Heinrich Kuhn

This article introduces the first joint shelf design and space planning problem that considers two placement options for items—hanging and shelving—on flexible shelves. These types of shelves are used in sectors such as do-it-yourself and toy retail, and for household goods in grocery stores. However, they have received limited attention in the literature, which typically focuses on regular shelves with a single placement option: placing items on shelf panels. The problem requires three interdependent decisions: (1) the placement option for each item (shelving or hanging), (2) the shelf design (number and vertical positioning of shelf panels), and (3) the shelf space planning (assignment of facings as well as the vertical and horizontal positioning of items). We formalize this problem as a mixed-integer linear program (MILP) and develop a greedy multi-start matheuristic to efficiently solve practical instances. Computational experiments demonstrate that our algorithm outperforms both a commercial solver and a benchmark method for two-dimensional shelf space planning in terms of runtime and solution quality. A comprehensive analysis of synthetic instances and a real-world case study provides several managerial insights. First, hanging items enables more flexible use of shelf space, which is especially beneficial when item variety is high. However, when large vertical grabbing gaps must be considered, stacking items on shelf panels may be more advantageous. In turn, there is a risk of wasted space due to height or width mismatches. Second, the number and size of the segments on the shelf significantly influence layout profitability, highlighting the need to jointly optimize shelf design and space allocation. Third, it is common practice to arrange hanging items in horizontal rows, which can be facilitated by segmenting the shelf space. While this improves visual appeal, increasing the number of segments tends to reduce overall space utilization and profitability. Finally, a real-world case study involving 237 products across six categories from a European grocery retailer confirms the practical applicability of our approach. It demonstrates significant potential for improving space utilization and layout efficiency.

E-commerce retailers are challenged to maintain cost-efficiency and customer satisfaction while pursuing sustainability, especially in the last mile. In response, retailers are offering a range of delivery speeds, including same-day and instant options. Faster deliveries, while trending, often increase costs and emissions due to limited planning time and reduced consolidation opportunities in the last mile. In contrast, this paper proposes the inclusion of a slower delivery option, termed some-day. Slowing down the delivery process allows for greater shipment consolidation, achieving cost savings and environmental goals simultaneously. We introduce the dynamic and stochastic some-day delivery problem, which accounts for a latest delivery day, customer time windows, and capacity limitations within a multi-period planning framework. Our solution approach is based on addressing auxiliary prize-collecting vehicle routing problems with time windows (PCVRPTW) on a daily basis, where the prize reflects the benefit of promptly serving the customer. We develop a hybrid adaptive large neighborhood search with granular insertion operators, outperforming existing metaheuristics for PCVRPTWs. Our numerical study shows significant cost savings with only small increases in delivery times compared to an earliest policy.

Efficiently managing retail space is critical as the increase in product variety is in conflict with limited shelf space and instore replenishment constraints. This paper develops a general framework for retail space management and presents a decision support model with the related problems within the framework of optimizing assortment, shelf space assignment and replenishment. An integrative approach to these planning problems becomes particularly relevant for fast-moving consumer goods and groceries, where stores are regularly replenished from distribution centers. The planning problem at hand is a multi-product shelf space allocation problem where demand is a composite function of the shelf space allocated and assortment-related demand substitution, and actual replenishment practices from retail are incorporated. The model developed extends existing models of shelf space management by jointly considering space-elastic demand and assortment-based substitution and integrating restocking constraints. For the latter, we consider real-world replenishment processes of retailers that distinguish between period-based and ad-hoc replenishment from the backroom. We develop three solution approaches that are based on efficient pre-processing and a nonlinear binary integer programming formulation of the problem. The computation tests based on retail data show the efficiency of the solution approaches in terms of computation time and solution quality. We reveal the improvement in profit levels that can be achieved from integrating assortments, shelf space planning and replenishment where challenges arise in obtaining feasible solutions with limited shelf space and replenishment constraints. We also use sensitivity analyses to demonstrate the high impact of replenishment constraints on profits and solution structures.

