Fábio Silva Moreira

Abstract
The stochastic dynamic inventory-routing problem (SDIRP) is a fundamental problem within supply chain operations that integrates inventory management and vehicle routing while handling the stochastic and dynamic nature of exogenous factors unveiled over time, such as customer demands, inventory supply and travel times. While practical applications require dynamic and stochastic decision-making, research in this field has only recently experienced significant growth, with most inventory-routing literature focusing on static variants. This paper reviews the current state of research on SDIRPs, identifying critical gaps and highlighting emerging trends in problem settings and decision policies. We extend the existing inventory-routing taxonomies by incorporating additional problem characteristics to better align models with real-world contexts. As a result, we highlight the need to account for further sources of uncertainty, multiple-supplier networks, perishability, multiple objectives, and pickup and delivery operations. We further categorize each study based on its policy design, investigating how different problem aspects shape decision policies. To conclude, we emphasize that large-scale and real-time problems require more attention and can benefit from decomposition approaches and learning-based methods.

Abstract
We study the optimal online service for grocery retailers operating both physical and online stores. The challenge lies in optimizing the size of the online assortment and the delivery fees to maximize profitability across channels, while considering customer, operational, and market dynamics. Using transaction data from a major grocery retailer, we employ an alternative-specific conditional logit model to investigate how delivery fees, assortment size, network characteristics, and customer needs influence store choice and spending across physical and online channels. We develop a profitability model that incorporates online service variables, customer behavior, and operational costs, enabling us to explore optimal strategies under various conditions. By identifying favorable conditions for the online store and analyzing optimal service variables, we provide actionable insights for retailers. Our findings challenge common practices in omnichannel retail. We show that delivery fees should not merely cover costs but can be strategically set higher, particularly for retailers with strong offline presence. Additionally, while reducing fulfillment costs improves profitability, its impact is smaller than expected. Multichannel retailers can offset these costs by passing them on to customers, with minimal overall demand loss, as some customers opt to shop in physical stores rather than abandoning the retailer entirely. Lastly, maximizing the online assortment may not always be optimal, particularly if the operational inefficiencies and costs outweigh the value customers place on variety. Our methodological framework provides retailers the opportunity to align their online services with customer preferences and operational constraints and to leverage customer data in shaping their omnichannel strategies. © 2026 The Author(s)

Abstract
Forest management and operations planning involve complex decisions that integrate ecological knowledge, spatial data, and analytical tools to balance sustainable resource use with risk mitigation. Disturbances such as storms, diseases, and wildfires increasingly disrupt forest ecosystems and value chains. The timely removal, processing, and delivery of forest residues to bioenergy facilities are essential to reduce wildfire risk, prevent disease spread, and ensure operational continuity for forest managers and owners. This study presents a decision-support approach to address supply uncertainty caused by wildfires within the forest-to-bioenergy value chain. The methodology first generates multiple raw material variability scenarios using a fire simulation model, then clusters them according to post-fire biomass availability and probability of occurrence. These clusters are integrated into a two-stage stochastic optimization model incorporating a Conditional Value-at-Risk (CVaR) metric. Results show that the stochastic model with CVaR achieves the lowest total cost while ensuring complete processing of biomass under the most severe wildfire scenarios. The findings highlight the value of flexible and risk-aware planning strategies for forest operations, supporting decision-makers in balancing investments in processing capacity, cost efficiency, and post-disturbance resource utilization.

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
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.