EURO Journal on Computational Optimization最新文献_第3页

On the asymptotic behavior of a higher-order extrapolation primal–dual interior-point method for nonlinear programming 非线性规划的高阶外推原对偶内点法的渐近性

IF 1.2

EURO Journal on Computational Optimization Pub Date : 2026-01-01 Epub Date: 2026-08-13 DOI: 10.1016/j.ejco.2026.100138

Pim Heeman, Anders Forsgren

{"title":"On the asymptotic behavior of a higher-order extrapolation primal–dual interior-point method for nonlinear programming","authors":"Pim Heeman, Anders Forsgren","doi":"10.1016/j.ejco.2026.100138","DOIUrl":"10.1016/j.ejco.2026.100138","url":null,"abstract":"<div><div>A trajectory-following primal–dual interior-point method solves nonlinear optimization problems with inequality and equality constraints by approximately finding points satisfying perturbed Karush–Kuhn–Tucker optimality conditions for a decreasing order of perturbation controlled by the barrier parameter. Under some conditions, there is a unique local correspondence between small residuals of the optimality conditions and points yielding that residual, and the solution on the barrier trajectory for the next barrier parameter can be approximated using an approximate solution for the current parameter. A framework using higher-order derivative information of the correspondence is analyzed in which an extrapolation step to the trajectory is first taken after each decrease of the barrier parameter upon reaching a sufficient approximation. It suffices asymptotically to only take extrapolation steps for convergence at the rate at which the barrier parameter decreases when using derivative information of high enough order. Numerical results for nonlinear programming problems are presented using extrapolation as an accelerator.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"14 ","pages":"Article 100138"},"PeriodicalIF":1.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148770134","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

Data-driven stochastic VRP: Integration of forecast duration into optimization for utility workforce management 数据驱动的随机VRP：将预测持续时间整合到公用事业劳动力管理的优化中

IF 1.2

EURO Journal on Computational Optimization Pub Date : 2026-01-01 Epub Date: 2026-06-19 DOI: 10.1016/j.ejco.2026.100132

Matteo Garbelli

{"title":"Data-driven stochastic VRP: Integration of forecast duration into optimization for utility workforce management","authors":"Matteo Garbelli","doi":"10.1016/j.ejco.2026.100132","DOIUrl":"10.1016/j.ejco.2026.100132","url":null,"abstract":"<div><div>This paper investigates the integration of machine learning forecasts of intervention durations into a stochastic variant of the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). In particular, we exploit tree-based gradient boosting (XGBoost) trained on eight years of gas meter maintenance data to produce point predictions and uncertainty estimates, which then drive a multi-objective evolutionary optimization routine. The methodology addresses uncertainty through sub-Gaussian concentration bounds for route-level risk buffers and explicitly accounts for competing operational KPIs through a multi-objective formulation. Empirical analysis of prediction residuals validates the sub-Gaussian assumption underlying the risk model. From an empirical point of view, our results report improvements of around 20–25% in operator utilization and completion rates compared with plans computed using default durations and unconditional stochastic baselines. The integration of uncertainty quantification and risk-aware optimization provides a practical framework for handling stochastic service durations in real-world routing applications.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"14 ","pages":"Article 100132"},"PeriodicalIF":1.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523406","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

Solving multi-leader-follower games via parametric optimization 通过参数优化求解多领导-随从博弈

IF 1.2

EURO Journal on Computational Optimization Pub Date : 2026-01-01 Epub Date: 2026-08-13 DOI: 10.1016/j.ejco.2026.100139

Sonja Steffensen, Addis Belete Zewde

引用次数: 0

A compact model for the home healthcare routing and scheduling problem 家庭医疗保健路由和调度问题的紧凑模型

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 Epub Date: 2024-12-05 DOI: 10.1016/j.ejco.2024.100101

Roberto Montemanni , Sara Ceschia , Andrea Schaerf

引用次数: 0

Speed planning by minimizing travel time and energy consumption 通过最小化旅行时间和能量消耗来规划速度

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 Epub Date: 2025-07-18 DOI: 10.1016/j.ejco.2025.100112

Stefano Ardizzoni, Luca Consolini, Mattia Laurini, Marco Locatelli

引用次数: 0

Interior point methods in the year 2025 2025年的内部点法

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 Epub Date: 2025-02-13 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

On two vectorization schemes for set-valued optimization 集值优化的两种向量化方案

IF 1.7

EURO Journal on Computational Optimization Pub Date : 2025-01-01 Epub Date: 2025-10-10 DOI: 10.1016/j.ejco.2025.100120

