A Decomposition Algorithm to Solve the Multi-Hop Peer-to-Peer Ride-Matching Problem

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel by transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem, and devise a decomposition algorithm to solve the original ride-matching problem to optimality by means of solving multiple smaller problems. We conduct extensive numerical experiments to demonstrate the computational efficiency of the proposed algorithm and show its practical applicability to reasonably-sized dynamic ride-matching contexts. Finally, in the interest of even lower solution times, we propose heuristic solution methods, and investigate the trade-offs between solution time and accuracy

A Tabu Search Based Metaheuristic for Dynamic Carpooling Optimization

International audienceThe carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France

Modeling and Evaluation of a Ridesharing Matching System from Multi-Stakeholders\u27 Perspective

With increasing travel demand and mobility service quality expectations, demand responsive innovative services continue to emerge. Ridesharing is an established, yet evolving, mobility option that can provide more customized, reliable shared service without any new investment in the transportation infrastructure. To maximize the benefits of ridesharing service, efficient matching and distribution of riders among available drivers can provide a reliable mobility option under most operating conditions. Service efficiency of ridesharing depends on the system performance (e.g., trip travel time, trip delay, trip distance, detour distance, and trip satisfaction) acceptable to diverse mobility stakeholders (e.g., riders, drivers, ridesharing operators, and transportation agencies). This research modeled the performance of a ridesharing service system considering four objectives: (i) minimization of system-wide passengers’ waiting time, (ii) minimization of system-wide vehicle miles travelled (VMT), (iii) minimization of system-wide detour distance, and (iv) maximization of system-wide drivers’ profit. Tradeoff evaluation of objectives revealed that system-wide VMT minimization objective performed best with least sacrifices on the other three objectives from their respective best performance level based on set of routes generated in this study. On the other hand, system-wide drivers’ profit maximization objective provided highest monetary incentives for drivers and riders in terms of maximizing profit and saving travel cost respectively. System-wide minimization of detour distance was found to be least flexible in providing shared rides. The findings of this research provide useful insights on ridesharing system modeling and performance evaluation, and can be used in developing and implementing ridesharing service considering multiple stakeholders’ concerns

The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

This work introduces a new routing problem called the Dynamic Multi-Objective Multi-vehicle Covering Tour Problem (DMOMCTP). The DMOMCTPs is a combinatorial optimization problem that represents the problem of routing multiple vehicles to survey an area in which unpredictable target nodes may appear during execution. The formulation includes multiple objectives that include minimizing the cost of the combined tour cost, minimizing the longest tour cost, minimizing the distance to nodes to be covered and maximizing the distance to hazardous nodes. This study adapts several existing algorithms to the problem with several operator and solution encoding variations. The efficacy of this set of solvers is measured against six problem instances created from existing Traveling Salesman Problem instances which represent several real countries. The results indicate that repair operators, variable length solution encodings and variable-length operators obtain a better approximation of the true Pareto front

Enhancing Urban Mobility: Integrating Ride-sharing and Public Transit

Seamless integration of ride-sharing and public transit may offer fast, reliable, and affordable transfer to and from transit stations in suburban areas thereby enhancing mobility of residents. We investigate the potential benefits of such a system, as well as the ride-matching technology required to support it, by means of an extensive computational study

The Benefits of Meeting Points in Ride-sharing Systems

We investigate the potential benefits of introducing meeting points in a ride-sharing system. With meeting points, riders can be picked up and dropped off either at their origin and destination or at a meeting point that is within a certain distance from their origin or destination. The increased flexibility results in additional feasible matches between drivers and riders, and allows a driver to be matched with multiple riders without increasing the number of stops the driver needs to make. We design and implement an algorithm that optimally matches drivers and riders in large-scale ride- sharing systems with meeting points. We perform an extensive simulation study to assess the benefits of meeting points. The results demonstrate that meeting points can significantly increase the number of matched participants as well as the system-wide driving distance savings in a ride-sharing system

