Abstract
Retailers offering Attended Home Delivery (AHD) struggle with thin profit margins due to high delivery costs and constrained routing flexibility. AHD requires retailers and customers to agree on specific time windows, limiting operational efficiency and increasing fleet requirements, particularly when customer preferences tend to cluster around peak times. While retailers have some ability to influence customer choices through pricing and availability strategies, failing to account for fleet costs and delivery constraints can lead to inefficient operations and reduced profitability. This study introduces an integrated approach to fleet sizing and time-window pricing for price-sensitive customers. We propose a Mixed Integer Programming (MIP) model that maximizes profit by balancing revenue and delivery costs, leveraging a nonparametric rank-based choice model to capture customer behavior while explicitly considering routing constraints and fleet ownership expenses over multiple periods. Using computational experiments on small-sized instances inspired by real-world data, we evaluate the impact of explicitly modeling routing costs, compare different pricing strategies, examine the effects of multi-period fleet planning, and assess sensitivity to varying customer and cost conditions. Results show that explicitly modeling routing constraints reduces profit loss by 29% compared to traditional cost approximations but increases computational complexity. To address this, we develop a Fix & Optimize (F&O) matheuristic approximate solution method that enables the application of our model to larger instances. Our findings emphasize the need for retailers to integrate demand management and fleet planning to optimize operational profitability.

Abstract

Abstract
This chapter aims to understand how Esports organizations can improve digital marketing strategies, considering the unique characteristics of this sector and the importance of maintaining solid relationships with the target audience. The research was carried out using a mixed methodology, which included the application of quantitative research to evaluate the behaviors of Esports fans and a qualitative literature review to explore the trends and challenges of digital marketing in this context. The results show that the esports audience consists predominantly of young males, with a strong interest in video games, technology and pop culture. The personalization of digital strategies, focusing on platforms such as YouTube and Twitch, as well as the use of promotions and sweepstakes, proved essential for audience engagement. Although the use of influencers has a neutral perception, campaigns that offer direct benefits, such as promotions, are more attractive.

Abstract

Abstract
We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation. In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt. We further propose aBAE, an annealed variant of BAE drawing on methods from statistical inference, to enhance robustness. Our proposals are parallelizable in both quantum and classical components, offer tools for fast noise model assessment, and can leverage preexisting information. Additionally, they accommodate experimental limitations and preferred cost trade-offs. We propose a robust benchmark for amplitude estimation algorithms and use it to test BAE against other approaches, demonstrating its competitive performance in both noisy and noiseless scenarios. In both cases, it achieves lower error than any other algorithm as a function of the cost. In the presence of decoherence, it is capable of learning when other algorithms fail. © 2025 Elsevier B.V., All rights reserved.

Abstract
In an era where robots are becoming an integral part of human quotidian activities, understanding how they function is crucial. Among the inherent building complexities, from electronics to mechanics, path planning emerges as a universal aspect of robotics. The primary contribution of this work is to provide an overview of the current state of robot path planning topics and a comparison between those same algorithms and its inherent characteristics. The path planning concept relies on the process by which an algorithm determines a collision-free path between a start and an end point, optimizing parameters such as energy consumption and distance. The quest for the most effective path planning method has been a long-standing discussion, as the choice of method is highly dependent on the specific application. This review consolidates and elucidates the categories of path planning methods, specifically classical or analytical methods, and computer intelligence methods. In addition, the operational principles of these categories will be explored, discussing their respective advantages and disadvantages, and reinforcing these discussions with relevant studies in the field. This work will focus on the most prevalent and recognized methods within the robotics path planning problem, being mobile robotics or manipulator arms, including Cell Decomposition, A*, Probabilistic Roadmaps, Rapidly-exploring Random Trees, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Artificial Potential Fields, Fuzzy, and Neural Networks. Following the detailed explanation of these methods, a comparative analysis of their advantages and drawbacks is organized in a comprehensive table. This comparison will be based on various quality metrics, such as the type of trajectory provided (global or local), the scenario implementation type (real or simulated scenarios), testing environments (static or dynamic), hybrid implementation possibilities, real-time implementation, completeness of the method, consideration of the robot's kinodynamic constraints, use of smoothing techniques, and whether the implementation is online or offline.