Robot-Based Last-Mile Deliveries With Pedestrian Zones

Since delivery robots share sidewalks with pedestrians, it may be beneficial to choose paths for them that avoid zones with high pedestrian density. In this paper, we investigate a robot-based last-mile delivery problem considering path flexibility given the presence of zones with varying pedestrian level of service (LOS). Pedestrian LOS is a measure of pedestrian flow density. We model this new problem with stochastic travel times and soft customer time windows. The model includes an objective that reflects customer service quality based on early and late arrivals. The heuristic solution approach uses the minimum travel time paths from different LOS zones (path flexibility). We demonstrate that the presence of pedestrian zones leads to alternative path choices in 30% of all cases. In addition, we find that extended time windows may help increase service quality in zones with high pedestrian density by up to 40%.

A Bi-level Decomposition Algorithm for Multi-factory Scheduling With Batch Delivery Problem

This paper introduces a multi-factory scheduling with batch delivery problem. A novel mixed-integer programming model is proposed to minimize the sum of total tardiness, holding and batching costs. A bi-level decomposition algorithm (BLDA) is developed involving two sub-problems: scheduling problem in the upper level and batching problem in the lower level. Four versions of the BLDA are created by combinations of CPLEX and simulated annealing in both levels, which interactively collaborate until the algorithm converges to a solution. The BLDAs are examined on several random and real-life test instances. A statistical analysis is performed by comparing the BLDAs’ solutions with the exact minimum and lower bound values of the total cost. The results indicate that about all versions of the developed BLDA provide high quality solutions for real-world zinc industry problems as well as generated instances in a reasonably short time. Finally, some managerial insights are derived based on sensitivity analysis.

A genetic algorithm for vehicle routing in logistic networks with practical constraints

We optimise a postal delivery problem with time and capacity constraints imposed on vehicles and nodes of the logistic network. Time constraints relate to the duration of routes, whereas capacity constraints concern technical characteristics of vehicles and postal operation outlets. We consider a method which can be applied to a brownfield scenario, in which capacities of outlets can be relaxed and prospective hubs identified. As a solution, we apply a genetic algorithm and test its properties both in small case studies and in a simulated problem instance of a larger (i.e. comparable with real-world instances) size. We show that the genetic operators we employ are capable of switching between solutions based on direct origin-to-destination routes and solutions based on transfer connections, depending on what is more beneficial in a given problem instance. Moreover, the algorithm correctly identifies cases in which volumes should be shipped directly, and those in which it is optimal to use transfer connections within a single problem instance, if an instance in question requires such a selection for optimality. The algorithm is thus suitable for determining hubs and satellite locations. All considerations presented in this paper are motivated by real-life problem instances experienced by the Polish Post, the largest postal service provider in Poland, in its daily plans of delivering postal packages, letters and pallets.

The Critical Contribution of Pseudouridine to mRNA COVID-19 Vaccines

The current COVID-19 pandemic is a massive source of global disruption, having led so far to two hundred and fifty million COVID-19 cases and almost five million deaths worldwide. It was recognized in the beginning that only an effective vaccine could lead to a way out of the pandemic, and therefore the race for the COVID-19 vaccine started immediately, boosted by the availability of the viral sequence data. Two novel vaccine platforms, based on mRNA technology, were developed in 2020 by Pfizer-BioNTech and Moderna Therapeutics (comirnaty® and spikevax®, respectively), and were the first ones presenting efficacies higher than 90%. Both consisted of N1-methyl-pseudouridine-modified mRNA encoding the SARS-COVID-19 Spike protein and were delivered with a lipid nanoparticle (LNP) formulation. Because the delivery problem of ribonucleic acids had been known for decades, the success of LNPs was quickly hailed by many as the unsung hero of COVID-19 mRNA vaccines. However, the clinical trial efficacy results of the Curevac mRNA vaccine (CVnCoV) suggested that the delivery system was not the only key to the success. CVnCoV consisted of an unmodified mRNA (encoding the same spike protein as Moderna and Pfizer-BioNTech’s mRNA vaccines) and was formulated with the same LNP as Pfizer-BioNTech’s vaccine (Acuitas ALC-0315). However, its efficacy was only 48%. This striking difference in efficacy could be attributed to the presence of a critical RNA modification (N1-methyl-pseudouridine) in the Pfizer-BioNTech and Moderna’s mRNA vaccines (but not in CVnCoV). Here we highlight the features of N1-methyl-pseudouridine and its contributions to mRNA vaccines.