化

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2021-01-01 DOI: 10.1016/j.ejco.2021.100014

Gabriele Eichfelder

引用次数: 11

Sample average approximation for risk-averse problems: A virtual power plant scheduling application 风险规避问题的样本平均近似:一个虚拟电厂调度应用

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2021-01-01 DOI: 10.1016/j.ejco.2021.100005

Ricardo M. Lima , Antonio J. Conejo , Loïc Giraldi , Olivier Le Maître , Ibrahim Hoteit , Omar M. Knio

{"title":"Sample average approximation for risk-averse problems: A virtual power plant scheduling application","authors":"Ricardo M. Lima , Antonio J. Conejo , Loïc Giraldi , Olivier Le Maître , Ibrahim Hoteit , Omar M. Knio","doi":"10.1016/j.ejco.2021.100005","DOIUrl":"10.1016/j.ejco.2021.100005","url":null,"abstract":"<div><p>In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices. The problem is modeled using a risk-neutral and two risk-averse two-stage stochastic programming formulations, where the conditional value at risk is used to represent risk. A sample average approximation methodology is integrated with an adapted L-Shaped solution method, which can solve risk-neutral and specific risk-averse problems. This methodology provides a framework to understand and quantify the impact of the sample size on the variability of the results. The numerical results include an analysis of the computational performance of the methodology for two case studies, estimators for the bounds of the true optimal solutions of the problems, and an assessment of the quality of the solutions obtained. In particular, numerical experiences indicate that when an adequate sample size is used, the solution obtained is close to the optimal one.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"9 ","pages":"Article 100005"},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejco.2021.100005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134173836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

An exact approach for the multi-constraint graph partitioning problem 多约束图划分问题的一种精确方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2020-10-01 DOI: 10.1007/s13675-020-00126-9

Diego Recalde , Ramiro Torres , Polo Vaca

引用次数: 4

Computing Euclidean Steiner trees over segments 计算分段上的欧几里得斯坦纳树

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2020-10-01 DOI: 10.1007/s13675-020-00125-w

Ernst Althaus , Felix Rauterberg , Sarah Ziegler

{"title":"Computing Euclidean Steiner trees over segments","authors":"Ernst Althaus , Felix Rauterberg , Sarah Ziegler","doi":"10.1007/s13675-020-00125-w","DOIUrl":"10.1007/s13675-020-00125-w","url":null,"abstract":"<div><p>In the classical Euclidean Steiner minimum tree (SMT) problem, we are given a set of points in the Euclidean plane and we are supposed to find the minimum length tree that connects all these points, allowing the addition of arbitrary additional points. We investigate the variant of the problem where the input is a set of line segments. We allow these segments to have length 0, i.e., they are points and hence we generalize the classical problem. Furthermore, they are allowed to intersect such that we can model polygonal input. As in the GeoSteiner approach of Juhl et al. (Math Program Comput 10(2):487–532, 2018) for the classical case, we use a two-phase approach where we construct a superset of so-called full components of an SMT in the first phase. We prove a structural theorem for these full components, which allows us to use almost the same GeoSteiner algorithm as in the classical SMT problem. The second phase, the selection of a minimal cost subset of constructed full components, is exactly the same as in GeoSteiner approach. Finally, we report some experimental results that show that our approach is more efficient than the approximate solution that is obtained by sampling the segments.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"8 3","pages":"Pages 309-325"},"PeriodicalIF":2.4,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s13675-020-00125-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48047296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Special issue on: Computational discrete optimization 特刊:计算离散优化

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2020-10-01 DOI: 10.1007/s13675-020-00132-x

ArieM.C.A. Koster , Clemens Thielen

引用次数: 0

A hierarchical approach for solving an integrated packing and sequence-optimization problem in production of glued laminated timber 一种解决胶合层合木材生产中集成包装和顺序优化问题的分层方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2020-10-01 DOI: 10.1007/s13675-020-00127-8

Heiner Ackermann , Erik Diessel

引用次数: 2

Two-row and two-column mixed-integer presolve using hashing-based pairing methods 使用基于哈希的配对方法求解两行和两列混合整数

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2020-10-01 DOI: 10.1007/s13675-020-00129-6

Patrick Gemander , Wei-Kun Chen , Dieter Weninger , Leona Gottwald , Ambros Gleixner , Alexander Martin

{"title":"Two-row and two-column mixed-integer presolve using hashing-based pairing methods","authors":"Patrick Gemander , Wei-Kun Chen , Dieter Weninger , Leona Gottwald , Ambros Gleixner , Alexander Martin","doi":"10.1007/s13675-020-00129-6","DOIUrl":"10.1007/s13675-020-00129-6","url":null,"abstract":"<div><p>In state-of-the-art mixed-integer programming solvers, a large array of reduction techniques are applied to simplify the problem and strengthen the model formulation before starting the actual branch-and-cut phase. Despite their mathematical simplicity, these methods can have significant impact on the solvability of a given problem. However, a crucial property for employing presolve techniques successfully is their speed. Hence, most methods inspect constraints or variables individually in order to guarantee linear complexity. In this paper, we present new hashing-based pairing mechanisms that help to overcome known performance limitations of more powerful presolve techniques that consider pairs of rows or columns. Additionally, we develop an enhancement to one of these presolve techniques by exploiting the presence of set-packing structures on binary variables in order to strengthen the resulting reductions without increasing runtime. We analyze the impact of these methods on the MIPLIB 2017 benchmark set based on an implementation in the MIP solver SCIP.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"8 3","pages":"Pages 205-240"},"PeriodicalIF":2.4,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s13675-020-00129-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129533614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10