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

Integrating a connected micromobility infrastructure to the existing public transport

This paper presents the integration of connected micromobility infrastructure into the existing public transport system. The integration purpose is to help organize the public space in the urban environment, lower operation costs for micromobility operators, and create a better Mobility-as-a-Service (MaaS) experience for citizens with the connected and universal micromobility charging infrastructure solution. Our goal is to efficiently consolidate electric-powered shared micromobility vehicles such as e-scooters and e-bikes into hubs to manage their charging and maintenance operations efficiently. Therefore, determining the locations of these e-hubs and the required charging infrastructure is paramount for satisfying the commuters' needs. We address this problem using an optimization approach and develop a model for siting and sizing micromobility e-hubs within an urban context. We formulate the problem as a mixed-integer linear programming (MILP) and develop a Variable Neighbourhood Search (VNS) metaheuristic algorithm to solve the problem. The evaluation of the performance of the solution methodology is applied using real data from Ankara Metropolitan Municipality (AMM)

An improved matheuristic for solving the electric vehicle routing problem with time windows and synchronized mobile charging/battery swapping

The shift towards low-emission vehicles in transportation activities, electric vehicles (EVs) in particular, has accelerated lately due to the growing concerns in modern societies regarding greenhouse gas emissions and climate change. Delivery companies have started using EVs in their fleets to reduce their dependency on fossil fuels and improve their carbon footprints. However, range anxiety, long recharge durations and insufficient recharging infrastructure still restrain the wider adoption of EVs in the sector. As a remedy, battery swapping vans (BSVs) were proposed in the literature to supply energy to EVs at points of need and the arising problem was referred to as the Electric Vehicle Routing Problem with Time Windows and Synchronized Mobile Battery Swapping (EVRPTW-SMBS). However, the use of BSVs is limited to small commercial vehicles. In this study, we generalize the problem and present the Electric Vehicle Routing Problem with Time Windows and Mobile Charging Stations (EVRPTW-MCS). In this problem, EVs serve the customers within their time windows and electric trucks/vans are employed to recharge or swap their batteries at selected customer locations during their visits. The objective is to minimize the total operational cost with the minimum fleet size. First, we present the mathematical model of the EVRPTW-MCS. Next, we propose a matheuristic approach that combines the Variable Neighborhood Search with exact method to solve it. Then, we perform an extensive numerical study to validate the performance of the proposed approach and present new best solutions for two related problems in the literature. We also investigate the potential benefits of utilizing MCSs and provide several trade-off analyses. Finally, we provide a case study based on real data to present managerial insights

Last mile delivery routing problem using autonomous electric vehicles

This paper presents a study on the application of Autonomous Delivery Vehicles (ADVs) in last-mile delivery for urban logistics. Specifically, we focus on a routing problem using multi-stop ADVs, with the goal of minimizing route and vehicle usage costs while satisfying several constraints associated with load and battery capacities, maximum route duration for ADVs, and maximum walking distance for customers. We refer to this problem as the Autonomous Delivery Vehicle Routing Problem (ADVRP) and present its mixed-integer linear programming formulation. Due to the NP-hardness of the problem, we propose a two-phase metaheuristic approach that first clusters customers and determines stopping locations for the ADVs, followed by a phase that determines the optimal routes for the ADVs using hybrid variable neighborhood search and simulated annealing. To evaluate our proposed solution methodology, we conduct computational experiments on various related Vehicle Routing Problems (VRPs) from the literature and newly generated ADVRP instances. The results show that the proposed two-phase metaheuristic approach can produce high-quality solutions with minimal computational effort while outperforming an exact solver in 26 medium- and large-sized instances of ADVRP and reaching optimal solutions in most VRP instances and related problems. Furthermore, we conduct sensitivity analyses on selected problem parameters and present a case study in Istanbul, Turkey to provide managerial insights for implementing ADVs in urban logistics