EURO Journal on Computational Optimization最新文献

A compact model for the home healthcare routing and scheduling problem

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2024.100101

Roberto Montemanni , Sara Ceschia , Andrea Schaerf

引用次数: 0

Interior point methods in the year 2025

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100105

Jacek Gondzio

{"title":"Interior point methods in the year 2025","authors":"Jacek Gondzio","doi":"10.1016/j.ejco.2025.100105","DOIUrl":"10.1016/j.ejco.2025.100105","url":null,"abstract":"<div><div>Interior point methods (IPMs) have hugely influenced the field of optimization. Their fast development has been triggered by the seminal paper of Narendra Karmarkar published in 1984 which delivered a polynomial algorithm for linear programming and suggested that it might be implemented into a very efficient method in practice. Indeed, this has been demonstrated within a few years after 1984 and has gained IPMs a status of exceptionally powerful optimization tool. Linear Programming (LP) is at the centre of many operational research techniques including mixed-integer programming, network optimization and various decomposition techniques. Therefore, any progress in LP has far-reaching consequences. IPMs certainly did not disappoint in this context: they have become a heavily used methodology in modern optimization and operational research. Their accuracy, efficiency and reliability have been particularly appreciated when IPMs are applied to truly large scale problems which challenge any alternative approaches.</div><div>In this survey we will discuss several issues related to interior point methods. We will recall techniques which provide the building blocks of IPMs, and observe that actually at least some of them have been developed before 1984. We will briefly comment on the worst-case complexity results for different variants of IPMs and then focus on key aspects of their implementation. We will also address some of the most spectacular features of IPMs and discuss their potential advantages when applied in decomposition algorithms, cutting planes scheme and column generation technique.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100105"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428084","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: 0

In memoriam: Marguerite Straus Frank (1927–2024)

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100104

Immanuel Bomze, Anna Nagurney

引用次数: 0

Modern optimization approaches to classification—Special issue editorial 分类的现代优化方法--特刊社论

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 DOI: 10.1016/j.ejco.2024.100097

António Pedro Duarte Silva , Laura Palagi , Veronica Piccialli

引用次数: 0

An effective hybrid decomposition approach to solve the network-constrained stochastic unit commitment problem in large-scale power systems 解决大规模电力系统中网络受限随机机组承诺问题的有效混合分解方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2024-01-01 DOI: 10.1016/j.ejco.2024.100085

Ricardo M. Lima , Gonzalo E. Constante-Flores , Antonio J. Conejo , Omar M. Knio

{"title":"An effective hybrid decomposition approach to solve the network-constrained stochastic unit commitment problem in large-scale power systems","authors":"Ricardo M. Lima , Gonzalo E. Constante-Flores , Antonio J. Conejo , Omar M. Knio","doi":"10.1016/j.ejco.2024.100085","DOIUrl":"https://doi.org/10.1016/j.ejco.2024.100085","url":null,"abstract":"<div><p>We propose a novel hybrid method to solve the network-constrained stochastic unit commitment problem. We target realistic large-scale instances including hundreds of thermal generation units, thousands of transmission lines and nodes, and a large number of stochastic renewable generation units. This scheduling problem is formulated as a two-stage stochastic programming problem with continuous and binary variables in the first stage and only continuous variables in the second stage. We develop a hybrid solution method that decomposes the original problem into a master problem including unit commitment and dispatch decisions, and decomposed subproblems representing dispatch with transmission constraints per scenario. The proposed decomposition embeds a column-and-constraint generation step within the traditional Benders decomposition framework. The performance of the proposed decomposition technique is contrasted with the solution of the extensive form via branch-and-cut and Benders decomposition available in commercial solvers, and with conventional Benders decomposition variants. Our computational experiments show that the proposed method generates bounds of superior quality and finds solutions for instances where other approaches fail.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100085"},"PeriodicalIF":2.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440624000029/pdfft?md5=6e5b9d1b9d47dcb072bff8ae23d37d2b&pid=1-s2.0-S2192440624000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714415","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: 0

New computational results for integrated production and outbound distribution scheduling problems for a product with a short lifespan 短寿命产品的综合生产和配送调度问题的新计算结果

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 DOI: 10.1016/j.ejco.2024.100095

