An Adaptive Tabu Search Optimisation Algorithm for Solving E-Scooters Battery Swapping Problem

E-scooters have become a popular mode of transportation for first and last-mile excursions in recent years. Their usage as a short-distance public transit system is commonly regarded as an efficient solution to minimise carbon emissions while also being handy for individuals on the go. The need for charging, however, has become problematic as a result of the popularity of e-scooters because it effectively renders the scooter that is presently being charged inoperable. A relatively new method being used involves swapping batteries in e-scooters rather than transferring full scooters to be recharged, reducing out-of-service time to a few minutes rather than hours. To reduce trip distances and maximise fuel economy for battery swapping operators, a system for determining the most effective path to switch the e-scooters’ batteries will be required. This paper aims to do this through the use of Tabu Search (TS) algorithm to determine the optimal number of battery swapping operators for an area and then to ascertain the most efficient routes for each operator. This method will then be compared to Simulated Annealing in order to determine which method is the most optimal for this scenario. The data used to evaluate this method was obtained from the 2019 Chicago pilot program . The results showed an adapted tabu search in the total distance travelled, leading to shorter charging trips comparing to simulated annealing

Construction of an Optimal Solution for a Real-World Routing-Scheduling- Loading Problem Construcción de una Solución Óptima para un Problema de Asignación de Rutas, Horarios y Cargas del Mundo Real

Abstract This work presents an exact method for the Routing-Loading-Scheduling Problem (RoSLoP). The objective of RoSLoP consists of optimizing the delivery process of bottled products in a company study case. RoSLoP, formulated through the well-known Vehicle Routing Problem (VRP), has been solved as a rich VRP variant through approximate methods. The exact method uses a linear transformation function, which allows the reduction of the complexity of the problem to an integer programming problem. The optimal solution to this method establishes metrics of performance for approximate methods, which reach an efficiency of 100% in distance traveled and 75% in vehicles used, objectives of VRP. The transformation function reduces the computation time from 55 to four seconds. These results demonstrate the advantages of the modeling mathematical to reduce the dimensionality of problems NP-hard, which permits to obtain an optimal solution of RoSLoP. This modeling can be applied to get optimal solutions for real-world problems. Keywords: Optimization, Routing-Scheduling-Loading Problem (RoSLoP), Vehicle Routing Problem (VRP), rich VRP. Resumen Éste trabajo presenta un método exacto para el problema de Asignación de Rutas, Horarios y Cargas (RoSLoP). El objetivo de RoSLoP consiste en optimizar el proceso de entrega de productos embotellados en una compañía caso de estudio. El problema RoSLoP, formulado a través del conocido Problema de Enrutado de Vehículos (VRP), ha sido resuelto como una variable VRP enriquecida a través de métodos aproximados. El método exacto usa una función de transformación lineal, la cual permite la reducción de la complejidad del problema a un problema de programación entera. La solución óptima para éste método establece las métricas del desempeño para los métodos aproximados, los cuales alcanzan una eficiencia del 100% en distancia recorrida y 75% en vehículos utilizados, objetivos del VRP. La función de transformación reduce el tiempo del cálculo de 55 a cuatro segundos. Éstos resultados demuestran las ventajas del modelado matemático para reducir la dimensionalidad de problemas NPDuros, lo cual permite la obtención de una solución óptima del problema RoSLoP. Éste modelado puede ser aplicado para obtener las soluciones óptimas para problemas del mundo real. Palabras Clave: Optimización, Problema de Asignación de Rutas, Horarios y Cargas (RoSLoP), Problema de Enrutado de Vehículos (VRP), Problema VRP Enriquecido

A Matheuristic Approach for the Multi-Depot Periodic Petrol Station Replenishment Problem

Planning petrol station replenishment is an important logistics activity for all the major oil companies. The studied Multi-Depot Periodic Petrol Station Replenishment problem derives from a real case in which the company must replenish a set of petrol stations from a set of depots, during a weekly planning horizon. The company must ensure refuelling according to available visiting patterns, which can be different from customer to customer. A visiting pattern predefines how many times (days) the replenishment occurs during a week and in which visiting days how much fuel must be delivered. To fulfil the weekly demand of each petrol station, one of the available replenishment plans must be selected among a given set of visiting patterns. The aim is to minimize the total distance travelled by the fleet of tank trucks during the entire planning horizon. We provide a math-heuristic approach, based on cluster-first route-second paradigm, to solve it. We thoroughly experiment our approach on a set of realistic random instances. Finally, we consider a weekly large real instance with 194 petrol stations and 2 depots

