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

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Differential Evolution with a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System

This paper studies an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV is employed as a data collection platform for a group of ground IoT devices. Our objective is to minimize the energy consumption of this system by optimizing the UAV’s deployment, including the number and locations of stop points of the UAV. When using evolutionary algorithms to solve this UAV’s deployment problem, each individual usually represents an entire deployment. Since the number of stop points is unknown a priori, the length of each individual in the population should be varied during the optimization process. Under this condition, the UAV’s deployment is a variable-length optimization problem and the traditional fixed-length mutation and crossover operators should be modified. In this paper, we propose a differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV’s deployment. In DEVIPS, the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment. Over the course of evolution, differential evolution is employed to produce offspring. Afterward, we design a strategy to adjust the population size according to the performance improvement. By this strategy, the number of stop points can be increased, reduced, or kept unchanged adaptively. In DEVIPS, since each individual has a fixed length, the UAV’s deployment becomes a fixed-length optimization problem and the traditional fixed-length mutation and crossover operators can be used directly. The performance of DEVIPS is compared with that of five algorithms on a set of instances. The experimental studies demonstrate its effectiveness

Multiobjective programming for type-2 hierarchical fuzzy inference trees

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases. Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a different input’s combination, where the evolutionary process governs the input’s combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selectio

A fuzzy approach to addressing uncertainty in Airport Ground Movement optimisation

Funded by Engineering and Physical Sciences Research Counci

Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State

As many new power infrastructures are planned under Sarawak State, the energy demand is expected to grow exponentially in these coming years. Besides, the minority of the rural villages are still not electrified yet. Fortunately, Sarawak State is blessed with indigenous Renewable Energy such as solar, hydro and wind power but they are scattered in the interior of the Sarawak State. Thus, the first phase is to develop a criteria scheme data for potential Renewable Energy Sources (RES) sites. It is followed by identifying RES sites using spatial data and Multi-Criteria Decision Making-Analytical Hierarchy Process (MCDM-AHP) algorithm. Accordingly, Spatial-Artificial Intelligence (AI) approach is utilised to integrate a high number of RES sites with minimum total distance. The research also proposed a hybrid Spatial-AI approach to integrate a high number of RES sites with minimum total distance and minimum total elevation difference. Initially, the Geographic Information System (GIS) tool is utilised to perform the assessments on current geographical conditions. From this, the spatial criteria scheme data is produced. The MCDM-AHP algorithm is applied to the criteria scheme data to identify the number of RES sites. Four cases were developed for RES sites integration, representing four different arrangements of RES sites. In each case, the Traveling Salesman Problem-Genetic Algorithm (TSP-GA) algorithm is applied to determine a minimum total distance of RES sites integration. Furthermore, a hybrid Spatial-Artificial Intelligence (AI) algorithm is proposed to integrate RES sites with minimum total distance and minimum total elevation difference. This research successfully identifies 55 solar energy sites and 15 wind energy sites. Meanwhile, 155 hydro energy sites were identified using the spatial map from Sarawak Energy Berhad (SEB). The second phase of the research work is to integrate the RES sites. TSP-GA algorithm is applied to generate the transmission line routing among the RES sites with minimum total distance. The minimum total distances in all four cases are acquired and validated as both the TSP-GA algorithm and the Traveling Salesman Problem-Mixed Integer Linear Programming (TSP-MILP) algorithm produced the same routing pattern. In the end, the proposed algorithm is successfully minimized the total distance and total elevation difference. The improved Spatial-AI algorithm showed approximately 15% better compared to ordinary TSP-GA in all four cases

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

NASA Tech Briefs, March 1995

This issue contains articles with a special focus on Computer-Aided design and engineering amd a research report on the Ames Research Center. Other subjects in this issue are: Electronic Components and Circuits, Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Manufacturing/Fabrication, Mathematics and Information Sciences and Life Science

