Insertion techniques and constraint propagation for the DARP

International audienceThis paper deals with the Dial and Ride Problem (DARP), while using randomized greedy insertion techniques together with constraint propagation techniques. Though it focuses here on the static version of Dial and Ride, it takes into account the fact that practical DARP has to be handled according to a dynamical point of view, and even, in some case, in real time contexts. So, the kind of algorithmic solution which is proposed here, aim at making easier to bridge both points of view. The model is a classical one, and considers a performance criterion which is a mix between Quality of Service (QoS) and economical cost. We first propose the general framework of the model and discuss the link with dynamical DARP, next describe the algorithm and end with numerical experiments

Constraint Propagation for the Dial-a-Ride Problem with Split Loads

International audienceAbstract. This paper deals with a new problem: the Dial and Ride Problem with Split Loads (DARPSL), while using randomized greedy insertion techniques together with constraint propagation techniques. Though it focuses here on the static versions of Dial and Ride, it takes into account the fact that practical DARP has to be handled according to a dynamical point of view, and even, in some case, in real time contexts. So, the kind of algorithmic solution which is proposed here, aim at making easier to bridge both points of view. First, we propose the general framework of the model and discuss the link with dynamical DARP, second, we describe the two algorithms (DARP and DARPSL), and lastly, show numerical experiments for both

Introduction à la notion d'anticipation et de robustesse dans les problèmes de dial-a-ride dynamiques

Soumis à JESALe dial-a-ride problem, noté DARP, est un problème d'optimisation combinatoire associé aux transports à la demande. Il consiste à construire des tournées de véhicules satisfaisant plusieurs requêtes de transport de personnes, ces requêtes entraînant notamment des contraintes de temps difficiles à résoudre. L'expérience montre que les méthodes d'insertion de demandes permettent de fournir au plus vite un rendez-vous aux usagers. L'objet de la recherche menée ici est de proposer une heuristique à base d'insertions successives intégrant une certaine robustesse, obtenue par anticipation des demandes futures, en gardant les tournées flexibles. Cette flexibilité se perd dès lors qu'un rendez-vous est proposé, ce dernier ne pouvant plus être modifié au risque de désynchroniser le système... et déstabiliser les usagers. Nous proposons donc une méthode pour procéder au calcul de l'insérabilité, à sa prise en compte au moment de la sélection de la demande courante, de la tournée et de sa position au sein de celle-ci, et, enfin, à son utilisation pour aiguiller le calcul des rendez-vous

Anticipation in the Dial-a-Ride Problem: an introduction to the robustness

International audienceThe Dial-a-Ride Problem (DARP) models an operation research problem related to the on demand transport. This paper introduces one of the fundamental features of this type of transport: the robustness. This paper solves the Dial-a-Ride Problem by integrating a measure of insertion capacity called Insertability. The technique used is a greedy insertion algorithm based on time constraint propagation (time windows, maximum ride time and maximum route time). In the present work, we integrate a new way to measure the impact of each insertion on the other not inserted demands. We propose its calculation, study its behavior, discuss the transition to dynamic context and present a way to make the system more robust

Transfers in the on-demand transportation: the DARPT Dial-a-Ride Problem with transfers allowed

International audienceToday, the on-demand transportation is used for elderly and disabled people for short distances. Each user provides a specific demand: a particular ride from an origin to a destination with hard time constraints like time windows, maximum user ride time, maximum route duration limits and precedence. This paper deals with the resolution of these problems (Dial-a-Ride Problems - DARP), including the possibility of one transshipment from a transfer point by request. We propose an algorithm based on insertion techniques and constraints propagation