A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

Dynamic programming for the orienteering problem with time windows

We present an exact optimization algorithm for the Orienteering Problem with Time Windows (OPTW). The algorithm is based on bi-directional and bounded dynamic programming with decremental state space relaxation. We compare different strategies proposed in the literature to guide decremental state space relaxation: our experiments on instances derived from the literature show that there is no dominance between these strategies. We also propose a new heuristic technique to initialize the critical vertex set and we provide experimental evidence of its effectiveness

Human movement route mapping: A study in the Galapagos Islands

This work focuses on the study of human maritime movement within the Galapagos Islands. As they are one of the biggest marine reserves in the world it is important to have control of the maritime transport with the purpose of minimizing its impact in the islands. Historic data was gathered, verified and analyzed for the subsequent application of mathematical optimization models. The aim is to propose an improvement of the dynamic of the transportation in the Islands...El presente trabajo se centra en el estudio del movimiento humano marítimo inter-islas en el archipiélago de Galápagos. Al ser una de las reservas marinas más grandes del mundo resulta de gran importancia mantener un control del transporte marítimo, con el fin de que este tenga un impacto mínimo en el ecosistema. Se realizó una recopilación, verificación y análisis de datos históricos para la posterior aplicación de modelos de optimización que permitan un mejoramiento de la dinámica de transporte existente en las islas..

Approches générales de résolution pour les problèmes multi-attributs de tournées de véhicules et confection d'horaires

