Abstract
In this study, we address the inventory decision problem of ameliorating goods by explicitly incorporating a demand spillover effect between product categories - an interaction that has received little attention in operations management. We first empirically demonstrate the existence of this spillover using multi-year sales data from 11 Port wine brands across 86 markets. Building on these insights, we integrate the spillover effect into a stochastic inventory decision model for a (Port) wine seller who must decide whether to sell existing inventory or continue aging it to offer higher-quality products in the future. The problem is formulated as a Markov Decision Process and solved using a forecast-based Deterministic Lookahead (DLA) approach and a Proximal Policy Optimization (PPO) algorithm. Our results show that accounting for the spillover effect can increase profits by up to 1.31%, and that both proposed solution methods outperform the myopic strategy currently applied by producers. While the DLA policy performs best under high forecast accuracy, the PPO algorithm proves more robust when uncertainty is high. The study contributes to bridging marketing and operations perspectives by quantifying the economic impact of spillover effects and providing decision-support tools for managing aged inventory under demand uncertainty.

Abstract
In modern industrial environments, ensuring the quality of manufactured components is critical, particularly when dealing with reflective surfaces that hinder conventional inspection techniques. Although deep learning-based methods offer robust solutions for visual defect detection, their performance often hinges on the availability of substantial annotated datasets. In industrial scenarios, labeling such datasets is costly and time-consuming. This study investigates applying sample selection techniques to reduce annotation efforts for porosity detection on machined aluminium parts. Several selection strategies were evaluated using a real-world dataset composed of high-resolution images, including uncertainty, diversity, random-based criteria, and hybrid combinations. The best-performing strategy, which combined entropy-based uncertainty, spatial diversity, and random-based, achieved an F1-score of 86.70% and a recall of 82.99% after ten iterations using only 2,400 annotated images, corresponding to 66.67% of the active learning pool. Although the fully supervised model achieved an F1-score of 88.84% and a recall of 86.30%, the proposed approach proved a competitive alternative. These results demonstrate that selective data annotation can significantly reduce labeling effort while maintaining reliable performance in defect detection, even under the challenging conditions posed by reflective industrial parts.

Abstract
Switched Reluctance Motors (SRMs) are becoming increasingly popular for various applications, including automotive applications. However, challenges such as torque ripple and vibration persist, limiting their performance. This chapter investigates the application of intelligent control strategies, particularly fuzzy logic, to mitigate these issues. Fuzzy logic modeling does not require an accurate mathematical model which is very difficult to obtain from a SRM because of its inherit nonlinearities. In this work a Fuzzy Logic Controller (FLC) applied to the speed control of an SRM, highlighting the advantages of FL over traditional methods in terms of flexibility and performance. A comparison is made between the FLC, a Sliding Mode Control (SMC), and a Proportional Integral (PI) controller. Simulation results using MATLAB/Simulink show that the FLC substantially reduces torque ripple, offering better overall performance in terms of smoothness and robustness under varying operational conditions. The findings demonstrate that FLC offers a more effective solution than conventional approaches for SRM applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
In perishable categories, assortment design entails a three-way trade-off among variety, profitability, and waste. Because products deteriorate over time, effective assortment management requires not only selecting the right product mix but also ensuring that items meet consumers' freshness expectations at the point of purchase. Although assortment optimization has been extensively studied, existing models typically ignore shelf life or treat it as a static availability constraint, overlooking its dynamic effect on demand, substitution, and the selling window. We address this gap by developing an assortment optimization framework that incorporates remaining shelf life (RSL) directly into consumer utility and captures RSL-driven substitution across products. Using transaction-level data from the non-dairy yogurt category of a major European grocery retailer, we estimate an RSL-aware multinomial logit model and evaluate alternative assortment strategies via simulation. Relative to a freshness-agnostic specification, the RSL-aware approach yields a 3.3% increase in expected profit under a profit maximization objective. Under a relative waste minimization objective, it reduces food waste by 0.7 percentage points. To ensure scalability in larger categories, we introduce two simple, objective-specific heuristics that use ranking rules to restrict the candidate assortment set. Despite their computational simplicity, these heuristics deliver assortments whose profit is, on average, within 1.42% of the exhaustiveenumeration reference profit and whose waste differs by only 0.04 percentage points. Overall, the results establish product freshness as a critical determinant of assortment performance. The framework links freshness-sensitive consumer behavior to operational decisions and provides retailers with practical guidance for improving both profitability and sustainability in perishable categories.

Abstract
The connection of distributed energy resources in distribution system have been increasing significantly, requiring new approaches as market-based flexibility solutions. This paper proposes the coordinated operation of on-load tap changer and flexibility services traded in a local market for voltage regulation in medium and low voltage grid. The wider action of on-load tap changer is used to restore voltages at the medium voltage feeder based on sensitivity coefficients. If voltage violations persist, flexibilities are traded in a local energy market with a cost-effective approach, where flexibility costs are minimized, and are activated according to their effectiveness indicated by sensitivity coefficients. Sensitivity coefficients are obtained in the medium voltage using an analytical approach that can be applied to multi-phase unbalanced systems, and in the low voltage using a data-driven approach due to their limited observability. Results show the proposed approach can be an effective solution to regulate voltages, combining the wider action of on-load tap changer with local flexibility, avoiding unnecessary tap changes and requesting a small volume of flexibility services.

Abstract
Switched reluctance generators (SRG) are one of the machines with huge potential in wind power generation due to their reliability and robust design. However, the inherent characteristics of SRGs lead to significant challenges in achieving high efficiency and low output current and torque ripple simultaneously. The performance of SRGs is hindered by conflicting requirements. To address these issues, this chapter presents an optimization control strategy aimed at improving the static performance of SRGs. The chapter discusses the application of the Particle Swarm Optimization (PSO) technique to optimize the commutation angles, specifically the turn-on (?on) and turn-off (?off) angles, for an 8/6 SRG. The proposed strategy consists of two main steps. First, a Maximum Power Point Tracking (MPPT) algorithm is implemented to maximize power output at varying rotor speeds, combined with a direct power control method to regulate the power generated by the SRG. Second, a multi-objective function is developed to evaluate the SRG performance, considering key factors such as power output, output current ripple, and torque ripple. The simulation results indicate that implementing optimized turn-on and turn-off angles leads to a reduction in torque ripple from -1.78 Nm using the conventional technique to -0.66 Nm with the proposed method, corresponding to an impressive 63% decrease. Furthermore, the optimization strategy effectively maximizes the efficiency of the system employing an MPPT approach, ensuring optimal energy conversion under varying operating conditions. Future research directions include experimental validation of the proposed control system on real hardware to assess its practical feasibility and performance under real-world operating conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.