EURO Journal on Computational Optimization最新文献_第6页

An optimisation model for minimising changes in frequency allocations 频率分配变化最小化的优化模型

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100042

J.E. Beasley

{"title":"An optimisation model for minimising changes in frequency allocations","authors":"J.E. Beasley","doi":"10.1016/j.ejco.2022.100042","DOIUrl":"https://doi.org/10.1016/j.ejco.2022.100042","url":null,"abstract":"<div><p>In this paper we deal with a problem associated with frequency assignment. Suppose we have a number of transmitters, each of which has been allocated a frequency. The problem we consider is how, given one (or more) transmitters are requesting a new frequency allocation, for example because of the interference they are currently suffering, to decide the new frequencies. Here we wish to constrain overall interference, but minimise the number of frequency changes needed for transmitters that have not requested a change.</p><p>We present an optimisation model for frequency allocation that minimises changes in the existing allocation, whilst limiting interference. We consider the standard mathematical representation of interference in the literature and show that we can represent it in a way that involves far fewer variables and constraints.</p><p>We make use of this new representation of interference in our zero-one integer linear program for deciding a new frequency allocation. We also show how our formulation can be adapted to deal with a number of other possibilities, specifically allocating frequencies to new transmitters with known locations and also deciding a location (and frequency) for a single new transmitter.</p><p>We present computational results for our approach making use of minimum interference frequency assignment test problems taken from the literature. We compare the results from our new representation of interference with those obtained using the standard representation.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100042"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000181/pdfft?md5=4c8788ff211f90edb9b73aa9b0d471f9&pid=1-s2.0-S2192440622000181-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92106810","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

Sustainable two stage supply chain management: A quadratic optimization approach with a quadratic constraint 可持续两阶段供应链管理:具有二次约束的二次优化方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100040

Massimiliano Caramia , Giuseppe Stecca

{"title":"Sustainable two stage supply chain management: A quadratic optimization approach with a quadratic constraint","authors":"Massimiliano Caramia , Giuseppe Stecca","doi":"10.1016/j.ejco.2022.100040","DOIUrl":"https://doi.org/10.1016/j.ejco.2022.100040","url":null,"abstract":"<div><p>Designing a supply chain to comply with environmental policy requires awareness of how work and/or production methods impact the environment and what needs to be done to reduce those environmental impacts and make the company more sustainable. This is a dynamic process that occurs at both the strategic and operational levels. However, being environmentally friendly does not necessarily mean improving the efficiency of the system at the same time. Therefore, when allocating a production budget in a supply chain that implements the green paradigm, it is necessary to figure out how to properly recover costs in order to improve both sustainability and routine operations, offsetting the negative environmental impact of logistics and production without compromising the efficiency of the processes to be executed. In this paper, we study the latter problem in detail, focusing on the CO<sub>2</sub> emissions generated by the transportation from suppliers to production sites, and by the production activities carried out in each plant. We do this using a novel mathematical model that has a quadratic objective function and all linear constraints except one, which is also quadratic, and models the constraint on the budget that can be used for green investments caused by the increasing internal complexity created by large production flows in the production nodes of the supply network. To solve this model, we propose a multistart algorithm based on successive linear approximations. Computational results show the effectiveness of our proposal.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100040"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000168/pdfft?md5=d990f6e03fce4dcda54bf2a8938898dd&pid=1-s2.0-S2192440622000168-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92106811","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

Feasible rounding based diving strategies in branch-and-bound methods for mixed-integer optimization 混合整数优化分支定界法中基于舍入的可行潜水策略