Thèse réalisée en cotutelle entre l'Université de Montréal et l'Université de Technologie de TroyesLe problème de tournées de véhicules (VRP) implique de planifier les itinéraires d'une flotte de véhicules afin de desservir un ensemble de clients à moindre coût. Ce problème d'optimisation combinatoire NP-difficile apparait dans de nombreux domaines d'application, notamment en logistique, télécommunications, robotique ou gestion de crise dans des contextes militaires et humanitaires. Ces applications amènent différents contraintes, objectifs et décisions supplémentaires ; des "attributs" qui viennent compléter les formulations classiques du problème. Les nombreux VRP Multi-Attributs (MAVRP) qui s'ensuivent sont le support d'une littérature considérable, mais qui manque de méthodes généralistes capables de traiter efficacement un éventail significatif de variantes. Par ailleurs, la résolution de problèmes "riches", combinant de nombreux attributs, pose d'importantes difficultés méthodologiques. Cette thèse contribue à relever ces défis par le biais d'analyses structurelles des problèmes, de développements de stratégies métaheuristiques, et de méthodes unifiées. Nous présentons tout d'abord une étude transversale des concepts à succès de 64 méta-heuristiques pour 15 MAVRP afin d'en cerner les "stratégies gagnantes". Puis, nous analysons les problèmes et algorithmes d'ajustement d'horaires en présence d'une séquence de tâches fixée, appelés problèmes de "timing". Ces méthodes, développées indépendamment dans différents domaines de recherche liés au transport, ordonnancement, allocation de ressource et même régression isotonique, sont unifiés dans une revue multidisciplinaire. Un algorithme génétique hybride efficace est ensuite proposé, combinant l'exploration large des méthodes évolutionnaires, les capacités d'amélioration agressive des métaheuristiques à voisinage, et une évaluation bi-critère des solutions considérant coût et contribution à la diversité de la population. Les meilleures solutions connues de la littérature sont retrouvées ou améliorées pour le VRP classique ainsi que des variantes avec multiples dépôts et périodes. La méthode est étendue aux VRP avec contraintes de fenêtres de temps, durée de route, et horaires de conducteurs. Ces applications mettent en jeu de nouvelles méthodes d'évaluation efficaces de contraintes temporelles relaxées, des phases de décomposition, et des recherches arborescentes pour l'insertion des pauses des conducteurs. Un algorithme de gestion implicite du placement des dépôts au cours de recherches locales, par programmation dynamique, est aussi proposé. Des études expérimentales approfondies démontrent la contribution notable des nouvelles stratégies au sein de plusieurs cadres méta-heuristiques. Afin de traiter la variété des attributs, un cadre de résolution heuristique modulaire est présenté ainsi qu'un algorithme génétique hybride unifié (UHGS). Les attributs sont gérés par des composants élémentaires adaptatifs. Des expérimentations sur 26 variantes du VRP et 39 groupes d'instances démontrent la performance remarquable de UHGS qui, avec une unique implémentation et paramétrage, égalise ou surpasse les nombreux algorithmes dédiés, issus de plus de 180 articles, révélant ainsi que la généralité ne s'obtient pas forcément aux dépends de l'efficacité pour cette classe de problèmes. Enfin, pour traiter les problèmes riches, UHGS est étendu au sein d'un cadre de résolution parallèle coopératif à base de décomposition, d'intégration de solutions partielles, et de recherche guidée. L'ensemble de ces travaux permet de jeter un nouveau regard sur les MAVRP et les problèmes de timing, leur résolution par des méthodes méta-heuristiques, ainsi que les méthodes généralistes pour l'optimisation combinatoire.The Vehicle Routing Problem (VRP) involves designing least cost delivery routes to service a geographically-dispersed set of customers while taking into account vehicle-capacity constraints. This NP-hard combinatorial optimization problem is linked with multiple applications in logistics, telecommunications, robotics, crisis management in military and humanitarian frameworks, among others. Practical routing applications are usually quite distinct from the academic cases, encompassing additional sets of specific constraints, objectives and decisions which breed further new problem variants. The resulting "Multi-Attribute" Vehicle Routing Problems (MAVRP) are the support of a vast literature which, however, lacks unified methods capable of addressing multiple MAVRP. In addition, some "rich" VRPs, i.e. those that involve several attributes, may be difficult to address because of the wide array of combined and possibly antagonistic decisions they require. This thesis contributes to address these challenges by means of problem structure analysis, new metaheuristics and unified method developments. The "winning strategies" of 64 state-of-the-art algorithms for 15 different MAVRP are scrutinized in a unifying review. Another analysis is targeted on "timing" problems and algorithms for adjusting the execution dates of a given sequence of tasks. Such methods, independently studied in different research domains related to routing, scheduling, resource allocation, and even isotonic regression are here surveyed in a multidisciplinary review. A Hybrid Genetic Search with Advanced Diversity Control (HGSADC) is then introduced, which combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and a bi-criteria evaluation of solutions based on cost and diversity measures. Results of remarkable quality are achieved on classic benchmark instances of the capacitated VRP, the multi-depot VRP, and the periodic VRP. Further extensions of the method to VRP variants with constraints on time windows, limited route duration, and truck drivers' statutory pauses are also proposed. New route and neighborhood evaluation procedures are introduced to manage penalized infeasible solutions w.r.t. to time-window and duration constraints. Tree-search procedures are used for drivers' rest scheduling, as well as advanced search limitation strategies, memories and decomposition phases. A dynamic programming-based neighborhood search is introduced to optimally select the depot, vehicle type, and first customer visited in the route during local searches. The notable contribution of these new methodological elements is assessed within two different metaheuristic frameworks. To further advance general-purpose MAVRP methods, we introduce a new component-based heuristic resolution framework and a Unified Hybrid Genetic Search (UHGS), which relies on modular self-adaptive components for addressing problem specifics. Computational experiments demonstrate the groundbreaking performance of UHGS. With a single implementation, unique parameter setting and termination criterion, this algorithm matches or outperforms all current problem-tailored methods from more than 180 articles, on 26 vehicle routing variants and 39 benchmark sets. To address rich problems, UHGS was included in a new parallel cooperative solution framework called "Integrative Cooperative Search (ICS)", based on problem decompositions, partial solutions integration, and global search guidance. This compendium of results provides a novel view on a wide range of MAVRP and timing problems, on efficient heuristic searches, and on general-purpose solution methods for combinatorial optimization problems

