Abstract
The backroom of retail stores has structural differences when compared with other warehouses and distribution centres, which are more traditionally studied in the literature. This paper presents a mathematical optimisation approach for an unequal area facility layout problem, applied in designing the backroom layout in grocery retail. A set of rectangular facilities (backroom departments) with given area requirements has to be placed, without overlapping, on a limited floor space (backroom area), which can have a regular or an irregular shape. The objective is to find the location and format of the storage departments, such that the walking distances in the store by store employees are minimised. The proposed approach is tested in a European grocery retailer. In the computational experiments, several real store layouts are compared with the ones suggested by the proposed model. The decrease in the walking distances is, on average, 30 percent. In order to understand what the current designers' strategy is, a set of scenarios was created and compared with the real layouts. Each scenario ignores a characteristic of the problem. The goal is to understand what aspect designers are currently discarding. The findings indicate that, currently, designers neglect the different replenishment frequencies of storage departments.

Abstract
Omnichannel retailers are increasingly introducing subscription-based delivery services. By subscribing to this service and paying fees upfront, customers are entitled to have orders delivered to their home for a given period without paying any extra delivery charge. We analyze the resulting changes in customer behavior from two perspectives:(i) ordering behavior and (ii) delivery preferences. The model is estimated from the online transactional data of a grocery retailer and combines matching and difference in-differences approaches. We confirm that subscription customers spend more per month and purchase more frequently online than customers without subscriptions. However, this outcome is compromised by shifts towards narrower time slots in the mornings and at night, where slots are requested with less advance notice. When weighing the increased revenue and higher operational costs, we show that subscriptions have a negative impact on a retailer's incremental profit. This remains valid for a wide range of assumptions about (i) the cannibalisation of sales from the retailer's offline business, (ii) picking cost and (iii) delivery cost. To mitigate the impact of subscriptions on retailer profits, we develop a data-driven algorithm that predicts whether certain customers should receive promotions for the subscription plan, rather than it being advertised to all customers. As an extension, we also study whether the addition of a minimum order threshold to subscription plans changes consumer behaviour. We find that this introduction encourages customers to seek more variety and increase their basket size, but does not reduce their order frequency, a phenomena which may be ascribed to cross-selling.

Abstract
Soon, a new generation of Collaborative Robots embodying Human-Robot Teams (HRTs) is expected to be more widely adopted in manufacturing. The adoption of this technology requires evaluating the overall performance achieved by an HRT for a given production workflow. We study this performance by solving the underlying scheduling problem under different production settings. We formulate the problem as a Multimode Multiprocessor Task Scheduling Problem, where tasks may be executed by two different types of resources (humans and robots), or by both simultaneously. Two algorithms are proposed to solve the problem - a Constraint Programming model and a Genetic Algorithm. We also devise a new lower bound for benchmarking the methods. Computational experiments are conducted on a large set of instances generated to represent a variety of HRT production settings. General instances for the problem are also considered. The proposed methods outperform algorithms found in the literature for similar problems. For the HRT instances, we find optimal solutions for a considerable number of instances, and tight gaps to lower bounds when optimal solutions are unknown. Moreover, we derive some insights on the improvement obtained if tasks can be executed simultaneously by the HRT. The experiments suggest that collaborative tasks reduce the total work time, especially in settings with numerous precedence constraints and low robot eligibility. These results indicate that the possibility of collaborative work can shorten cycle time, which may motivate future investment in this new technology.

Abstract
Omnichannel retailing and digitalization result in considerable challenges for the management and optimization of retail operations. The continued demand of quantitative insights, their practical need, and the growing availability of data motivates an increasing number of scientists and practitioners to intensify research on demand and supply-related issues in retailing. This featured cluster provides the state-of-the art literature on forecasting and digitalization technologies, channel structures and delivery concepts as well as logistics in omnichannel and online retailing. The featured cluster contains 17 articles that deal with such topics. © 2021 Elsevier B.V.

Abstract
Technological advances in the last two decades have aroused great interest in the class of dynamic vehicle routing problems (DVRPs), which is reflected in the significant growth of the number of articles published in this period. Our work presents a comprehensive review of the DVRP literature of the last seven years (2015-2021) focusing mainly on applications and solution methods. Consequently, we provide a taxonomy of the problem and a taxonomy of the related solution methods. The papers considered for this review are discussed, analyzed in detail and classified according to the proposed taxonomies. The results of the analysis reveal that 65% of the articles deal with dynamic and stochastic problems (DS) and 35% with dynamic and deterministic problems (DD). With respect to applications, 40% of articles correspond to the transportation of goods, 17.5% to services, 17.5% to the transport of people and 25% to generic applications. Among the solution methods, heuristics and metaheuristics stand out. We discussed the application opportunities associated with DVRPs in recent business models and new concepts of logistical operations. An important part of these new applications that we found in our review is in the segment of business-to-consumer crowd-sourced services, such as peer-to-peer ride-sharing and online food ordering services. In our review many of the applications fall into the stochastic and dynamic category. This means that for many of these applications, companies usually possess historical data about the dynamic and uncertainty sources of their routing problems. Finally, we present the main solution streams associated with DVRPs.

Abstract
The fast-moving consumer goods sector relies on economies of scale. However, its assortments have been overextended as a means of market share appropriation and top-line growth. This paper studies the se-lection of the optimal set of products for fast-moving consumer goods producers to offer, as there is no previous model for product line selection that satisfies the requirements of the sector. Our mixed -integer programming model combines a multi-category attraction model with a capacitated lot-sizing problem, shared setups and safety stock. The multi-category attraction model predicts how the demand for each product responds to changes within the assortment. The capacitated lot-sizing problem allows us to account for the indirect production costs associated with different assortments. As seasonality is prevalent in consumer goods sales, the production plan optimally weights the trade-off between stocking finished goods from a long run with performing shorter runs with additional setups. Finally, the safety stock extension addresses the effect of the demand uncertainty associated with each assortment. With the computational experiments, we assess the value of our approach using data based on a real case. Our findings suggest that the benefits of a tailored approach are at their highest in scenarios typical fast-moving consumer goods industry: when capacity is tight, demand exhibits seasonal patterns and high service levels are required. This also occurs when the firm has a strong competitive position and consumer price-sensitivity is low. By testing the approach in two real-world instances, we show that this decision should not be made based on the current myopic industry practices. Lastly, our approach obtains profits of up to 9.4% higher than the current state-of-the-art models for product line selection.