Abstract
To maintain high levels of efficiency and compliance with delivery dates, automotive repair shops must have a good system for scheduling their activities. The scheduling of the activities of an automotive repair shop is a very complex task to be performed manually. Throughout this work, a Decision Support System (DSS) was developed and tested that considers two major constraints in an automotive workshop: human resources (technicians) and physical resources (work stalls). The proposed DSS has an embedded MIP model that assigns a technician and a work stall to each job, according to the input conditions. The DSS also generates schedules with the planning of technicians and jobs. The system was tested with real data from an automotive workshop and was able to create plans and schedules not only for the human and physical resources in but also to analyse the limiting resources of the workshop.

Abstract
The main goal of this paper was to develop, implement, and test a practical framework for large-scale last-mile delivery problems that employ a combination of optimisation and machine learning while focussing on different routing methods. Delivery companies in big cities choose delivery orders based on the tacit knowledge of experienced drivers, since solving a large optimisation model with several variables is not a practical solution to meet their daily needs. This framework includes three phases of districting, sequencing, and routing, and in total 30 different variants were tested in different capacities. Using the power of machine learning, a model is trained and tuned to predict driving road distances, allowing the implementation of the whole framework and improving performance from analysing 2983 stops in several hours to 58,192 stops in less than 15 minutes. The results demonstrated that Inter 1 - Centroids is the best inter-district connection method, and one of the best variants in this framework is variant 26 which managed to decrease up to 34,77% total distances with 79 fewer drivers in a full month analysis compared to the original routes of the delivery company. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

Abstract
At the practical level, the static stability constraint is one of the most important constraints in practical pallet loading problems, such as air cargo palletizing. Approaches to modeling static stability, which range from base support and mechanical equilibrium calculations to physical simulation, differ in workflow, focus, and assumptions, so choosing the right static stability approach has a substantial impact on the quality of the solution and, ultimately, on loading security. To date, little research has investigated the structural differences between approaches. The aim of this paper is to integrate knowledge and shed light on the applicability of the different approaches for the practical scenario of air cargo palletizing. We tackle this problem through (1) a reformulation and extension of static stability toward loading stability, (2) a conceptual analysis of current approaches, and (3) benchmarking that employs an independent multibody simulation on multiple heterogeneous datasets. Our results show that all approaches are prone to structure errors and vary significantly in their premises and information usage. Further, full base support is revealed to be the most restrictive approach by far, while physical simulation achieves the greatest accuracy. Given the trade-off between accuracy and runtime, the mechanical equilibrium approach is a good choice, while partial base support performs best for lower support values.

Abstract
This special issue showcases state-of-the-art research at the intersection of analytics and retail operations. As the retail landscape becomes increasingly complex – driven by omnichannel strategies, evolving customer expectations, and a surge in data availability – analytics has emerged as a critical enabler of operational efficiency, customer experience, responsiveness, and sustainability and ethics. Collectively, these contributions demonstrate how advanced analytics can support retailers in navigating uncertainty, personalizing services, and scaling up innovation across formats and channels. The articles featured in this issue address a diverse set of decision domains, including warehousing, inventory and assortment planning, and distribution and last-mile delivery. Methodologically, they span descriptive, prescriptive, and hybrid approaches, leveraging tools such as machine learning, stochastic modeling, and dynamic optimization. By grounding models in real-world data and focusing on practical implementation, the issue provides actionable insights for both scholars and practitioners. It also highlights emerging opportunities for future research on behavioral integration, human-machine collaboration, and the ethical dimensions of retail analytics. © 2025 Elsevier B.V.

Abstract
In online grocery retail, out-of-stocks can cause order fulfillment failures. Store-based fulfillment models have heightened this challenge. Here, online customers often receive orders not fulfilled as expected, with products being substituted, partially fulfilled, or reimbursed. When order fulfillment fails, the customer may change future ordering behavior by delaying the next order or by spending less in the online channel. Using data from the online operation of a leading omnichannel grocery retailer, we evaluate the magnitude of impact on the next order when the prior one is not fulfilled as expected. We also explore the role of retailer efforts in mitigating this impact. We find that failures significantly delay the time to the next order by 7.22% on average, with delays becoming more pronounced for non-perishable products. Spending reductions are especially evident when promoted items fail to ship. Mitigation efforts, substitutions in particular, often exacerbate delays and compound the dissatisfaction. Although substitutions help recover lost sales, they negatively impact future customer behavior. This suggests that selective stockout prevention, coupled with improved substitution practices, should be prioritized to optimize economic and customer outcomes.

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.