Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Solution methods for the dynamic stochastic dial-a-ride problem with time-dependent travel speeds

Die wissenschaftliche Forschung in Bezug auf humanitäre Hilfe sowie Gesundheitsfürsorge ist sehr weitläufig. Im Laufe der letzten Jahrzehnte stiegen die Anstrengungen welche in einschlägige Forschungsprojekte investiert wurden stetig an. In diesem Buch betrachten wir einen konkreten Problemfall aus diesem großen Forschungsbereich: den Transport von alten, kranken oder in ihrer Mobilität eingeschränkten Personen. Probleme dieser Art werden in der Fachliteratur regelmäßig als dial-a-ride Probleme bezeichnet. Wir studieren drei dynamische und stochastische Varianten dieser Problemklasse mit dem Ziel, Effekte, welche durch das Ausnutzen von stochastischen Informationen über zukünftige Umstände während der Planung verursacht werden, zu untersuchen. Zunächst betrachten wir stochastische Informationen über zukünftige Rücktransporte und adaptieren zwei Paare von metaheuristischen Lösungsverfahren für diese Problemstellung. Anschließend untersuchen wir die Ausnutzung stochastischer Informationen über zukünftige Verkehrsbedingungen während der Planung der Fahrzeugrouten. Schlussendlich kombinieren wir diese beiden stochastischen Einflüsse mit zusätzlichen Aspekten bezüglich heterogener Fahrzeugflotten, heterogener Patienten sowie mehrerer Heimatstandorte. Basierend auf unseren Ergebnissen identifizieren wir Faktoren, welche starken Einfluss auf das mögliche Verbesserungspotential haben, das durch Ausnutzung der stochastischen Informationen erreichbar ist. Grundsätzlich sind die Vorteile, die durch eine stochastische Planung erreicht werden können bemerkenswert, sofern die entsprechenden Rahmenbedingungen vorliegen. Insbesondere das in einer Problemstellung vorhandene Ausmaß an Dynamik erweist sich als mit der erreichten Lösungsqualität stark negativ korrelierend.The field of research regarding the optimization of humanitarian aid as well as health care efforts is very wide. During the last decades, the efforts expended in research projects related to this area are steadily increasing. In this book, we address a specific problem out of this large field: the transportation of elderly, ill and disabled persons. Such problems are commonly referred to as dial-a-ride problem (DARP) in the literature. We study three dynamic and stochastic variants of this problem type with the aim of examining the effects of exploiting stochastic information about different future circumstances while planning. First, we consider stochastic information about future return transports and tailor two pairs of metaheuristic solution methods to the requirements of this problem. Second, we study the usage of stochastic information about future travel speeds while constructing the vehicle routes. Finally, we combine these two stochastic aspects with additional heterogeneous extensions regarding the vehicle fleet, the transported patients and multiple depots. Based on our findings we identify factors which have a strong influence on the potential benefits of exploiting stochastic information about future circumstances. Generally speaking, the benefits obtainable by planning in a stochastic way can be remarkable if the underlying conditions are suitable. Especially the total degree of dynamism present in a problem setting turns out to be negatively correlated with the achieved solution quality

The dial-a-ride problem with electric vehicles and battery swapping stations

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for customers with special needs and/or disabilities. The DARP with Electric Vehicles and battery swapping stations (DARP-EV) concerns scheduling a fleet of EVs to serve a set of pre-specified transport requests during a certain planning horizon. In addition, EVs can be recharged by swapping their batteries with charged ones from any battery-swap stations. We propose three enhanced Evolutionary Variable Neighborhood Search (EVO-VNS) algorithms to solve the DARP-EV. Extensive computational experiments highlight the relevance of the problem and confirm the efficiency of the proposed EVO-VNS algorithms in producing high quality solutions

EXPLOITING AVAILABLE URBAN TRANSPORTATION RESOURCES WITH TAXI SHARING AND RAPID TRANSPORTATION NETWORKS: A CASE STUDY FOR MILAN

We assess a bimodal transportation system based on a massive urban on-demand transportation service, named Taxi Sharing, and a rapid Local Public Transportation optimized for users without movement impairments, according to users' traveling and walking time. The aim is to increase, qualitatively and quantitatively, public mobility services by exploiting available urban transportation resources, in order to reduce private motorized mobility and related externalities in urban context. We developed a new technique to optimize a high quality Taxi Sharing service starting from state-of-the-art DARP optimization algorithms. In Taxi Sharing, time windows on pick-up and delivery times are narrow and the service is provided by many small vehicles, taxis. These features allow an enumeration of all possible subsets of incoming users' requests for each vehicle and to compute in real time an optimal set of routes by solving a large set partitioning problem with state-of-the-art integer linear programming solvers. Owing to this fast global optimization capability, the system allows for a high quality service without any need of booking the ride in advance. We present three development scenarios according to demand level, we discuss the performance of the service in terms of number of requests serviced per hour, average travel time and waiting time, number of taxis simultaneously on duty, ride fare and taxi revenue. We explored the possibility of planning, in presence of Taxi Sharing, a rapid LPT optimized for users without movement impairments according to users' traveling and walking time. We based the optimization process on data collected in the field. We evaluated the effects of optimal stops spacing on commercial speed, in relation also to traffic light priority. Obtained results indicate a huge potential increase in efficiency related both to taxi service and to local public transportation

Modelling blue-light ambulance mobility in the London metropolitan area

Actions taken immediately following a life-threatening incident are critical for the survival of the patient. In particular, the timely arrival of ambulance crew often makes the difference between life and death. As a consequence, ambulance services are under persistent pressure to achieve rapid emergency response. Meeting stringent performance requirements poses special challenges in metropolitan areas where the higher population density results in high rates of life-threatening incident occurrence, compounded by lower response speeds due to traffic congestion. A key ingredient of data-driven approaches to address these challenges is the effective modelling of ambulance movement thus enabling the accurate prediction of the expected arrival time of a crew at the site of an incident. Ambulance mobility patterns however are distinct and in particular differ from civilian traffic: crews travelling with ashing blue lights and sirens are by law exempt from certain traffic regulations; and moreover, ambulance journeys are triggered by emergency incidents that are generated following distinct spatial and temporal patterns. We use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on a road routing network modified to reflect the differences between emergency and regular vehicle traffic, we develop a methodology for matching ambulances Global Positioning System (GPS) coordinates to road segments, allowing the reconstruction of ambulance routes with precise speed data. We demonstrate how a road speed model that exploits this information achieves best predictive performance by implicitly capturing route-specific patterns in changing traffic conditions. We then present a hybrid model that achieves a high route similarity score while minimising journey duration error. This hybrid model outperforms alternative mobility models. To the best of our knowledge, this study represents the first attempt to apply data-driven methodologies to route selection and estimation of arrival times of ambulances travelling with blue lights and sirens