Evolutionary Optimization of COVID-19 Vaccine Distribution With Evolutionary Demands

Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.

Minimizing total weighted latency in home healthcare routing and scheduling with patient prioritization.

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based integer programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a general variable neighborhood search algorithm. We evaluate the IP model and the metaheuristic on various small-, medium- and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small- and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses.

Developing a solution algorithm for the autonomous electric vehicle routing problem considering the mixed fleet

In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. The arrival and departure times of the AEV at the customer’s location must be pre-planned, because, the AEV is not able to decide what to do if the customer is late at this point. Also, due to increasing the security of the loads inside the AEVs and the lack of control of the driver during the delivery of the goods, each customer should only have access to his/her orders. Therefore, the compartmentation of the AEV’s loading area has been proposed in its conceptual model. We developed a mathematical model based on these properties and proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. Author

Medical Staff Scheduling Problem in Chinese Mobile Cabin Hospitals During Covid-19 Outbreak

In this paper, we discuss the medical staff scheduling problem in the Mobile Cabin Hospital (MCH) during the pandemic outbreaks. We investigate the working contents and patterns of the medical staff in the MCH of Wuhan during the outbreak of Covid-19. Two types of medical staff are considered in the paper, i.e., physicians and nurses. Besides, two different types of physicians are considered, i.e., the expert physician and general physician, and the duties vary among different types of physicians. The objective of the studied problem is to get the minimized number of medical staff required to accomplish all the duties in the MCH during the planning horizon. To solve the studied problem, a general Variable Neighborhood Search (general VNS) is proposed, involving the initialization, the correction strategy, the neighborhood structure, the shaking procedure, the local search procedure, and the move or not procedure. The mutation operation is adopted in the shaking procedure to make sure the diversity of the solution and three neighborhood structure operations are applied in the local search procedure to improve the quality of the solution. © 2021, Springer Nature Switzerland AG.

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak.

During major infectious disease outbreak, such as COVID-19, the goods and parcels supply and distribution for the isolated personnel has become a key issue worthy of attention. In this study, we propose a delivery problem that arises in the last-mile delivery during major infectious disease outbreak. The problem is to construct a Hamiltonian tour over a subset of candidate parking nodes, and each customer is assigned to the nearest parking node on the tour to pick up goods or parcels. The aim is to minimize the total cost, including the routing, allocation, and parking costs. We propose three models to formulate the problem, which are node-based, flow-based and bilevel programing formulations. Moreover, we develop a variable neighborhood search algorithm based on the ideas from the bilevel programing formulations to solve the problem. Finally, the proposed algorithm is tested on a set of randomly generated instances, and the results indicate the effectiveness of the proposed approach.