Constraint Programming-Based Heuristics for the Multi-Depot Vehicle Routing Problem with a Rolling Planning Horizon

Der Transportmarkt ist sowohl durch einem intensiven Kostenwettbewerb als auch durch hohe Erwartungen der Kunden an den Service geprägt. Die vorliegende Dissertation stellt zwei auf Constraint Programming basierende heuristische Frameworks vor, die eine Reoptimierung bereits geplanter Touren zu festgelegten Zeitpunkten erlauben und so eine Reaktion auf die gesteigerte Wettbewerbsdynamik und den Kostendruck ermöglichen.Actors on the transportation market currently face two contrary trends: Cost pressure caused by intense competition and a need for prompt service. We introduce two heuristic solution frameworks to enable freight carriers to deal with this situation by reoptimizing tours at predefined points in time. Both heuristics are based on Constraint Programming techniques

Heuristic solution methods for multi-attribute vehicle routing problems

Le Problème de Tournées de Véhicules (PTV) est une clé importante pour gérér efficacement des systèmes logistiques, ce qui peut entraîner une amélioration du niveau de satisfaction de la clientèle. Ceci est fait en servant plus de clients dans un temps plus court. En terme général, il implique la planification des tournées d'une flotte de véhicules de capacité donnée basée à un ou plusieurs dépôts. Le but est de livrer ou collecter une certain quantité de marchandises à un ensemble des clients géographiquement dispersés, tout en respectant les contraintes de capacité des véhicules. Le PTV, comme classe de problèmes d'optimisation discrète et de grande complexité, a été étudié par de nombreux au cours des dernières décennies. Étant donné son importance pratique, des chercheurs dans les domaines de l'informatique, de la recherche opérationnelle et du génie industrielle ont mis au point des algorithmes très efficaces, de nature exacte ou heuristique, pour faire face aux différents types du PTV. Toutefois, les approches proposées pour le PTV ont souvent été accusées d'être trop concentrées sur des versions simplistes des problèmes de tournées de véhicules rencontrés dans des applications réelles. Par conséquent, les chercheurs sont récemment tournés vers des variantes du PTV qui auparavant étaient considérées trop difficiles à résoudre. Ces variantes incluent les attributs et les contraintes complexes observés dans les cas réels et fournissent des solutions qui sont exécutables dans la pratique. Ces extensions du PTV s'appellent Problème de Tournées de Véhicules Multi-Attributs (PTVMA). Le but principal de cette thèse est d'étudier les différents aspects pratiques de trois types de problèmes de tournées de véhicules multi-attributs qui seront modélisés dans celle-ci. En plus, puisque pour le PTV, comme pour la plupart des problèmes NP-complets, il est difficile de résoudre des instances de grande taille de façon optimale et dans un temps d'exécution raisonnable, nous nous tournons vers des méthodes approcheés à base d’heuristiques.The Vehicle Routing Problem (VRP) is an important key to efficient logistics system management, which can result in higher level of customer satisfaction because more customers can be served in a shorter time. In broad terms, it deals with designing optimal delivery or collection routes from one or several depot(s) to a number of geographically scattered customers subject to side constraints. The VRP is a discrete optimization and computationally hard problem and has been extensively studied by researchers and practitioners during the past decades. Being complex problems with numerous and relevant potential applications, researchers from the fields of computer science, operations research and industrial engineering have developed very efficient algorithms, both of exact and heuristic nature, to deal with different types of VRPs. However, VRP research has often been criticized for being too focused on oversimplified versions of the routing problems encountered in real-life applications. Consequently, researchers have recently turned to variants of the VRP which before were considered too difficult to solve. These variants include those attributes and constraints observed in real-life planning and lead to solutions that are executable in practice. These extended problems are called Multi-Attribute Vehicle Routing Problems (MAVRPs). The main purpose of this thesis is to study different practical aspects of three multi-attribute vehicle routing problems which will be modeled in it. Besides that, since the VRP has been proved to be NP-hard in the strong sense such that it is impossible to optimally solve the large-sized problems in a reasonable computational time by means of traditional optimization approaches, novel heuristics will be designed to efficiently tackle the created models