Markó Horváth

{"title":"New computational results for integrated production and outbound distribution scheduling problems for a product with a short lifespan","authors":"Markó Horváth","doi":"10.1016/j.ejco.2024.100095","DOIUrl":"https://doi.org/10.1016/j.ejco.2024.100095","url":null,"abstract":"<div><p>In this paper, we consider an integrated production and outbound distribution scheduling problem with a single production site, and its extension to multiple plants. A set of orders must be satisfied such that the required pieces from a single product must be first processed on a single machine in a plant, then must be delivered to the customers before their lifespan expire using a single vehicle. The goal is to minimize the makespan of the solution, which is the return time of the vehicle after its last trip. We propose an elementary variable neighborhood search to solve the problem, using two new local search operators. Our computational results show that this simple procedure outperforms the existing, sometimes complex approaches on the widely used benchmark dataset. We also review the existing computational results, and demonstrate that in some cases the comparisons in the literature are invalid due to the use of different rounding of the data. By re-evaluating the accessible solutions we provide a fair comparison for each rounding method. We also consider the extension of the problem to multiple plants, and adapt our solution approach for this extension. Our experiments show that our method is competitive in terms of solution quality with the existing solution approach for the problem.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100095"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440624000121/pdfft?md5=cf7e3d7b2aaa208cbc58be5f8cb0cff9&pid=1-s2.0-S2192440624000121-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483110","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: 0

Finding quadratic underestimators for optimal value functions of nonconvex all-quadratic problems via copositive optimization 利用组合优化方法寻找非凸全二次问题最优值函数的二次低估量

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 DOI: 10.1016/j.ejco.2024.100100

Markus Gabl , Immanuel M. Bomze

{"title":"Finding quadratic underestimators for optimal value functions of nonconvex all-quadratic problems via copositive optimization","authors":"Markus Gabl , Immanuel M. Bomze","doi":"10.1016/j.ejco.2024.100100","DOIUrl":"10.1016/j.ejco.2024.100100","url":null,"abstract":"<div><div>Modeling parts of an optimization problem as an optimal value function that depends on a top-level decision variable is a regular occurrence in optimization and an essential ingredient for methods such as Benders Decomposition. It often allows for the disentanglement of computational complexity and exploitation of special structures in the lower-level problem that define the optimal value functions. If this problem is convex, duality theory can be used to build piecewise affine models of the optimal value function over which the top-level problem can be optimized efficiently. In this text, we are interested in the optimal value function of an all-quadratic problem (also called quadratically constrained quadratic problem, QCQP) which is not necessarily convex, so that duality theory can not be applied without introducing a generally unquantifiable relaxation error. This issue can be bypassed by employing copositive reformulations of the underlying QCQP. We investigate two ways to parametrize these by the top-level variable. The first one leads to a copositive characterization of an underestimator that is sandwiched between the convex envelope of the optimal value function and that envelope's lower-semicontinuous hull. The dual of that characterization allows us to derive affine underestimators. The second parametrization yields an alternative characterization of the optimal value function itself, which other than the original version has an exact dual counterpart. From the latter, we can derive convex and nonconvex quadratic underestimators of the optimal value function. In fact, we can show that any quadratic underestimator is associated with a dual feasible solution in a certain sense.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100100"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748605","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: 0

Communication-efficient ADMM using quantization-aware Gaussian process regression 使用量化感知高斯过程回归的通信高效 ADMM

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 DOI: 10.1016/j.ejco.2024.100098

Aldo Duarte , Truong X. Nghiem , Shuangqing Wei

{"title":"Communication-efficient ADMM using quantization-aware Gaussian process regression","authors":"Aldo Duarte , Truong X. Nghiem , Shuangqing Wei","doi":"10.1016/j.ejco.2024.100098","DOIUrl":"10.1016/j.ejco.2024.100098","url":null,"abstract":"<div><p>In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents' local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, due to data quantization, the GP algorithm is modified to account for the introduced quantization noise statistics. We further improve our approach by introducing an orthogonalization process to the quantizer's input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer's introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution's accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100098"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440624000157/pdfft?md5=8fd7b21c89e782f11b9739c488b6329b&pid=1-s2.0-S2192440624000157-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238586","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: 0

Recent advances in exact optimization methods for OR applications – Special issue editorial

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 DOI: 10.1016/j.ejco.2024.100096

Markus Sinnl

引用次数: 0

The Marguerite Frank Award for the best EJCO paper 2023 玛格丽特-弗兰克奖--表彰 2023 年最佳 EJCO 论文

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2024-01-01 DOI: 10.1016/j.ejco.2024.100087

Immanuel Bomze (Editor-in-Chief)

引用次数: 0