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

Ambulance routing problems with rich constraints and multiple objectives

Humanitäre non-profit Organisationen im Bereich des Patiententransports sehen sich dazu verpflichtet alle möglichen Einsparungs- und Optimierungspotentiale auszuloten um ihre Ausgaben zu reduzieren. Im Gegensatz zu Notfalleinsatzfahrten, bei denen ein Zusammenlegen mehrerer Transportaufträge normalerweise nicht möglich ist, besteht bei regulären Patiententransporten durchaus Einsparungspotential. Diese Tatsache gibt Anlass zur wissenschaftlichen Analyse jener Problemstellung, welche die täglich notwendige Planung regulärer Patiententransportaufträge umfasst. Solche Aufgabenstellungen werden als Dial-A-Ride-Probleme modelliert. Eine angemessene Service-Qualität kann entweder durch entsprechende Nebenbedingungen gewährleistet oder durch eine zusätzliche Zielfunktion minimiert werden. Beide Herangehensweisen werden hier untersucht. Zuerst wird eine vereinfachte Problemstellung aus der Literatur behandelt und ein kompetitives heuristisches Lösungsverfahren entwickelt. Diese vereinfachte Problemstellung wird in zwei Richtungen erweitert. Einerseits wird, zusätzlich zur Minimierung der Gesamtkosten, eine zweite benutzerorientierte Zielfunktion eingeführt. Andererseits werden eine heterogene Fahrzeugflotte und unterschiedliche Patiententypen in die Standardproblemstellung integriert. Letztendlich wird das reale Patiententransportproblem, basierend auf Informationen des Roten Kreuzes, definiert und gelöst. Neben heterogenen Fahrzeugen und unterschiedlichen Patienten, werden nun auch die Zuordnung von Fahrern und sonstigem Personal zu den verschiedenen Fahrzeugen, Mittagspausen und weitere Aufenthalte am Depot berücksichtigt. Alle eingesetzten exakten Methoden, obwohl sie auf neuesten Erkenntnissen aus der Literatur aufbauen, können Instanzen von realistischer Größe nicht lösen. Dieser Umstand macht die Entwicklung von passenden heuristischen Verfahren nach wie vor unumgänglich. In der vorliegenden Arbeit wird ein relativ generisches System basierend auf der Variable Neighborhood Search Idee entwickelt, das auf alle behandelten Einzielproblemversionen angewandt werden kann; auch für die bi-kriterielle Problemstellung, in Kombination mit Path Relinking, werden gute Ergebnisse erzielt.Humanitarian non-profit ambulance dispatching organizations are committed to look at cost reduction potentials in order to decrease their expenses. While in the context of emergency transportation cost reduction cannot be achieved by means of combined passenger routes, this can be done when dealing with regular patients. This research work is motivated by the problem situation faced by ambulance dispatchers in the field of patient transportation. Problems of this kind are modeled as dial-a-ride problems. In the field of patient transportation, the provision of a certain quality of service is necessary; the term “user inconvenience” is used in this context. User inconvenience can either be considered in terms of additional constraints or in terms of additional objectives. Both approaches are investigated in this book. The aim is to model and solve the real world problem based on available information from the Austrian Red Cross. In a first step, a competitive heuristic solution method for a simplified problem version is developed. This problem version is extended in two ways. On the one hand, besides routing costs, a user-oriented objective, minimizing user inconvenience, in terms of mean user ride time, is introduced. On the other hand, heterogeneous patient types and a heterogeneous vehicle fleet are integrated into the standard dial-a-ride model. In a final step, in addition to heterogeneous patients and vehicles, the assignment of drivers and other staff members to vehicles, the scheduling of lunch breaks, and additional stops at the depot are considered. All exact methods employed, although based on state of the art concepts, are not capable of solving instances of realistic size. This fact makes the development of according heuristic solution methods necessary. In this book a rather generic variable neighborhood search framework is proposed. It is able to accommodate all single objective problem versions and also proves to work well when applied to the bi-objective problem in combination with path relinking

Modélisation et résolution de problèmes difficiles de transport à la demande et de Lot-Sizing

