Abstract
The cost of transportation of raw materials is a significant part of the procurement costs in the forestry industry. As a result, routing and scheduling techniques were introduced to the transportation of raw materials from extraction sites to transformation mills. However, little to no attention has been given to date to the material reception process at the mill. Another factor that motivated this study was the formation of large waiting queues at the mill gates and docks. Queues increase the reception time and associated costs. This work presents the development of a scheduling and reception system for deliveries at a mill. The scheduling system is based on Trucking Appointment Systems (TAS), commonly used at maritime ports, and on revenue management concepts. The developed system allocates each delivery to a timeslot and to an unloading dock using revenue management concepts. Each delivery is segmented according to its priority. Higher-segment deliveries have priority when there are multiple candidates to be allocated for one timeslot. The developed scheduling system was tested on a set of 120 daily deliveries at a Portuguese paper pulp mill and led to a reduction of 66% in the daily reception cost when compared to a first-in, first-out (FIFO) allocation approach. The average waiting time was also significantly reduced, especially in the case of high-priority trucks.

Abstract
The Vehicle Routing Problem with Synchronization (VRPSync) aims to minimise the total routing costs while considering synchronization requirements that must be fulfilled between tasks of different routes. These synchronization requirements are especially relevant when it is necessary to have tasks being performed by vehicles within given temporal offsets, a frequent requirement in applications where multiple vehicles, crews, materials, or other resources are involved in certain operations. Although several works in the literature have addressed this problem, mainly the deterministic version has been tackled so far. This paper presents a robust optimization approach for the VRPSync, taking into consideration the uncertainty in vehicle travel times between customers. This work builds on existing approaches in the literature to develop mathematical models for the Robust VRPSync, as well as a branch-and-cut algorithm to solve more difficult problem instances. A set of computational experiments is also devised and presented to obtain insights regarding key performance parameters of the mathematical models and the solution algorithm. The results suggest that solution strategies where certain standard problem constraints are only introduced if a candidate solution violates any of those constraints provide more consistent improvements than approaches that rely on tailor-made cutting planes, added through separation routines. Furthermore, the analysis of the Price of Robustness indicators shows that the adoption of robust solutions can have a significant increase in the total costs, however, this increase quickly plateaus as budgets of uncertainty increase.

Abstract
To get products delivered, clients and retailers agree on a delivery time window. We collaborated with an online retailer to develop a real-world application aimed at dynamically determining the delivery fee for each time window while ensuring the explainability of the pricing policy. This sequential decision-making problem arises as new customers continuously arrive. The objective is to maximize the final profit, given by the sum of baskets and delivery fees, discounted by the transportation and fleet costs. As multiple customers share the same delivery route, the costs are distributed among them, complicating the calculation of the marginal cost of each customer. Our study employs Genetic Programming (GP) to create explainable and easy-to-compute pricing policies to determine the delivery fees. These policies, expressed as mathematical formulas, rank price panels combinations of time slots and corresponding fees to identify optimal prices for each customer. The inputs to the GP algorithm capture the current state of the system, including factors such as capacity, customer location, and basket value. The resulting expressions offer operational managers a transparent pricing policy that allows them to maximize total profit.

Abstract
The sustainable utilization of forest biomass for bioenergy production is increasingly challenged by the variability and unpredictability of raw material availability. These challenges are particularly critical in regions like Central Portugal, where seasonality, dispersed resources, and wildfire prevention policies disrupt procurement planning. This study investigates two flexibility strategies-dynamic network reconfiguration and operations postponement-as policy relevant tools to enhance resilience in forest-to-bioenergy supply chains. A novel mathematical model, the mobile Facility Location Problem with dynamic Operations Assignment (mFLP-dOA), is proposed and solved using a scalable matheuristic approach. Applying the model to a real case study, we demonstrate that incorporating temporary intermediate nodes and adaptable processing schedules can reduce costs by up to 17% while improving operational responsiveness and reducing non-productive machine time. The findings offer strategic insights for policymakers, biomass operators, and regional planners aiming to design more adaptive and cost-effective biomass supply systems, particularly under environmental risk scenarios such as summer operation bans. This work supports evidence-based planning and investment in flexible logistics infrastructure for cleaner and more resilient bioenergy supply chains.

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
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.