Algorithme de jumelage multimodal pour le covoiturage

RÉSUMÉ : Le covoiturage multimodal urbain est une solution économique pour réduire les émissions de gaz à effet de serre dans les villes. Le but du projet présenté dans ce mémoire est de modéliser et d’implémenter un algorithme de jumelage multimodal permettant de mettre en relation conducteurs et passagers pour effectuer des trajets quotidiens en zone urbaine. Cet algorithme a pour vocation d’être rapide et d’offrir des jumelages de qualité, en termes de détour acceptable et de respect des horaires. Il a également pour ambition de coupler le covoiturage avec les transports en commun. Ce projet est en partenariat avec Netlift, startup Montréalaise, ainsi qu’avec un autre étudiant en maîtrise recherche au département de Génie Civil de l’École Polytechnique de Montréal, qui travaille principalement sur les données utilisées. Les objectifs de ce mémoire sont multiples. Le premier consiste à construire une structure de données permettant de modéliser la ville de Montréal et de calculer des temps de parcours. Ceci permettrait de comparer les différents trajets des utilisateurs. Aussi, cette structure de données doit permettre le calcul d’itinéraires multimodaux, auto et transports en commun combinés. Le second objectif est de modéliser et d’implémenter en JAVA un algorithme de jumelage passagers/conducteurs pour le covoiturage dit « classique » (auto uniquement) et pour le covoiturage multimodal. Une revue de littérature a permis de diriger les travaux à mener. Ce travail est présenté dans le premier chapitre. Après une brève synthèse des concepts relatifs au covoiturage, une classification des systèmes et algorithmes existants permet d’amener différentes conclusions quant à la structure de données à implémenter, sur laquelle s’appuie l’algorithme envisagé. Elle doit permettre d’accéder rapidement aux données nécessaires à l’obtention de jumelages pour un passager donné. La structure du reste du mémoire est influencée par la chronologie du projet : la définition du besoin et les objectifs à atteindre ont été définis au fur et à mesure avec Netlift et les différents collaborateurs. Le second chapitre du corps du mémoire concerne les premières avancées menées en parallèle de la définition du besoin, tandis que le troisième chapitre décrit l’algorithme et la structure de données retenus pour satisfaire les objectifs fixés. Le quatrième et dernier chapitre présente les conclusions et les perspectives de recherche. Dans le second chapitre, on essaye d’établir des indicateurs de potentiel de covoiturage au moyen d’un score et de différentes régressions linéaires. Ce sont ces recherches préalables qui ont conduit à l’élaboration d’une structure de données plus complexe, présenté dans le troisième chapitre, qui fait appel aux concepts de la théorie des graphes. L’algorithme développé dans cette partie fait notamment appel à des calculs de plus courts chemins. Il permet de trier une liste de conducteurs pour un passager donné en fonction de leur potentiel de covoiturage – notion qui sera expliquée en détail. Son évaluation est réalisée à l’aide de différentes métriques relatives aux données fournies par Netlift (jumelages trouvés par leur algorithme) et aux données de l’enquête Origine-Destination de Montréal pour l’année 2008. Les résultats sont satisfaisants pour le covoiturage classique (sans transports en commun) puisque l’algorithme implémenté réussit à fournir rapidement des covoitureurs de bonne qualité pour une grande partie des utilisateurs. Parmi les passagers des données de l’enquête Origine-Destination, plus d’un passager sur deux possède un conducteur qui peut covoiturer avec lui pour des détours de 10min maximum. Le potentiel du covoiturage multimodal pour la période de pointe du matin est évalué grâce à une étude des trajets de l’enquête OD de 2008. Les jumelages obtenus sont moins bons que pour le covoiturage classique, mais la méthode employée présente une marge d’amélioration et une perspective de recherche future. Ce projet permet à Netlift de gagner en pertinence et en rapidité par rapport aux jumelages proposés dans leur application actuelle.----------ABSTRACT : Urban multimodal ridesharing is an economical way to reduce greenhouse gases emissions in cities. The goal of the project presented in this thesis is to modelise and implement a multimodal matching algorithm able to match drivers and passengers for everyday short ridesharing. This algorithm aims to be fast and to offer precise matches regarding acceptable detour and schedule respect. It also tries to mix ridesharing with public transportation. This project is led in partnership with Netlift, a Montreal startup, an another master’s student linked to the Civil Engineering department of Polytechnique Montreal, working especially on data. Multiple objectives are targeted in this thesis. The first one consists of making a data structure representing Montreal and enabling travelling time calculation. This could lead to compare user’s paths. Multimodal paths need also to be calculated thanks to this data structure. The second objective is to modelise and implement in JAVA a matching algorithm between riders and drivers for « classic » ridesharing (only car) and multimodal ridesharing (car and public transportation). In the first part of this thesis, a litterature review has been conducted in order to guide the goals to achieve. After a short synthesis of ridesharing concepts, a classification of existing articles about ridesharing leads to conclusions related to the data structure to implement. A need of speed is necessary to propose matched drivers to a given passenger. The structure of this thesis is affected by the chronology of the project : the requirements definition and goals to achieve have been precised all along with Netlift and the other partners. The second chapter of the thesis deals with the initial steps conducted at the same time than the requirements definition. The third chapter describes then the data structure and algorithm selected to achieve goals. In the second chapter, ridesharing potential is represented by two differents indicators : a score and different linear regressions. These preliminary searches led to the development of a data structure more complex, presented in the third chapter. Graph theory is central in this chapter. The final algorithm particularly uses shortest path calculation. It sorts a list of drivers for a given rider according to their ridesharing potential. A dedicated section of the thesis details this notion. The algorithm is evaluated thanks to different metrics related to Netlift data (found matches by Netlift algorithm) and the Origin-Destination Survey of Montreal conducted in 2008. The results are satisfying for classic ridesharing (without public transportation) since the implemented algorithm succeeds to give good drivers for a big amount of passengers fastly. More than one out of two among riders from the OD Survey has a driver to share the ride with for detours less than 10 minuts. The multimodal ridesharing potential for the morning peak period is evaluated by a study of rides from the Montreal 2008 OD Survey. Obtained matches lack of quality compared to classic ridesharing, but the used method deserves improvements and a perspective of future research. This project enables Netlift to gain in relevance and computation speed against the matches proposed by the current algorithm

