E‐Commerce activity has been increasing during recent years, and this trend is expected to continue in the near future. e‐Commerce practices are subject to uncertainty conditions and high variability in customers’ demands. Considering these characteristics, we propose two facility–location models that represent alternative distribution policies in e‐commerce (one based on outsourcing and another based on in‐house distribution). These models take into account stochastic demands as well as more than one regular supplier per customer. Two methodologies are then introduced to solve these stochastic versions of the well‐known capacitated facility–location problem. The first is a two‐stage stochastic‐programming approach that uses an exact solver. However, we show that this approach is not appropriate for tackle large‐scale instances due to the computational effort required. Accordingly, we also introduce a “simheuristic” approach that is able to deal with large‐scale instances in short computing times. An extensive set of benchmark instances contribute to illustrate the efficiency of our approach, as well as its potential utility in modern e‐commerce practices.

With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features—scalability, portability, interoperability, and usability—that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.