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100051

Christoph Neumann , Stefan Schwarze , Oliver Stein , Benjamin Müller

{"title":"Feasible rounding based diving strategies in branch-and-bound methods for mixed-integer optimization","authors":"Christoph Neumann , Stefan Schwarze , Oliver Stein , Benjamin Müller","doi":"10.1016/j.ejco.2022.100051","DOIUrl":"10.1016/j.ejco.2022.100051","url":null,"abstract":"<div><p>In this paper, we study the behavior of feasible rounding approaches for mixed-integer optimization problems when integrated into branch-and-bound methods. Our research addresses two important aspects. First, we develop insights into how an (enlarged) inner parallel set, which is the main component for feasible rounding approaches, behaves when we move down a search tree. Our theoretical results show that the number of feasible points obtainable from the inner parallel set is nondecreasing with increasing depth of the search tree. Thus, they hint at the potential benefit of integrating feasible rounding approaches into branch-and-bound methods. Second, based on those insights, we develop a novel primal heuristic for MILPs that fixes variables in a way that promotes large inner parallel sets of child nodes.</p><p>Our computational study shows that combining feasible rounding approaches with the presented diving ideas yields a significant improvement over their application in the root node. Moreover, the proposed method is able to deliver best solutions for the MIP solver SCIP for a significant share of problems which hints at its potential to support solving MILPs.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100051"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000272/pdfft?md5=30c165049139cd66d0e46e1fec637066&pid=1-s2.0-S2192440622000272-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121304055","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

A hybrid genetic algorithm for scheduling jobs sharing multiple resources under uncertainty 不确定条件下多资源共享作业调度的混合遗传算法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100050

Hanyu Gu, Hue Chi Lam, Yakov Zinder

引用次数: 0

Celebrating 20 years of EUROpt 庆祝欧盟成立20周年

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100036

Miguel F. Anjos , Tibor Illés , Tamás Terlaky

引用次数: 0

The Marguerite Frank Award for the best EJCO paper 2021 2021年最佳EJCO论文的玛格丽特弗兰克奖

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100026

Immanuel Bomze (Editor-in-Chief)

引用次数: 0

Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations 利用乘法器交替方向法结合层次半可分核近似训练超大规模非线性支持向量机

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100046

S. Cipolla, J. Gondzio

{"title":"Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations","authors":"S. Cipolla, J. Gondzio","doi":"10.1016/j.ejco.2022.100046","DOIUrl":"10.1016/j.ejco.2022.100046","url":null,"abstract":"<div><p>Typically, nonlinear Support Vector Machines (SVMs) produce significantly higher classification quality when compared to linear ones but, at the same time, their computational complexity is prohibitive for large-scale datasets: this drawback is essentially related to the necessity to store and manipulate large, dense and unstructured kernel matrices. Despite the fact that at the core of training an SVM there is a <em>simple</em> convex optimization problem, the presence of kernel matrices is responsible for dramatic performance reduction, making SVMs unworkably slow for large problems. Aiming at an efficient solution of large-scale nonlinear SVM problems, we propose the use of the <em>Alternating Direction Method of Multipliers</em> coupled with <em>Hierarchically Semi-Separable</em> (HSS) kernel approximations. As shown in this work, the detailed analysis of the interaction among their algorithmic components unveils a particularly efficient framework and indeed, the presented experimental results demonstrate, in the case of Radial Basis Kernels, a significant speed-up when compared to the <em>state-of-the-art</em> nonlinear SVM libraries (without significantly affecting the classification accuracy).</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100046"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000223/pdfft?md5=f2bf24cfecce1c28928c1acc1630dd05&pid=1-s2.0-S2192440622000223-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128898167","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: 3

Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network 机器人舞蹈：在复杂网络中干预新冠肺炎的数学优化平台

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100025

Luis Gustavo Nonato , Pedro Peixoto , Tiago Pereira , Claudia Sagastizábal , Paulo J.S. Silva

