Large-scale Ridesharing DARP Instances Based on Real Travel Demand

Accurately predicting the real-life performance of algorithms solving the Dial-a-Ride Problem (DARP) in the context of Mobility on Demand (MoD) systems with ridesharing requires evaluating them on representative instances. However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, artificial instances or outdated non-public instances, hindering direct comparisons. With the rise of large MoD systems and the availability of open travel demand datasets for many US cities, there is now an opportunity to evaluate these algorithms on standardized, realistic, and representative instances. Despite the significant challenges involved in processing obfuscated and diverse datasets, we have developed a methodology using which we have created a comprehensive set of large-scale demand instances based on real-world data. These instances cover diverse use cases, one of which is demonstrated in an evaluation of two established DARP methods: the insertion heuristic and optimal vehicle-group assignment method. We publish the full results of both methods in a standardized format. The results show significant differences between areas in all measured quantities, emphasizing the importance of evaluating methods across different cities.Comment: 8 pages, 9 figures. Submitted to 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023. For the published associated dataset and source codes, see the repository https://github.com/aicenter/Ridesharing_DARP_instance

An integer L-shaped algorithm for the Dial-a-Ride Problem with stochastic customer delays

AbstractThis paper considers a single-vehicle Dial-a-Ride Problem in which customers may experience stochastic delays at their pickup locations. If a customer is absent when the vehicle serves the pickup location, the request is fulfilled by an alternative service (e.g., a taxi) whose cost is added to the total cost of the tour. In this case, the vehicle skips the corresponding delivery location, which yields a reduction in the total tour cost. The aim of the problem is to determine an a priori Hamiltonian tour minimizing the expected cost of the solution. This problem is solved by means of an integer L-shaped algorithm. Computational experiments show that the algorithm yields optimal solutions on several instances within reasonable CPU times. It is also shown that the actual cost of an optimal solution obtained with this algorithm can be significantly smaller than that of an optimal solution obtained with a deterministic formulation

Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care

International audienceThis paper addresses a vehicle scheduling problem encountered in home health care logistics. It concerns the delivery of drugs and medical devices from the home care company's pharmacy to patients' homes, delivery of special drugs from a hospital to patients, pickup of bio samples and unused drugs and medical devices from patients. The problem can be considered as a special vehicle routing problem with simultaneous delivery and pickup and time windows, with four types of demands: delivery from depot to patient, delivery from a hospital to patient, pickup from a patient to depot and pickup from a patient to a medical lab. Each patient is visited by one vehicle and each vehicle visits each node at most once. Patients are associated with time windows and vehicles with capacity. Two mixed-integer programming models are proposed. We then propose a Genetic Algorithm (GA) and a Tabu Search (TS) method. The GA is based on a permutation chromosome, a split procedure and local search. The TS is based on route assignment attributes of patients, an augmented cost function, route re-optimization, and attribute-based aspiration levels. These approaches are tested on test instances derived from existing VRPTW benchmarks

Introducing heterogeneous users and vehicles into models and algorithms for the dial-a-ride problem

AbstractDial-a-ride problems deal with the transportation of people between pickup and delivery locations. Given the fact that people are subject to transportation, constraints related to quality of service are usually present, such as time windows and maximum user ride time limits. In many real world applications, different types of users exist. In the field of patient and disabled people transportation, up to four different transportation modes can be distinguished. In this article we consider staff seats, patient seats, stretchers and wheelchair places. Furthermore, most companies involved in the transportation of the disabled or ill dispose of different types of vehicles. We introduce both aspects into state-of-the-art formulations and branch-and-cut algorithms for the standard dial-a-ride problem. Also a recent metaheuristic method is adapted to this new problem. In addition, a further service quality related issue is analyzed: vehicle waiting time with passengers aboard. Instances with up to 40 requests are solved to optimality. High quality solutions are obtained with the heuristic method

An Integer L-Shaped Algorithm for the Dial-a-Ride Problem with Stochastic Customer Delays

