Simulation and Control of Groups of People in Multi-modal Mobility

Tourism and transport are constantly growing and, with it, the movements of travellers. This entails two fundamental effects on which we must focus: control of mass tourism and the organization of transport. Good transport organization and travel planning avoid crowds and therefore mass tourism. This allows promoting sustainable tourism in which it is sought to offer a quality service to tourists taking care of the environment. In this thesis the objective is to manage the flow of groups of people through means of transport. This control of groups of people is aimed at customer satisfaction by offering quality tourism. On the one hand, the study focuses on the problem to mitigate the negative effects due to mass arrivals in touristic locations. A TEN network has been developed to define the optimal tours for different groups of tourists. A related mixed integer quadratic optimization model has been developed with three main objectives: it minimizes the maximum value of occupancy in the selected destinations to limit mass tourism, reduces the divergence between the proposed visit tour and one required by the tourist group and the overall duration of their visit, and a heuristic approach has been introduced. On the other hand, it has been implemented a railway scheduling and rescheduling problem introducing optimization-based and min-max approaches on the regional and high-speed railway network. The scheduling model defines the best schedules for a set of trains considering costumers\u2019 demand and the priority of the trains to cover the rail sections in case of conflict on the railway lines. Consecutively, the generated feasible timetables are used to minimize possible consequences due to events that may negatively affect the real time traffic management. The main contribution of this section is the introduction in the second approach the innovative concept to prioritize the train that can access on the block section in case of conflicts on the network

Measuring and predicting adaptation in multidimensional activity-travel patterns

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The habit-driven life: Accounting for inertia in departure time choices for commuting trips

This paper aims to explicitly account for the impact of inertia (or habit) on departure time decisions, and explore (1) to what extent departure time is influenced by inertia, (2) what influences individuals’ inertia with respect to departure time decisions, and (3) to what extent it impacts transport policies. We estimate an integrated choice and latent variable (ICLV) model using a stated preference survey for morning car commuters in the Greater Copenhagen Area. We interact the rescheduling components in the Scheduling Model (SM) with the latent variable Inertia. The modelling results show that higher levels of inertia yields higher rescheduling penalties and lower willing to shift departure time. Furthermore, we find that inertia in departure time is influenced by gender, presence of children in the household as well as work type. We test the behavioral responses to demand management policies for segments with different inertia, and find that the least inertial segment showed the highest substitution patterns, while the most inertial segment show the lowest substitution patterns. Finally, we compared the ICLV model to a reference model without inertia, and find that the effects of the demand management strategy is overestimated if inertia is neglected

Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems

Recovering the timetable after a delay is essential to the smooth and efficient operation of the railways for both passengers and railway operators. Most current railway rescheduling research concentrates on static problems where all delays are known about in advance. However, due to the unpredictable nature of the railway system, it is possible that further unforeseen incidents could occur while the trains are running to the new rescheduled timetable. This will change the problem, making it a dynamic problem that changes over time. The aim of this work is to investigate the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective railway rescheduling problems. ACO is a promising approach for dynamic combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to the new environment while retaining potentially useful information from the previous environment. In addition, ACO is able to handle multi-objective problems by the addition of multiple colonies and/or multiple pheromone and heuristic matrices. The contributions of this work are the development of a junction simulator to model unique dynamic and multi-objective railway rescheduling problems and an investigation into the application of ACO algorithms to solve those problems. A further contribution is the development of a unique two-colony ACO framework to solve the separate problems of platform reallocation and train resequencing at a UK railway station in dynamic delay scenarios. Results showed that ACO can be e ectively applied to the rescheduling of trains in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic junction rescheduling problem ACO outperformed First Come First Served (FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof- the-art multi-objective algorithm. When considering platform reallocation and rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These results suggest that ACO shows promise for the rescheduling of trains in both dynamic and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC

Optimization Models and Approximate Algorithms for the Aerial Refueling Scheduling and Rescheduling Problems