{"title":"Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network","authors":"Luis Gustavo Nonato , Pedro Peixoto , Tiago Pereira , Claudia Sagastizábal , Paulo J.S. Silva","doi":"10.1016/j.ejco.2022.100025","DOIUrl":"10.1016/j.ejco.2022.100025","url":null,"abstract":"<div><p>Robot Dance is a computational optimization platform developed in response to the COVID-19 outbreak, to support the decision-making on public policies at a regional level. The tool is suitable for understanding and suggesting levels of intervention needed to contain the spread of infectious diseases when the mobility of inhabitants through a regional network is a concern. Such is the case for the SARS-CoV-2 virus that is highly contagious and, therefore, makes it crucial to incorporate the circulation of people in the epidemiological compartmental models. Robot Dance anticipates the spread of an epidemic in a complex regional network, helping to identify fragile links where applying differentiated measures of containment, testing, and vaccination is important. Based on stochastic optimization, the model determines efficient strategies on the basis of commuting of individuals and the situation of hospitals in each district. Uncertainty in the capacity of intensive care beds is handled by a chance-constraint approach. Some functionalities of Robot Dance are illustrated in the state of São Paulo in Brazil, using real data for a region with more than forty million inhabitants.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100025"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000016/pdfft?md5=481392c4a63aa5d41081f96af794659f&pid=1-s2.0-S2192440622000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46259155","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: 1048

Multi-Neighborhood simulated annealing for the minimum interference frequency assignment problem 最小干扰频率分配问题的多邻域模拟退火

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2021.100024

Sara Ceschia, Luca Di Gaspero, Roberto Maria Rosati, Andrea Schaerf

{"title":"Multi-Neighborhood simulated annealing for the minimum interference frequency assignment problem","authors":"Sara Ceschia, Luca Di Gaspero, Roberto Maria Rosati, Andrea Schaerf","doi":"10.1016/j.ejco.2021.100024","DOIUrl":"https://doi.org/10.1016/j.ejco.2021.100024","url":null,"abstract":"<div><p>We consider the Minimum Interference Frequency Assignment Problem and we propose a novel Simulated Annealing approach that makes use of a portfolio of different neighborhoods, specifically designed for this problem.</p><p>We undertake at once the two versions of the problem proposed by Correia (2001) and by Montemanni et al. (2001), respectively, and the corresponding benchmark instances. With the aim of determining the best configuration of the solver for the specific version of the problem we perform a comprehensive and statistically-principled tuning procedure.</p><p>Even tough a totally precise comparison is not possible, the experimental analysis show that we outperform all previous results on most instances for the first version of the problem, and we are at the same level of the best ones for the second version.</p><p>As a byproduct of this research, we designed a new robust file format for instances and solutions, and a data repository for validating and maintaining the available solutions.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100024"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440621001519/pdfft?md5=bd15ad8aa76d9ff1c05a75649a88a79a&pid=1-s2.0-S2192440621001519-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92106812","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: 3

Newton-MR: Inexact Newton Method with minimum residual sub-problem solver Newton- mr:带最小残差子问题求解的不精确牛顿法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100035

Fred Roosta , Yang Liu , Peng Xu , Michael W. Mahoney

{"title":"Newton-MR: Inexact Newton Method with minimum residual sub-problem solver","authors":"Fred Roosta , Yang Liu , Peng Xu , Michael W. Mahoney","doi":"10.1016/j.ejco.2022.100035","DOIUrl":"10.1016/j.ejco.2022.100035","url":null,"abstract":"<div><p>We consider a variant of inexact Newton Method <span>[20]</span>, <span>[40]</span>, called Newton-MR, in which the least-squares sub-problems are solved approximately using Minimum Residual method <span>[79]</span>. By construction, Newton-MR can be readily applied for unconstrained optimization of a class of non-convex problems known as invex, which subsumes convexity as a sub-class. For invex optimization, instead of the classical Lipschitz continuity assumptions on gradient and Hessian, Newton-MR's global convergence can be guaranteed under a weaker notion of joint regularity of Hessian and gradient. We also obtain Newton-MR's problem-independent local convergence to the set of minima. We show that fast local/global convergence can be guaranteed under a novel inexactness condition, which, to our knowledge, is much weaker than the prior related works. Numerical results demonstrate the performance of Newton-MR as compared with several other Newton-type alternatives on a few machine learning problems.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100035"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000119/pdfft?md5=d469cd05ef15c6b063a51fd431c7a8dd&pid=1-s2.0-S2192440622000119-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123761580","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: 8