Model predictive control for freeway traffic networks

Falta palabras claveTraffic congestion on freeways is a critical problem due to higher delays, waste of fuel, a higher accident risk probability, negative impact on the environment, etc. Variable speed limits, ramp metering, and reversible lanes are some of the most often used examples of freeway traffic measures that can be used to dynamically control traffic. Nowadays, most of the dynamic traffic control systems operate according to a linear and local control loop. As explained in the thesis, the use of appropriate non-local and multivariable techniques can considerably improve the reduction in the total time spent by the drivers and other traffic performance indexes. Nonlinear centralized Model Predictive Control (MPC) is probably the best control algorithm choice for a small network as can be seen on previous references. The main practical problem of nonlinear centralized MPC is that the computational time quickly increases with the size of the network making diffcult to apply centralized MPC for large networks. Therefore, completely centralized control of large networks is viewed by most practitioners as impractical and unrealistic. The main objective of this thesis is the proposal of MPC techniques which can be applied, in practice, to real large traffic networks. Possible solutions are the use of distributed MPC (considering the network as a set of subsystems controlling each subsystem by one independent MPC), hybrid MPC (splitting the problem in a continuous optimization for the ramp metering signals and in a discrete optimization for speed limits) or genetic algorithms (finding the fittest individuals within a generation, applying genetic operators for the recombination of those individuals, and generating a good offspring). This thesis proposes and analyses these solutions. Other open problem in freeway traffic control is the dynamic operation of reversible lanes. Despite the long history and widespread use of reversible lanes worldwide, there have been few quantitative evaluations and research studies conducted on their performance. To address this problem, this thesis proposes a macroscopic model for reversible lanes and on-line controllers for the operation of reversible lanes. Moreover, a MPC controller for freeway traffic requires a model to make accurate and reliable predictions of the traffic flow. On the other hand, this model is required to be fast enough, so that it can be used for on-line based control applications. Therefore, it is imperative to select or develop appropriate models, i.e., models that are fast and that provide accurate predictions. In this thesis, the METANET model and its extensions have been selected to be used for the prediction of the traffic flow and, based on this model, new advances in freeway traffic modeling for optimal control strategies are proposed.El ahorro de combustible, la mejora de la movilidad de los ciudadanos, la reducción de las emisiones atmosféricas y de los accidentes de tráfico son algunos de los aspectos claves en las políticas gubernamentales en el primer mundo. Durante los últimos años, un gran esfuerzo investigador se ha centrado en resolver, o mitigar, estos problemas. Debido a que la construcción de nuevos ramales viarios (o la ampliación de las ya presentes) no es siempre una opción viable (por razones económicas o técnicas), es necesaria la búsqueda de otras alternativas. Los sistemas de control dinámico de tráfico miden o estiman el estado de la circulación en cada instante y calculan la señal de control que cambia la respuesta del sistema mejorando su funcionamiento. Las señales de control de tráfico más útiles son los “ramps metering'' (o rampas de acceso controlado) y los “límites dinámicos de velocidad'' (VSL) porque son fáciles de implementar, relativamente baratos y suponen una mejora sustancial en el tiempo total de conducción empleado por los conductores (TTS). En la actualidad, la mayoría de los sistemas de control de tráfico operan usando un control clásico por realimentación, lineal y local. Sin embargo, el uso apropiado de técnicas multivariables y no locales mejorará substancialmente el comportamiento del sistema controlado. El uso de un controlador predictivo basado en modelo (MPC) centralizado es posiblemente la mejor elección para una red de tráfico pequeña. El problema fundamental del MPC centralizado es que el tiempo de computación crece exponencialmente con el tamaño de la red. Por tanto, este tipo de controladores son imposibles de implementar en tiempo real en redes suficientemente grandes. El principal objetivo de la tesis es diseñar un algoritmo de control que pueda ser calculado en tiempo real en una red viaria de gran escala minimizando, al mismo tiempo, el tiempo total de conducción empleado. Las principales contribuciones al estado del arte pueden enumerarse en: • La extensión del modelo de tráfico en autovías METANET para permitir el modelado de carriles reversibles. • Un algoritmo de identificación para los parámetros de METANET, especialmente pensado para casos donde solo hay disponible un número limitado de sensores. • El uso de una nueva definición matemática del diagrama fundamental de tráfico. • La primera comparación directa entre los dos modelos macroscópicos de tráfico más comúnmente usados. • El análisis de la robustez de los controladores predictivos aplicados a sistemas de tráfico (con respecto a variaciones de la demanda de tráfico). • La justificación de la necesidad de usar algoritmos de control globales o distribuidos (y no algoritmos locales) en sistemas de control de tráfico. • El uso de dos algoritmos predictivos distribuidos para el control de tráfico en autovías. • El diseño de un método para obtener los valores óptimos de los límites de velocidad considerando la característica discreta de los mismos y otras restricciones prácticas. • El diseño de un controlador MPC discreto para la operación de carriles reversibles. • Un algoritmo lógico fácilmente implementable para la operación de carriles reversibles.Premio Extraordinario de Doctorado U