Abstract This paper considers a single-vehicle Dial-a-Ride problem in which customers may experience stochastic delays at their pickup locations. If a customer is absent when the vehicle serves the pickup location, the request is fulfilled by an alternative service (e.g., a taxi) whose cost is added to the total cost of the tour. In this case, the vehicle skips the corresponding delivery location, which yields a reduction in the total tour cost. The aim of the problem is to determine an a priori Hamiltonian tour minimizing the expected cost of the solution. This problem is solved by means of an integer L-shaped algorithm. Computational experiments show that the algorithm yields optimal solutions for small and medium size instances within reasonable CPU times. It is also shown that the actual cost of an optimal solution obtained with this algorithm can be significantly smaller than that of an optimal solution obtained with a deterministic formulation

A Heuristic Approach for a Transportation Problem with Capacity and Time Windows Constraints (Un Enfoque Heurístico para un Problema de Transportación con Restricciones de Capacidad y Ventanas de Horario)

Abstract. We consider a Pickup and Delivery Vehicle Routing Problem (PDP) commonly encountered in real-world logistics operations. The problem involves a set of practical complications that have received little attention in the vehicle routing literature. In this problem, there are multiple vehicle types available to cover a set of pickup and delivery requests, each of which has pickup time windows and delivery time windows. Transportation orders and vehicle types must satisfy a set of compatibility constraints that specify which orders cannot be covered by which vehicle types. In addition we include some dock service capacity constraints as is required on common real world operations. This problem requires to be attended on large scale instances (orders ≥ 500), (vehicles ≥ 150). As a generalization of the traveling salesman problem, clearly this problem is NP-hard. The exact algorithms are too slow for large scale instances. The PDP-TWDS is both a packing problem (assign order to vehicles), and a routing problem (find the best route for each vehicle). We propose to solve the problem in three stages. The first stage constructs initials solutions at aggregate level relaxing some constraints on the original problem. The other two stages imposes time windows and dock service constraints. Our results are favorable finding good quality solutions in relatively short computational times. Keywords. Vehicle Routing, Logistics & Distribution Planning, Scheduling, Time Windows. Resumen. Consideramos un problema de Ruteo para Entrega y Recolección (PDP) el cual es común encontrar en las operaciones Logísticas en el mundo real. El problema involucra un conjunto de consideraciones de tipo práctico que han recibido poca atención en la literatura científica de los problemas de ruteo. En nuestro problema, se presentan múltiples tipos de vehículos los cuales están disponibles para cubrir un conjunto de requerimientos de entrega y de recolección. Cada uno de estos requerimientos debe ser atendido dentro de cierta ventana de horario. Los requerimientos de transporte y los tipos de vehículos deben satisfacer las restricciones de compatibilidad las cuales especifican algunas órdenes que no pueden ser cubiertas por cierto tipo de vehículos. Además se incluyen algunas restricciones relacionadas con la capacidad de andenes disponibles para dar servicio y atención lo cual es común encontrar en las operaciones del mundo real. Nuestro problema requiere ser atendido para instancias de gran tamaño (ordenes ≥ 500) y (vehículos ≥ 150). Este problema considerado tal como una generalización del problema del agente viajero (TSP), debe ser visto claramente como un problema difícil de resolver matemáticamente hablando (NP-Hard). Los algoritmos de solución exacta son demasiado lentos en tiempo como para poder ser utilizados en la solución de instancias de gran tamaño. Nuestro problema PDP-TWDS es al mismo tiempo tanto un problema de empacamiento (para asignar las ordenes de transporte a los vehículos) así como un Daena: International Journal of Good Conscience. 5(2) 45-68. Octubre 2010. ISSN 1870 problema de ruteo (encontrar la mejor ruta para cada vehículo). Nuestra propuesta consiste en resolver el problema en 3 etapas. La primera etapa construye soluciones iniciales a un nivel agregado para lo cual se relajan algunas restricciones del problema original. Las otras dos etapas se encargan de incluir las restricciones de ventana de horario así como de capacidad de andenes para el servicio. Nuestros resultados son muy favorables al encontrar soluciones de alta calidad en tiempos de solución relativamente cortos. Palabras claves. Ruteo de Vehiculos, Logística, Planeación de la Distribución, Programación de Transporte, Ventanas de Horari