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for fighter aircrafts (jobs) on multiple tankers (machines) to minimize the total weighted tardiness. ARSP can be modeled as a parallel machine scheduling with release times and due date-to-deadline window. ARSP assumes that the jobs have different release times, due dates, and due date-to-deadline windows between the refueling due date and a deadline to return without refueling. The Aerial Refueling Rescheduling Problem (ARRP), on the other hand, can be defined as updating the existing AR schedule after being disrupted by job related events including the arrival of new aircrafts, departure of an existing aircrafts, and changes in aircraft priorities. ARRP is formulated as a multiobjective optimization problem by minimizing the total weighted tardiness (schedule quality) and schedule instability. Both ARSP and ARRP are formulated as mixed integer programming models. The objective function in ARSP is a piecewise tardiness cost that takes into account due date-to-deadline windows and job priorities. Since ARSP is NP-hard, four approximate algorithms are proposed to obtain solutions in reasonable computational times, namely (1) apparent piecewise tardiness cost with release time rule (APTCR), (2) simulated annealing starting from random solution (SArandom ), (3) SA improving the initial solution constructed by APTCR (SAAPTCR), and (4) Metaheuristic for Randomized Priority Search (MetaRaPS). Additionally, five regeneration and partial repair algorithms (MetaRE, BestINSERT, SEPRE, LSHIFT, and SHUFFLE) were developed for ARRP to update instantly the current schedule at the disruption time. The proposed heuristic algorithms are tested in terms of solution quality and CPU time through computational experiments with randomly generated data to represent AR operations and disruptions. Effectiveness of the scheduling and rescheduling algorithms are compared to optimal solutions for problems with up to 12 jobs and to each other for larger problems with up to 60 jobs. The results show that, APTCR is more likely to outperform SArandom especially when the problem size increases, although it has significantly worse performance than SA in terms of deviation from optimal solution for small size problems. Moreover CPU time performance of APTCR is significantly better than SA in both cases. MetaRaPS is more likely to outperform SAAPTCR in terms of average error from optimal solutions for both small and large size problems. Results for small size problems show that MetaRaPS algorithm is more robust compared to SAAPTCR. However, CPU time performance of SA is significantly better than MetaRaPS in both cases. ARRP experiments were conducted with various values of objective weighting factor for extended analysis. In the job arrival case, MetaRE and BestINSERT have significantly performed better than SEPRE in terms of average relative error for small size problems. In the case of job priority disruption, there is no significant difference between MetaRE, BestINSERT, and SHUFFLE algorithms. MetaRE has significantly performed better than LSHIFT to repair job departure disruptions and significantly superior to the BestINSERT algorithm in terms of both relative error and computational time for large size problems

