Abstract
This work studies a two-echelon distribution system, in the context of city logistics, where storage is not permitted at intermediate transfer locations. Therefore, vehicles operating at both echelons need to be synchronised in time and space, allowing loads to be directly transferred from the first to the second echelon vehicles. Moreover, the problem considers that vehicles operating at the first echelon can perform direct deliveries to customers, that load transfers may occur at some customers' locations, and that vehicles operating at the second echelon are able to perform multiple trips before returning to the depot at the end of the day. To address this problem, we propose a novel mixed integer programming (MIP) model for the two-echelon, multi-trip vehicle routing problem with satellite synchronisation and direct deliveries (2E-MTVRPSS-DD). We tighten this formulation with several sets of valid inequalities, including symmetry breaking constraints based on lexicographical ordering, vehicle rounded capacity constraints, and satellite rounded capacity constraints. We test the model using a commercial solver with newly generated instances, and present computational results, as well as an evaluation of the performance of the proposed valid inequalities. The results show that for relatively small instances, the proposed model is able to solve the problem optimally, but in general, is unable to solve large instances in acceptable computational time, even when considering the proposed valid inequalities. Nevertheless, we show that adding these valid inequalities has a positive impact in improving the model's linear relaxation, with better lower and upper bounds, and ultimately in improving the MIP gaps. Moreover, we show that adding symmetry breaking constraints based on lexicographical ordering has a negative impact, in terms of computational time, for the solver to find a first upper bound, and that this issue may be overcome by warm-starting the MIP model.

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.

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
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 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
Problem Definition: Grocery retailers increasingly fulfil online orders from existing stores rather than from dark stores (dedicated warehouses/fulfilment centers). Theory and practice debate whether this omnichannel strategy reduces or exacerbates food waste. There is a lack of empirical evidence on the impact on waste of introducing fulfillment to brick-and-mortar stores. Our study provides the first causal estimate of this impact and traces the operational levers behind it.&nbsp; <br><br>Methodology/results:Exploiting the staggered roll-out of store-basd online fulfilment at a European grocer, we apply difference-indifferences estimators to a 48-month panel of 27 stores, and six fresh categories. The conceptual framework decomposes the waste ratio (waste-to-sales) into inventory planning (inventory-to-sales) and inventory execution (waste-to-inventory) components. Introducing online fulfilment increases the waste ratio by 0.5 percentage points-about 15 percent relative to stores that are do not serve online customers. This rise is mostly explained by higher inventory-to-sales levels driven by a 25 percent jump in demand variability; inventory execution does not deteriorate significantly.&nbsp;<br><br>Managerial Implications:Fulfilling online orders from brick-and-mortar stores can backfire on waste unless retailers mitigate added demand variability via prioritizing tailored inventory management strategies and selecting stores that may mitigate demand variability and maximize overall sales. Our findings reconcile conflicting predictions in the literature by demonstrating that pooling benefits can be overwhelmed by variability-driven stock increases when service-level targets remain unchanged.