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Summary of current and next issues

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Next issue articles are papers that have been copy-edited and typeset but not yet paginated for inclusion in an issue of the journal. The final version of articles can be downloaded from the "Current issue" and "Downloadable articles" section.

Current issue: volume 50 (1), January-June 2026

  • Scatter search: foundations and implementations (invited article)

    Manuel Laguna, Sergio Cavero and Rafael Martí

    Abstract: Scatter search is a population-based metaheuristic designed to solve complex optimization problems through structured solution combination and adaptive memory. Unlike traditional evolutionary algorithms, scatter search emphasizes deterministic strategies to balance intensification and diversification. We present a comprehensive review of scatter search and its connection to path relinking, covering their historical development, core methodology, and applications. Key components of scatter search include diversification generation, improvement, reference set updating, subset generation, and solution combination. Advanced strategies such as dynamic reference set updating, tiered memory structures, constructive and destructive neighborhoods, and vocabulary building enhance its performance and scalability. Scatter search has been successfully applied in scheduling, routing, bioinformatics, and software engineering. Hybridizations with other metaheuristics and integration with machine learning further expand its applicability. The review concludes with a tutorial on a scatter search Python implementation for 0-1 knapsack problems that includes a Jupyter Notebook with code, execution traces, visualizations, and didactic analyses.

    Keywords: scatter search, path relinking, metaheuristics, optimization

    Pages: 3–42

    DOI: 10.57645/20.8080.02.30

  • Non-crossing neural network quantile regression estimation for driving data with telematics

    Xenxo Vidal-Llana, Carlos Salort Sánchez, Vincenzo Coia and Montserrat Guillén

    Abstract: State-of-the-art methods for estimating extreme quantiles (value at risk, VaR) and their tail expectations (conditional tail expectation, CTE) under covariate control primarily rely on quantile regression but lack explicit constraints for non-crossing conditions. To address this, we introduce the non-crossing dual neural network, a deep learning model that simultaneously estimates VaRs and CTEs across multiple quantile levels, incorporates covariate dependence, and enables the reconstruction of individual conditional distributions and risk profiles while ensuring the natural order of quantile levels. Using a 2015 telematics dataset, the proposed methodology outperforms benchmark models while enforcing previously unaddressed conditions. The model can be used to identify risk within an insurance portfolio and to analyze extreme right-tail behaviour at the individual level.

    Keywords: risk evaluation, telematics, quantile regression, motor insurance, value at risk, conditional tail expectation

    Pages: 43–66

    DOI: 10.57645/20.8080.02.31

  • An algorithm for reconciling indicators across multiple dimensions: weighted iterative proportional fitting

    Jose M. Pavía, Josep Lledó and Priscila Espinosa

    Abstract: Reconciling multidimensional count data across multiple sources is a common challenge in social and economic research. Iterative proportional fitting is widely used for this purpose, but aligning indices under weighted sum-convex constraints calls for a more flexible approach. We introduce the weighted iterative proportional fitting algorithm, which incorporates sum-weighted constraints to adjust indicators—such as death-risk indices by wealth, habitat, and climate—while preserving marginal consistency. Weighted iterative proportional fitting has been implemented in an R package of the same name, enabling scholars, statisticians, and policymakers’ advisors, among others, to apply it easily to multidimensional data.

    Keywords: weighted iterative proportional fitting (WIPF), biproportional fitting, RAS, raking, matrix scaling, socioeconomic indices, official statistics

    Pages: 67–88

    DOI: 10.57645/20.8080.02.32

  • A gentle introduction to deep neural networks for operations researchers

    Pau Amaré and Jordi Castro

    Abstract: Computing a neural network is, in essence, a large unconstrained optimization problem, though this fact is often obscured by machine learning jargon. We present a gentle introduction to deep learning for operations researchers, describing in a didactic manner the underlying optimization problem and providing examples developed from scratch. These examples are solved using both standard modeling languages and modern machine learning frameworks. We conclude by discussing applications of neural networks in operations research, illustrated through a concrete example involving the knapsack problem.

    Keywords: operations research, nonlinear optimization, deep learning, neural networks, optimization modeling languages, machine learning frameworks

    Pages: 89–116

    DOI: 10.57645/20.8080.02.33

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