Fábio Silva Moreira

Abstract
Omnichannel retailers are reinventing stores to meet the growing demand of the online channel. Several retailers now use stores as supporting distribution centers to offer quicker Buy-Online-Pickup-In-Store (BOPS) and Ship-From-Store (SFS) services. They resort to in-store picking to serve online orders using existing assets. However, in-store picking operations require picker carts traveling through store aisles, competing for store space, and possibly harming the offline customer experience. To learn picking policies that acknowledge interactions between pickers and offline customers, we formalize a new problem called Dynamic In-store Picker Routing Problem (diPRP). This problem considers a picker that tries to pick online orders (seeking) while minimizing customer encounters (hiding) – preserving the offline customer experience. We model the problem as a Markov Decision Process (MDP) and solve it using a hybrid solution approach comprising mathematical programming and reinforcement learning components. Computational experiments on synthetic instances suggest that the algorithm converges to efficient policies. We apply our solution approach in the context of a large European retailer to assess the proposed policies regarding the number of orders picked and customers encountered. The learned policies are also tested in six different retail settings, demonstrating the flexibility of the proposed approach. Our work suggests that retailers should be able to scale the in-store picking of online orders without jeopardizing the experience of offline customers. The policies learned using the proposed solution approach reduced the number of customer encounters by up to 50%, compared to policies solely focused on picking orders. Thus, to pursue omnichannel strategies that adequately trade-off operational efficiency and customer experience, retailers cannot rely on actual simplistic picking strategies, such as choosing the shortest possible route. © 2023 Elsevier B.V.

Abstract

Abstract
In this paper, we explore the value of considering simultaneous pickups and deliveries inmulti-product inventory-routing problems both with deterministic and uncertain demand. Wepropose a multi-commodity, develop an exact branch-and-cut algorithm with patching heuristicsto efficiently tackle this problem, and provide insightful analyses based on optimal plans. Thesimplicity of the proposed approach is an important aspect, as it facilitates its usage in practice,opposed to complicated stochastic or probabilistic methods. The computational experimentssuggest that in the deterministic demand setting, pickups are mainly used to balance initialinventories, achieving an average total cost reduction of 1.1%, while transshipping 2.4% oftotal demand. Under uncertain demand, pickups are used extensively, achieving cost savings of up to 6.5% in specific settings. Overall, our sensitivity analysis shows that high inventory costsand high degrees of demand uncertainty drive the usage of pickups, which, counter-intuitively, are not desirable in every case

Abstract
Service operation vessels are becoming the dominant mode for the maintenance of most offshore wind farms. To minimize turbine downtime, it is essential to bring the right components to the wind farm, while budget and volume constraints prohibit having excess inventories on board. This setting can be interpreted as a repair kit problem, which seeks to define a set of components that may be necessary for on-site maintenance operations in a given time period during which emergency resupply is costly. Current repair kit problem approaches however, do not cater sufficiently for some of the characteristics of offshore wind farm maintenance, including weather-dependent deterioration and the possibility to perform emergency resupplies. We propose mixed-integer programming models both to determine (tactical model) and validate (operational model) repair kits when maintenance operations are performed under different weather conditions. The models are flexible enough to be used with real world data considering multiple turbines composed of different deteriorating components, service operation vessels characteristics (speed and volumetric capacity), different weather conditions, and emergency resupplies. An important feature of this approach is its ability to consider detailed maintenance and vessel routing operations to test and validate repair kits in realistic wind farm environments. We provide valuable insights on the composition of repair kits and on relevant business indicators for a set of different scenarios. The practical implications are that repair kits should be adapted depending on weather forecasts and that considerable downtime reductions can be achieved by allowing emergency resupplies. © 2021 The Author(s)

Abstract
Most vehicle routing approaches disregard the need to refuel fleets. However, planners search for opportunities to refuel at lower prices even if, counter-intuitively, distant fuel stations need to be visited. We propose a novel mathematical formulation and develop branch-and-cut and matheuristic algorithms to efficiently tackle this problem. Results indicate that, to minimize costs, detour distances may increase up to 6 percentage points when fuel stations with lower prices are farther away from the depot. For practice, these insights imply that current policies disregarding station location and/or fuel prices along with "myopic" planning horizons may lead to sub-optimal decisions.