Gabriele Eichfelder, Tobias Gerlach, Ernest Quintana, Stefan Rocktäschel

{"title":"On two vectorization schemes for set-valued optimization","authors":"Gabriele Eichfelder, Tobias Gerlach, Ernest Quintana, Stefan Rocktäschel","doi":"10.1016/j.ejco.2025.100120","DOIUrl":"10.1016/j.ejco.2025.100120","url":null,"abstract":"<div><div>In this paper, we investigate two known solution approaches for set-valued optimization problems, both of which are based on so-called vectorization strategies. These strategies consist of deriving a parametric family of multi-objective optimization problems whose optimal solution sets approximate those of the original set-valued problem with arbitrary accuracy in a certain sense. Thus, these approaches can serve as a basis for the numerical solution of set-valued optimization problems using established solution algorithms from multi-objective optimization. We show that many properties that have already been obtained for one of the two vectorization schemes also hold for the other similarly. Thereby, it turns out that under certain assumptions there exist problem classes for both vectorization schemes in which the set-valued initial problems are even equivalent to the corresponding multi-objective replacement problems. This property is fulfilled, for example, for set-valued optimization problems with a finite feasible set, a polytope-valued objective map, or a convex graph. This was already known for one of the two vectorization schemes, and could now also be shown for the other scheme.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100120"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust personalized pricing under uncertainty of purchase probabilities 购买概率不确定下的鲁棒个性化定价

IF 1.7

EURO Journal on Computational Optimization Pub Date : 2025-01-01 Epub Date: 2025-08-06 DOI: 10.1016/j.ejco.2025.100114

Shunnosuke Ikeda , Naoki Nishimura , Noriyoshi Sukegawa , Yuichi Takano

{"title":"Robust personalized pricing under uncertainty of purchase probabilities","authors":"Shunnosuke Ikeda , Naoki Nishimura , Noriyoshi Sukegawa , Yuichi Takano","doi":"10.1016/j.ejco.2025.100114","DOIUrl":"10.1016/j.ejco.2025.100114","url":null,"abstract":"<div><div>This paper is concerned with personalized pricing models aimed at maximizing the expected revenues or profits for a single item. While it is essential for personalized pricing to predict the purchase probabilities for each consumer, these predicted values are inherently subject to unavoidable prediction errors that can negatively impact the realized revenues and profits. To resolve this challenge, we focus on robust optimization techniques that yield reliable solutions to optimization problems under uncertainty. Specifically, we propose a robust optimization model for personalized pricing that accounts for the uncertainty of predicted purchase probabilities. This model can be formulated as a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. We also develop a Lagrangian decomposition algorithm combined with the golden section search to efficiently find high-quality solutions to large-scale problems. Experimental results demonstrate the effectiveness of our robust optimization model and highlight the utility of our Lagrangian decomposition algorithm in terms of both computational efficiency and solution quality.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100114"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating the Monte-Carlo Tree Search approach for the job shop scheduling problem 研究了作业车间调度问题的蒙特卡洛树搜索方法

IF 1.7

EURO Journal on Computational Optimization Pub Date : 2025-01-01 Epub Date: 2025-10-09 DOI: 10.1016/j.ejco.2025.100118

Laurie Boveroux, Damien Ernst, Quentin Louveaux

引用次数: 0

Efficient use of optimality conditions in Interval Branch and Bound methods 区间分支和定界方法中最优性条件的有效利用

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 Epub Date: 2025-05-09 DOI: 10.1016/j.ejco.2025.100108

Mihály Gencsi, Boglárka G.-Tóth

{"title":"Efficient use of optimality conditions in Interval Branch and Bound methods","authors":"Mihály Gencsi, Boglárka G.-Tóth","doi":"10.1016/j.ejco.2025.100108","DOIUrl":"10.1016/j.ejco.2025.100108","url":null,"abstract":"<div><div>The Interval Branch and Bound (IBB) method is a widely used approach for solving nonlinear programming problems where a rigorous solution is required. The method uses Interval Arithmetic (IA) to handle rounding errors in calculations. In the literature, a wide range of variations of IBB exists. However, few IBB implementations use the Karush-Kuhn-Tucker (KKT) or the Fritz-John (FJ) optimality conditions to eliminate non-optimal boxes. The application of the FJ conditions implies to solve a system of interval linear equations, which is often challenging due to overestimation of the boxes. This study focuses on the geometric perspective of the FJ optimality conditions. A preliminary test is introduced, namely the Geometrical Test, which tries to decide when the optimality conditions cannot hold or whether it is convenient to compute the Fritz-John Test. Furthermore, a test case generator is presented that transforms unconstrained problems into constrained test cases by setting a given number of active and inactive constraints at a global optimizer. The efficiency of the Geometrical Test was considered through computational experiments on the generated benchmark. Six variations of the IBB were compared, with or without the FJ condition system and Geometrical Test. The best methods for solving the 272 generated test cases use the designed Geometrical Test with the Lagrange estimator and the Newton step on the normalized interval FJ conditions in most cases.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100108"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0