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact, necessitating the adoption of green logistics practices to mitigate environmental effects. The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis. Operations research provides a means to balance environmental concerns and costs, thereby enhancing the management of logistical activities. This paper presents a comprehensive review of studies integrating operations research into green logistics. A systematic search was conducted in the Web of Science Core Collection database, covering papers published until June 3, 2023. Six keywords (green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation) were used to identify relevant papers. The reviewed studies were categorized into five main research directions: Green waste logistics, the impact of costs on green logistics, the green routing problem, green transport network design, and emerging challenges in green logistics. The review concludes by outlining suggestions for further research that combines green logistics and operations research, with particular emphasis on investigating the long-term effects of the pandemic on this field.</p

Vehicle dispatch in high-capacity shared autonomous mobility-on-demand systems

Ride-sharing is a promising solution for transportation issues such as traffic congestion and parking land use, which are brought about by the extensive usage of private vehicles. In the near future, large-scale Shared Autonomous Mobility-on-Demand (SAMoD) systems are expected to be deployed with the realization of self-driving vehicles. It has the potential to encourage a car-free lifestyle and create a new urban mobility mode where ride-sharing is widely adopted among people. This thesis addresses the problem of improving the efficiency and quality of vehicle dispatch in high-capacity SAMoD systems. The first part of the thesis develops a dispatcher which can efficiently explore the complete candidate match space and produce the optimal assignment policy when only deterministic information is concerned. It uses an incremental search method that can quickly prune out infeasible candidates to reduce the search space. It also has an iterative re-optimization strategy to dynamically alter the assignment policy to take into account both previous and newly revealed requests. Case studies of New York City using real-world data shows that it outperforms the state-of-the-art in terms of service rate and system scalability. The dispatcher developed in this part can serve as a foundation for the next two parts, which consider two kinds of uncertain information, stochastic travel times and the dynamic distribution of requests in the long-term future, respectively. The second part of the thesis describes a framework which makes use of stochastic travel time models to optimize the reliability of vehicle dispatch. It employs a candidate match search method to generate a candidate pool, uses a set of preprocessed shortest path tables to score the candidates and provides an assignment policy that maximizes the overall score. Two different dispatch objectives are discussed: the on-time arrival probabilities of requests and the profit of the platform. Experimental studies show that higher service rates, reliability and profits can be achieved by considering travel time uncertainty. The third part of the thesis presents a deep reinforcement learning based approach to optimize assignment polices in a more far-sighted way. It models the vehicle dispatch problem as a Markov Decision Process (MDP) and uses a policy evaluation method to learn a value function from the historic movements of drivers. The learned value function is employed to score candidate matches to guide a dispatcher optimizing long-term objective, and will be continually updated online to capture the real-time dynamics of the system. It is shown by experiments that the value function helps the dispatcher to yield higher service rates