The rapid rise of electronic commerce has entailed an increase in logistic complexity, with last-mile logistics being the most critical element in deliveries. Since users prefer goods to be delivered at home, one of the biggest challenges faced by e-commerce is to reduce the number of incidents that occur in the delivery of goods to the homes of customers. In many cases, these deliveries cannot take place because recipients are not at the agreed delivery point, leading to a decrease in the quality of service and an increase in distribution costs. Furthermore, sometimes the delivery policies are not in tune with the customers’ expectations. This work presents a new perspective of the last-mile logistics in the context of multichannel retail, asking customers to provide several delivery locations (at home, at work, at a familiar home, in a shop, in a locker, etc.) associated with different time windows. In addition, the customer could state their preferences about these locations. This work formulates the problem and develops different approaches to solve it. A benchmark is proposed to analyze the performance and limitations. The results reveal that a distribution policy with several locations can improve the efficiency of electronic commerce by reducing delivery costs. The findings of this study have several implications for distribution companies.

This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability.

In the current paradigm for the retail industry, which is experiencing a rapid evolution, especially in textile companies, the generic problem of product allocation in a distribution and supply chain consisting of one main warehouse and several locations, belonging to different sales channels, is a challenge. The omnichannel replenishment process focuses on dynamically optimizing a shop or intermediate warehouse inventory for a wide range of products based on a forecast of sales, in order to fulfill the demand of all of the channels considered. In this context, the aims of this work were (a) to optimize inventory replenishment for multiple channels and products that are not perishable but devalue over time, and (b) to implement a methodology that combines the benefits of the Particle Swarm Optimization metaheuristic and Simulated Annealing. This study was carried out for different sales periods, channels and product configurations by performing a sensitivity analysis between the way new solutions are updated and the degree of intensification used in local search.