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EURO Journal on Computational Optimization
Scopus
ISSN 2192-4406 · Elsevier · 245 artikel di indeks
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Artikel Jurnal
Food security under trade policies and perishability: Extensions of the multicommodity network equilibrium model with monotonicity analysis
Building on recent work about multicommodity agricultural trade and food security, this paper develops a network equilibrium model with nutritional constraints formulated as a variational inequality problem. A key contribution of the paper is its monotonicity analysis of the associated operator. We…
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Artikel Jurnal
MineReduce-based heuristic for the maximum diversity problem
The Maximum Diversity Problem (MDP) is a challenging NP-hard optimization problem with applications in various domains, including social network analysis, bioinformatics, and facility location. Traditional heuristics often struggle to find high-quality solutions for large-scale MDP instances within…
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On the integration of Dantzig-Wolfe and Fenchel decompositions via directional normalizations
Strengthening linear relaxations and the bounds of mixed integer linear programs has been an active research topic for decades. Enumeration-based methods for integer programming like linear programming-based branch-and-bound exploit strong dual bounds to discard unpromising regions of the feasible…
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A combined linear and nonlinear presolve for nonlinear optimization
Presolve techniques have been experimentally shown to significantly accelerate the performance of optimization solvers, achieving speedups of several orders of magnitude in widely used benchmarks. Building on the success of these techniques in linear and mixed-integer linear optimization problems…
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Investigating the Monte-Carlo Tree Search approach for the job shop scheduling problem
The Job Shop Scheduling Problem (JSSP) is a well-known optimization problem in manufacturing, where the goal is to determine the optimal sequence of jobs across different machines to minimize a given objective. In this work, we focus on minimizing the weighted sum of job completion times. We…
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Artikel Jurnal
A distributionally robust machine learning model of simultaneous classification and feature selection under data uncertainty: Theory, methods, and application to the identification of Alzheimer's disease using handwriting
In this paper, we introduce an efficient machine learning method based on robust Support Vector Machines (SVMs) that simultaneously classifies data and selects relevant features whilst accounting for data uncertainty. Based on Wasserstein distributionally robust optimization, we develop…
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Efficient use of optimality conditions in Interval Branch and Bound methods
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…
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Artikel Jurnal
A tutorial on properties of the epigraph reformulation
This paper systematically surveys useful properties of the epigraph reformulation for optimization problems, and complements them by some new results. We focus on the complete compatibility of the original formulation and the epigraph reformulation with respect to solvability and unsolvability, the…
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Artikel Jurnal
Robust personalized pricing under uncertainty of purchase probabilities
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…
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Decentralised convex optimisation with probability-proportional-to-size quantization
Communication is one of the bottlenecks of distributed optimisation and learning. To overcome this bottleneck, we propose a novel quantization method that transforms a vector into a sample of components' indices drawn from a categorical distribution with probabilities proportional to values at…
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Randomized block coordinate DC algorithm
We introduce an extension of the Difference of Convex Algorithm (DCA) in the form of a randomized block coordinate approach for problems with separable structure. For n coordinate-blocks and k iterations, our main result proves a non-asymptotic convergence rate of O(n/k) in expectation, with…
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Artikel Jurnal
A compact model for the home healthcare routing and scheduling problem
Home healthcare has become more and more central in the last decades, due to the advantages it can bring to both healthcare institutions and patients. Planning activities in this context, however, presents significant challenges related to route planning and mutual synchronization of caregivers.In…
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A diving heuristic for mixed-integer problems with unbounded semi-continuous variables
Semi-continuous decision variables arise naturally in many real-world applications. They are defined to take either value zero or any value within a specified range, and occur mainly to prevent small nonzero values in the solution. One particular challenge that can come with semi-continuous…
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Interior point methods in the year 2025
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…
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Resource constraint scheduling on two dedicated machines: Application to avionics
In civil aircraft, two partially redundant hydraulic circuits typically power various systems. During assembly, a critical phase involves simultaneously rinsing and purging these hydraulic circuits using loops. Precedence constraints are necessary to prevent the recontamination of already rinsed…
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Revisiting a Cornuéjols-Nemhauser-Wolsey formulation for the p-median problem
We revisit a formulation for the simple plant facility location and p-median problems introduced by Cornuéjols, Nemhauser and Wolsey (1980). Despite being the smallest known formulation regarding the number of variables, this formulation is barely used or cited in the literature. Here, we…
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Artikel Jurnal
Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree
The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. Tree ensemble methods, such as Random Forests or XgBoost, are powerful learning tools for classification tasks. However, while combining…
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Artikel Jurnal
On improvements of multi-objective branch and bound
Branch and bound methods which are based on the principle “divide and conquer” are a well established solution approach in single-objective integer programming. In multi-objective optimization, branch and bound algorithms are increasingly attracting interest. However, the larger number of…