The main objective of the thesis is modeling and optimization of several on-demand transportation services. Supervision techniques must be able to handle numerous criteria and numerous constraints to adapt to the current and future services. Thus, this research develops several types of DARP - Dial-a-Ride Problem -, the operation research problem modeling and optimizing an on-demand transportation system. The standard DARP has been adapted to promising systems, such as those allowing to split the components of the same request and the possibility to dispatch them on different vehicles or the presence of intermodal mechanisms. This thesis also formulates new Operations Research problems in order to integrate autonomous vehicles such as the VIPA in an optimized on-demand transportation system. Modeling and optimizing these systems create schedules of these new vehicles. In the future, technological evolutions are expected and the automatic feature of the vehicles will not be taken into account anymore. These studies attempt to provide a generic framework in order to provide a usable tool for today and an adaptable tool for tomorrow.Le principal objet de cet thèse réside dans la modélisation et l’optimisation de services de transport à la demande aussi différents soient-ils (ou seront-ils). Les techniques de supervision doivent alors pouvoir supporter différents objectifs et différentes contraintes pour s’adapter aux services actuels et futurs. Ainsi, ce rapport de thèse développe différentes variantes du DARP - ang. Dial-a-Ride Problem -, le problème de Recherche Opérationnelle modélisant et optimisant un service classique de transport à la demande. Le DARP standard a été étendu de façon à prendre en compte des hypothèses de fonctionnement prometteuses, comme le fait de séparer les composants d’une même requête pour les dispatcher sur des véhicules différents ou encore la présence de mécanismes d’intermodalité. Cette thèse permet également d’inscrire les véhicules autonomes tels que les VIPA dans de nouvelles problématiques de la Recherche Opérationnelle tout en restant dans le domaine du transport à la demande. La modélisation puis l’optimisation de ces systèmes permet de créer les plannings de ces nouveaux véhicules. A long terme, l’évolution technologique devrait permettre de ne plus se soucier du fait qu’ils sont automatiques. Ces travaux tentent de fournir un cadre suffisamment générique permettant à la fois de fournir une solution exploitable aujourd’hui et qui soit adaptable demain

An Optimization Framework for a Dynamic Multi-Skill Workforce Scheduling and Routing Problem with Time Windows and Synchronization Constraints

This article addresses the dynamic multi-skill workforce scheduling and routing problem with time windows and synchronization constraints (DWSRP-TW-SC) inherent in the on-demand home services sector. In this problem, new service requests (tasks) emerge in real-time, necessitating a constant reevaluation of service team task plans. This reevaluation involves maintaining a portion of the plan unaltered, ensuring team-task compatibility, addressing task priorities, and managing synchronization when task demands exceed a team's capabilities. To address the problem, we introduce a real-time optimization framework triggered upon the arrival of new tasks or the elapse of a set time. This framework redesigns the routes of teams with the goal of minimizing the cumulative weighted throughput time for all tasks. For the route redesign phase of this framework, we develop both a mathematical model and an Adaptive Large Neighborhood Search (ALNS) algorithm. We conduct a comprehensive computational study to assess the performance of our proposed ALNS-based reoptimization framework and to examine the impact of reoptimization strategies, frozen period lengths, and varying degrees of dynamism. Our contributions provide practical insights and solutions for effective dynamic workforce management in on-demand home services

A distributed approach for robust, scalable, and flexible dynamic ridesharing

This dissertation provides a solution to dynamic ridesharing problem, a NP-hard optimization problem, where a fleet of vehicles move on a road network and ridesharing requests arrive continuously. The goal is to optimally assign vehicles to requests with the objective of minimizing total travel distance of vehicles and satisfying constraints such as vehicles’ capacity and time window for pick-up and drop-off locations. The dominant approach for solving dynamic ridesharing problem is centralized approach that is intractable when size of the problem grows, thus not scalable. To address scalability, a novel agent-based representation of the problem, along with a set of algorithms to solve the problem, is proposed. Besides being scalable, the proposed approach is flexible and, compared to centralized approach, more robust, i.e., vehicle agents can handle changes in the network dynamically (e.g., in case of a vehicle breakdown) without need to re-start the operation, and individual vehicle failure will not affect the process of decision-making, respectively. In the decentralized approach the underlying combinatorial optimization is formulated as a distributed optimization problem and is decomposed into multiple subproblems using spectral graph theory. Each subproblem is formulated as DCOP (Distributed Constraint Optimization Problem) based on a factor graph representation, including a group of cooperative agents that work together to take an optimal (or near-optimal) joint action. Then a min-sum algorithm is used on the factor graph to solve the DCOP. A simulator is implemented to empirically evaluate the proposed approach and benchmark it against two alternative approaches, solutions obtained by ILP (Integer Linear Programming) and a greedy heuristic algorithm. The results show that the decentralized approach scales well with different number of vehicle agents, capacity of vehicle agents, and number of requests and outperforms: (a) the greedy heuristic algorithm in terms of solution quality and (b) the ILP in terms of execution time