Arc Routing Problems for Road Network Maintenance

RÉSUMÉ : Cette thèse présente deux problèmes rencontrés dans l’entretien des réseaux routiers, soit la surveillance des réseaux routiers pour la détection de verglas sur la chaussée et la reprogrammation des itinéraires pour les activités de déneigement et d’épandage de sel. Nous représentons ces problèmes par des modèles de tournées sur les arcs. La dépendance aux moments et la nature dynamique sont des caractéristiques propres de ces problèmes, par conséquence le cas de surveillance des réseaux routiers est modélisé comme un problème de postier rural avec fenêtres-horaires (RPPTW), tandis que le cas de la reprogrammation utilise des modèles obtenus à partir des formulations de problèmes de tournées sur les arcs avec capacité. Dans le cas du problème de surveillance, une patrouille vérifie l’état des chemins et des autoroutes, elle doit principalement détecter le verglas sur la chaussée dans le but d’assurer de bonnes conditions aux chauffeurs et aux piétons. Étant donné un réseau routier et des prévisions météo, le problème consiste à créer une tournée qui permette de détecter opportunément le verglas sur les rues et les routes. L’objectif poursuivi consiste à minimiser le coût de cette opération. En premier, on présente trois formulations basées sur la programmation linéaire en nombres entiers pour le problème de surveillance des réseaux qui dépend du moment et deux méthodes de résolution: un algorithme de coupes et un algorithme heuristique appelé adaptive large neighborhood search (ALNS). La méthode exacte inclut des inéquations valides tirées du problème du voyageur de commerce avec fenêtres-horaires et aussi du problème de voyageur du commerce avec contraintes de précédence. La méthode heuristique considère deux phases: en premier, on trouve une solution initiale et après dans la deuxième phase, l’algorithme essaie d’améliorer la solution initiale en utilisant sept heuristiques de destruction et deux heuristiques de réparation choisies au hasard. La performance des heuristiques est évaluée pendant les itérations. Une meilleure performance correspond à une plus grande probabilité de choisir une heuristique. Plusieurs tests ont été faits sur deux ensembles d’exemplaires de problèmes. Les résultats obtenus montrent que l’algorithme de coupes est capable de résoudre des réseaux avec 104 arêtes requises et des fenêtres-horaires structurées par tranches horaires ; l’algorithme peut aussi résoudre des réseaux avec 45 arêtes requises et des fenêtres-horaires structurées pour chaque arête requise. Pour l’algorithme ALNS, différentes versions de l’algorithme sont comparées. Les résultats montrent que cette méthode est efficace parce qu’elle est capable de résoudre à l’optimalité 224 des 232 exemplaires et de réduire le temps de calcul significativement pour les exemplaires les plus difficiles. La dernière partie de la thèse introduit le problème de la reprogrammation de tournées sur les arcs avec capacité (RCARP), lequel permet de modéliser la reprogrammation des itinéraires après une panne d’un véhicule lors de la phase d’exécution d’un plan initial des activités de déneigement ou d’épandage de sel. Le planificateur doit alors modifier le plan initial rapidement et reprogrammer les véhicules qui restent pour finir les activités. Dans ce cas, l’objectif poursuivi consiste à minimiser le coût d’opération et le coût de perturbation. La distance couverte par les véhicules correspond au coût d’opération, cependant une nouvelle métrique est développée pour mesurer le coût de perturbation. Les coûts considérés sont des objectifs en conflit. On analyse quatre politiques à la phase de re-routage en utilisant des formulations de programmation linéaire en nombres entiers. On propose une solution heuristique comme méthode pour résoudre le RCARP quand les coûts d’opération et de perturbation sont minimisés en même temps et quand une réponse rapide est nécessaire. La méthode consiste à fixer une partie de l’itinéraire initial et après à modifier seulement les itinéraires des véhicules les plus proches de la zone de l’interruption de la tournée du véhicule défaillant. La méthode a été testée sur des exemplaires obtenus d’un réseau réel. Nos tests indiquent que la méthode peut résoudre rapidement des exemplaires avec 88 arêtes requises et 10 véhicules actifs après la panne d’un véhicule. En conclusion, la principale contribution de cette thèse est de présenter des modèles de tournées sur les arcs et de proposer des méthodes de résolution d’optimisation qui incluent la dépendance aux temps et l’aspect dynamique. On propose des modèles et des méthodes pour résoudre le RPPTW, et on présente des résultats pour ce problème. On introduit pour la première fois le RCARP. Trois articles correspondant aux trois principaux chapitres ont été acceptés ou soumis à des revues avec comité de Lecture: “The rural postman problem with time windows” accepté dans Networks, “ALNS for the rural postman problem with time windows” soumis à Networks, and “The rescheduling capacitated arc routing problem” soumis à International Transactions in Operational Research.----------ABSTRACT : This dissertation addresses two problems related to road network maintenance: the road network monitoring of black-ice and the rescheduling of itineraries for snow plowing and salt spreading operations. These problems can naturally be represented using arc routing models. Timing-sensitive and dynamic nature are inherent characteristics of these problems, therefore the road network monitoring is modeled as a rural postman problem with time windows (RPPTW) and in the rescheduling case, models based on capacitated arc routing formulations are suggested for the rerouting phase. The detection of black-ice on the roads is carried out by a patrol to ensure safety conditions for drivers and pedestrians. Specific meteorological conditions cause black-ice on the roads; therefore the patrol must design a route covering part of the network in order to timely detect the black-ice according to weather forecasts. We look for minimum-cost solutions that satisfy the timing constraints. At first, three formulations based on mixed integer linear programming are presented for the timing-sensitive road network monitoring and two solution approaches are proposed: a cutting plane algorithm and an adaptive large neighborhood search (ALNS) algorithm. The exact method includes valid inequalities from the traveling salesman problem (TSP) with time windows and from the precedence constrained TSP. The heuristic method consists of two phases: an initial solution is obtained, and then in the second phase the ALNS method tries to improve the initial solution using seven removal and two insertion heuristics. The performance of the heuristics is evaluated during the iterations, and therefore the heuristics are selected depending on their performance (with higher probability for the better ones). Several tests are done on two sets of instances. The computational experiments performed show that the cutting plane algorithm is able to solve instances with up to 104 required edges and with time windows structured by time slots, and problems with up to 45 required edges and time windows structured by each required edge. For the ALNS algorithm, several versions of the algorithm are compared. The results show that this approach is efficient, solving to optimality 224 of 232 instances and significantly reducing the computational time on the hardest instances. The last part of the dissertation introduces the rescheduling capacitated arc routing problem (RCARP), which models the rescheduling of itineraries after a vehicle failure happens in the execution of an initial plan of snow plowing or salt spreading operations. A dispatcher must quickly adjust the remaining vehicles and modify the initial plan in order to complete the operations. In this case we look for solutions that minimize operational and disruption costs. The traveled distance represents the operational cost, and a new metric is discussed as disruption cost. The concerned objectives are in conflict. Four policies are analyzed in the rerouting phase using mixed integer linear programming formulations. A heuristic solution is developed to solve the RCARP when operational and disruption costs are minimized simultaneously and a quick response is needed. The idea is to fix part of the initial itinerary and only modify the itinerary of vehicles closer to the failure zone. The method is tested on a set of instances generated from a real network. Our tests indicate that the method can solve instances with up to 88 required edges and 10 active vehicles after the vehicle breakdown. In short the main contribution of this dissertation is to present arc routing models and optimization solution techniques that consider timing-sensitive and dynamic aspects. Formulations and solution methods with computational results are given for the RPPTW, and the RCARP is studied for the first time here. Three articles corresponding to the main three chapters have been accepted or submitted to peer review journals: “The rural postman problem with time windows” accepted in Networks, “ALNS for the rural postman problem with time windows” submitted to Networks, and “The rescheduling capacitated arc routing problem” submitted to International Transactions in Operational Research