In business-to-consumer (B2C) parcel delivery, the presence of the customer at the time of delivery is implicitly required in many cases. If the customer is not at home, the delivery fails—causing additional costs and efforts for the parcel service provider as well as inconvenience for the customer. Parcel service providers typically report high failed-delivery rates, as they have limited possibilities to arrange a delivery time with the recipient. We address the failed-delivery problem in B2C parcel delivery by considering customer-individual availability profiles (APs) that consist of a set of time windows, each associated with a probability that the delivery is successful if conducted in the respective time window. To assess the benefit of APs for delivery tour planning, we formulate the vehicle routing problem with availability profiles (VRPAP) as a mixed integer program, including the trade-off between transportation and failed-delivery costs. We provide analytical insights concerning the model’s cost-savings potential by determining lower and upper bounds. In order to solve larger instances, we develop a novel hybrid adaptive large neighborhood search (HALNS). The HALNS is highly adaptable and also able to solve related time-constrained vehicle routing problems (i.e., vehicle routing problems with hard, multiple, and soft time windows). We show its performance on these related benchmark instances and find a total of 20 new best-known solutions. We additionally conduct various experiments on self-generated VRPAP instances to generate managerial insights. In a case study using real-world data, despite little information on the APs, we were able to reduce failed deliveries by approximately 12% and overall costs by 5%. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1182 .

For patients' treatments, pharmacies and ambulances are equipped with multiple drugs. If there is a medication shortage, the pharmacies are replenished via emergency deliveries, which is a road-based transport. On the other hand, ambulances transport the patient to a nearby hospital as fast as possible. These processes result in high costs, emissions, and delays in patients' treatments. They can be improved by aerial drones transporting the required medication to the pharmacy or ambulance in the field. Drones have great potential here as only a few medications need to be transported, and drones can travel fast, at low variable costs, and emissions caused, but contrary, drones incur high fixed costs. We consider a problem setting where a wholesaler, who is the supplier of multiple pharmacies, plans to integrate drones for emergency delivery of medicines to serve both pharmacies and ambulances at service. We evaluate varying drone network designs for emergency resupply with economic, ecological, and service-oriented key performance indicators. The drone network design includes the decision on the number of drones, batteries, transport boxes, and the location of spare batteries. Based on a case study, we apply a management decision support framework proposed in the literature to define and solve our drone network design problem. Within this framework, a discrete-event simulation model is developed to evaluate various design alternatives. The model accounts for stochastic seasonal demands and weather conditions, as well as daylight-dependent flight bans for drones. We give detailed insights on the relevant stakeholders and the supply chain of the emergency resupply of pharmacies and ambulances and present multiple managerial insights. With unchanged demand, cost savings of up to 19.6% are achieved with a supply rate and service level of at least 91.4%. If, on the other hand, demand increases and drone properties are improved, dependent on the drone network, costs can be reduced by up to 51.7% with a supply rate and service level of more than 99%.

Problem definition: We compare several approaches for generating a prioritized list of items to be counted in a retail store, with the objective of detecting inventory record inaccuracy and unknown out of stocks. Academic/practical relevance: We consider both “rule-based” approaches, which sort items based on heuristic indices, and “model-based” approaches, which maintain probability distributions for the true inventory levels updated based on sales and replenishment observations. Methodology: Our study evaluates these approaches on multiple metrics using data from inventory audits we conducted at European home and personal care retailer dm-drogerie markt. Results: Our results support arguments for both rule-based and model-based approaches. We find that model-based approaches provide versatile visibility into inventory states and are useful for a broad range of objectives but that rule-based approaches are also effective as long as they are matched to the retailer’s goal. We find that “high-activity” rule-based policies, which favor items with high sales volumes, inventory levels, and past errors, are more effective at detecting inventory discrepancies. The best policies uncover over twice the discrepancies detected by random selection. A “low-activity” rule-based policy based on low recorded inventory levels, on the other hand, is more effective at detecting unknown out of stocks. The best policy detects over eight times the unknown out of stocks found by random selection. Managerial implications: Our findings provide immediate guidance to our retail partner on appropriate methods for detecting inventory record inaccuracy and unknown out of stocks. Our approach can be replicated at other retailers interested in customized optimization of their counting programs. Funding: This work was supported by the Bavarian Ministry for Science and Arts [Grant BayIntAn_KUEI_2018_43] and the EHI Foundation and GS1 Germany [Prize for Best Collaboration Between Science and Practice in Retail Research (2019)]. A. J. Mersereau thanks the Sarah Graham Kenan Foundation for support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1119 .