Abstract
Online retail is transforming the way distribution networks are managed. One prominent change is that retailers can now use their full network to fulfil orders. This process involves allocating orders to fulfilment nodes and, depending on the setting, can include other operational decisions, such as order consolidation, shipping mode selection and product substitution. This order allocation problem (OAOR) has garnered considerable attention in recent years. However, there is no comprehensive view of what has been done in the literature, nor a consistent terminology across papers, which makes it hard to position existing work and identify research gaps. To address these concerns, we conduct a systematic literature review, where we find over 60 articles contributing to the OAOR literature. From this review, we formulate the baseline problem, consider multiple extensions, and identify key problem characteristics. Additionally, we analyse and categorize the solution methods found based on the optimization mechanism, policy class, and incorporation of future information and learning. Our review points to several avenues for future research, both in problems and in solution methods. © 2025 The Authors

Abstract

Abstract

Abstract
The Prisoner Transportation Problem is an NP-hard combinatorial problem and a complex variant of the Dial-a- Ride Problem. Given a set of requests for pick-up and delivery and a homogeneous fleet, it consists of assigning requests to vehicles to serve all requests, respecting the problem constraints such as route duration, capacity, ride time, time windows, multi-compartment assignment of conflicting prisoners and simultaneous services in order to optimize a given objective function. In this paper, we present anew solution framework to address this problem that leads to an efficient heuristic. A comparison with computational results from previous papers shows that the heuristic is very competitive for some classes of benchmark instances from the literature and clearly superior in the remaining cases. Finally, suggestions for future studies are presented.

Abstract
The detection of glucose is crucial for diagnosing diseases such as diabetes and enables timely medical intervention. In this study, a disposable enzymatic screen-printed electrode electrochemical biosensor enhanced with machine learning (ML) for quantifying glucose in serum is presented. The platinum working surface was modified by chemical adsorption with biographene (BGr) and glucose oxidase, and the enzyme was encapsulated in polydopamine (PDP) by electropolymerisation. Electrochemical characterisation and morphological analysis (scanning and transmission electron microscopy) confirmed the modifications. Calibration curves in Cormay serum (CS) and selectivity tests with chronoamperometry were used to evaluate the biosensor's performance. Non-linear ML regression algorithms for modelling glucose concentration and calibration parameters were tested to find the best-fit model for accurate predictions. The biosensor with BGr and enzyme encapsulation showed excellent performance with a linear range of 0.75-40 mM, a correlation of 0.988, and a detection limit of 0.078 mM. Of the algorithms tested, the decision tree accurately predicted calibration parameters and achieved a coefficient of determination above 0.9 for most metrics. Multilayer perceptron models effectively predicted glucose concentration with a coefficient of determination of 0.828, demonstrating the synergy of biosensor technology and ML for reliable glucose detection.

Abstract
Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights.