Biased-Randomized Discrete-Event heuristics for dynamic optimization with time dependencies and synchronization

Many real-life combinatorial optimization problems are subject to a high degree of dynamism, while, simultaneously, a certain level of synchronization among agents and events is required. Thus, for instance, in ride-sharing operations, the arrival of vehicles at pick-up points needs to be synchronized with the times at which users reach these locations so that waiting times do not represent an issue. Likewise, in warehouse logistics, the availability of automated guided vehicles at an entry point needs to be synchronized with the arrival of new items to be stored. In many cases, as operational decisions are made, a series of interdependent events are scheduled for the future, thus making the synchronization task one that traditional optimization methods cannot handle easily. On the contrary, discrete-event simulation allows for processing a complex list of scheduled events in a natural way, although the optimization component is missing here. This paper discusses a hybrid approach in which a heuristic is driven by a list of discrete events and then extended into a biased-randomized algorithm. As the paper discusses in detail, the proposed hybrid approach allows us to efficiently tackle optimization problems with complex synchronization issuesPeer ReviewedPostprint (published version

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

[EN] This work proposes a persuasion model based on argumentation theory and users' characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system.This work was supported by the projects TIN2015-65515-C4-1-R and TIN2017-89156-R of the Spanish government, and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politècnica de València.Diez-Alba, C.; Palanca Cámara, J.; Sanchez-Anguix, V.; Heras, S.; Giret Boggino, AS.; Julian Inglada, VJ. (2019). Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach. Energies. 12(4):1-19. https://doi.org/10.3390/en12040662S119124Erdoğan, G., Laporte, G., & Wolfler Calvo, R. (2014). The static bicycle relocation problem with demand intervals. European Journal of Operational Research, 238(2), 451-457. doi:10.1016/j.ejor.2014.04.013Alvarez-Valdes, R., Belenguer, J. M., Benavent, E., Bermudez, J. D., Muñoz, F., Vercher, E., & Verdejo, F. (2016). Optimizing the level of service quality of a bike-sharing system. Omega, 62, 163-175. doi:10.1016/j.omega.2015.09.007Schuijbroek, J., Hampshire, R. C., & van Hoeve, W.-J. (2017). Inventory rebalancing and vehicle routing in bike sharing systems. European Journal of Operational Research, 257(3), 992-1004. doi:10.1016/j.ejor.2016.08.029Li, L., & Shan, M. (2016). Bidirectional Incentive Model for Bicycle Redistribution of a Bicycle Sharing System during Rush Hour. Sustainability, 8(12), 1299. doi:10.3390/su8121299Anagnostopoulou, E., Bothos, E., Magoutas, B., Schrammel, J., & Mentzas, G. (2018). Persuasive Technologies for Sustainable Mobility: State of the Art and Emerging Trends. Sustainability, 10(7), 2128. doi:10.3390/su10072128Galbrun, E., Pelechrinis, K., & Terzi, E. (2016). Urban navigation beyond shortest route: The case of safe paths. Information Systems, 57, 160-171. doi:10.1016/j.is.2015.10.005Ferrara, J. (2013). Games for Persuasion: Argumentation, Procedurality, and the Lie of Gamification. Games and Culture, 8(4), 289-304. doi:10.1177/1555412013496891Fei, X., Shah, N., Verba, N., Chao, K.-M., Sanchez-Anguix, V., Lewandowski, J., … Usman, Z. (2019). CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future Generation Computer Systems, 90, 435-450. doi:10.1016/j.future.2018.06.042Faed, A., Hussain, O. K., & Chang, E. (2013). A methodology to map customer complaints and measure customer satisfaction and loyalty. Service Oriented Computing and Applications, 8(1), 33-53. doi:10.1007/s11761-013-0142-6Xu, W., Li, Z., Cheng, C., & Zheng, T. (2012). Data mining for unemployment rate prediction using search engine query data. Service Oriented Computing and Applications, 7(1), 33-42. doi:10.1007/s11761-012-0122-2GARCÍA, A. J., & SIMARI, G. R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95-138. doi:10.1017/s147106840300167

The multi-vehicle stochastic-dynamic inventory routing problem for bike sharing systems

We address the operational management of station-based bike sharing systems (BSSs). In BSSs, users can spontaneously rent and return bikes at any stations in the system. Demand is driven by commuter, shopping, and leisure activities. This demand constitutes a regular pattern of bike usage over the course of the day but also shows a significant short-term uncertainty. Due to the heterogeneity and the uncertainty in demand, stations may run out of bikes or congest during the day. At empty stations, no rental demand can be served. At full stations, no return demand can be served. To avoid unsatisfied demand, providers dynamically relocate bikes between stations in reaction of current shortages or congestion, but also in anticipation of potential future demand. For this real-time decision problem, we present a method that anticipates potential future demands based on historical observations and that coordinates the fleet of vehicles accordingly. We apply our method for two case studies based on real-world data of the BSSs in Minneapolis and San Francisco. We show that our policy outperforms benchmark policies from the literature. Moreover, we analyze how the interplay between anticipation and coordination is essential for the successful operational management of BSSs. Finally, we reveal that the value of coordination and anticipation based on the demand-structure of the BSS under consideration

New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201

A decision support system for the management of smart mobility services

Master Dissertation (Master Degree in Engineering and Management of Information Systems)Nos dias que correm, a mobilidade assume especial importância no quotidiano das áreas metropolitanas em crescimento no país. . Com o notório crescimento das cidades, torna-se necessária e urgente uma transformação dos costumes e formas de mobilidade dentro das áreas urbanas, alterando as realidades aparentes que hoje conhecemos. Inseridos numa sociedade cada vez mais consciencializada e alerta para as questões ambientais, é essencial transportar esta mentalidade renovada para a resolução das problemáticas citadinas. Assim, o conceito de “Cidade Verde” levanta uma série de questões que exigem uma resposta eficaz para o bem-estar dos seus habitantes. Por entre as várias soluções apresentadas para estas patologias, uma das mais promissoras é, sem dúvida, o sistema de mobilidade partilhada. Pela sua dimensão, é pertinente expor o caso prático da cidade de Barcelona, em Espanha, explorando o seu sistema de partilha de scooters, um meio que adquire especial importância como meio de transporte urbano. Como qualquer sistema em constante aprimoramento, procura-se uma solução para a problemática da variação de procura, que apresenta oscilações constantes, tanto a nível temporal como geográfico, resultando na falta de veículos em algumas áreas e excesso noutras. Assim sendo, o rebalanceamento do sistema torna-se crucial para uma possível maximização na utilização de veículos, satisfazendo a procura e potenciando um aumento da sua utilização. No correr desta dissertação, foram estudados e utilizados vários métodos de otimização moderna (metaheurísticas) para a procura de soluções (sub)ótimas para o(s) percurso(s) a percorrer pelo(s) veículo(s) que executam a redistribuição das scooter/bicicletas pelas diversas áreas abrangidas pelo sistema de partilha. Deste modo, foi desenvolvido um sistema de apoio à decisão para satisfazer estas necessidades, garantindo ao utilizador toda a informação relevante para um trabalho mais eficiente e preciso.Nowadays, mobility is especially important in the daily life of the country growing metropolitan areas. With the increasing influx of people and development of these large cities, the reality of mobility that we know becomes increasingly unsustainable. Along with mobility, the environmental concerns are one of the main topics of discussion worldwide and the population is starting to act and change the way they live to find a more “green” and sustainable way of doing it. Several proposals have been put forward, trying to mitigate this issue and, one of the most promising is, undoubtedly, shared mobility systems. In this case study will be addressed the Barcelona scooter sharing system, characterized by its great size and importance as a mean of urban transport. One of the problems presented by these sharing services is that demand varies widely, both temporal and geographical. Thus, there are several cases where there is a lack of vehicles in some areas and an excess in others. The rebalancing of the system is crucial to maximize vehicle utilization and meet customer demand. In this thesis, several modern optimization methods (metaheuristics) were used to search for (sub)optimal solutions for the redistribution route(s). A decision support system was developed to meet this end, giving the end user relevant information for a more efficient and precise work

서울시 ‘따릉이’를 중심으로

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 황준석.더욱 합리적이고 인간적인 공공자전거 서비스를 제공하기 위해서 공공자전거가 시스템 더 효율적으로 운영되고 서비스 이용자 만족도를 높이도록 한 가지 자전거 재배치 경로 최적화 목적으로 공공자전거 재배치 최적화 모델을 제시하였다. 기존 재배치 모델들의 속도 느림, 정확도 낮음 등 한계점을 개선하기 위해서, 본 논문에서 GA와 ACO 알고리즘을 조합돼서 GAACO-BSP(a Genetic Hybrid Ant Colony Optimization Algorithm for Solving Bike-sharing Scheduling Problem) 알고리즘을 개발하였다. 그리고 성능 향상시키기 위하여 GA 수행횟수 제어 함수를 수립하여 두 알고리즘을 동적으로 연결하였다. 우선 GA가 스케줄링 가능한 초기해를 구하고, 그 다음으로 GA 수행횟수 제어 함수를 통해 최적 전환 시기를 파악해서 동적으로 ACO으로 전환한다. ACO가 GA에게서 초기화 필요한 페로몬을 얻고 최종 최적해를 찾는 것이다. 서울시 공공자전거 따릉이 사례로 결과를 검증하여, GAACO-BSP은 전통 단일 알고리즘보다 뛰어난 성능 우세로 대규모 자전거 시스템에 적용하고 더 짧은 시간 만에 재배치 거리를 더 많이 줄였다. 실험을 통해 GAACO-BSP가 실제 도시 공공자전거 시스템에서 적용할 수 있다는 것을 알 수 있다.To improve the service efficiency and customer satisfaction degree of public bicycle, a bike-sharing scheduling model is proposed, which aims to get the shortest length of the bicycle scheduling. To address the slow solution speed of the existing algorithms, which is not conducive to real-time scheduling optimization, this paper designed a Genetic Hybrid Ant Colony System Algorithm for Solving Bike-sharing Scheduling Problem (GAACS-BSP). Genetic algorithm was used to search initial feasible scheme， which was used to initialize pheromone distribution of ant colony algorithm. It solved problem of lack initial pheromone, to improve the efficiency of bike-sharing scheduling tasks. There also proposed a genetic algorithm control function to control the appropriate combination opportunity of the two algorithms. Finally, the results show that compared with GA or ACS, it is more suitable for solving the problem of large-scale bike-sharing scheduling tasks, which shortens the scheduling distance in a short period.제 1 장 서 론 1 1.1. 연구의 배경 1 1.2. 연구의 내용 2 제 2 장 선행 연구 3 2.1. 기존 공공자전거 재배치에 관한 연구 3 2.2. 기존 GA-ACO 융합 알고리즘 5 제 3 장 모델 구축 방법론 8 3.1. BSP 문제의 수학적 해석 8 3.2. BSP 해결을 위한 GAACO-BSP 11 3.2.1. 기본 생각 11 3.2.2. 전체 프레임워크 11 제 4 장 GAACO-BSP 알고리즘 13 4.1. GA 부분의 규칙 14 4.1.1. 인코딩 방식 및 초기화 14 4.1.2. 선택 15 4.1.3. 교차 및 변이 15 4.1.4. 정지 조건 및 전환 16 4.2. ACO 부분의 규칙 17 4.2.1. ACO 초기화 17 4.2.2. 경로 선택 규칙 18 4.2.3. Pheromone 농도 조절 18 4.3. 알고리즘 흐름도 20 제 5 장 실험 및 결과 21 5.1. 데이터 전처리 21 5.2. 지역센터(배송팀) 재구분 26 5.3. 재배치 전략방안 도출 29 5.3.1. 수요현황 분석 29 5.3.2. 재배치 최적화 방안 도출 32 제 6 장 결 론 38 참고 문헌 41석

Crowdsensing-driven route optimisation algorithms for smart urban mobility

Urban rörlighet anses ofta vara en av de främsta möjliggörarna för en hållbar statsutveckling. Idag skulle det dock kräva ett betydande skifte mot renare och effektivare stadstransporter vilket skulle stödja ökad social och ekonomisk koncentration av resurser i städerna. En viktig prioritet för städer runt om i världen är att stödja medborgarnas rörlighet inom stadsmiljöer medan samtidigt minska trafikstockningar, olyckor och föroreningar. Att utveckla en effektivare och grönare (eller med ett ord; smartare) stadsrörlighet är en av de svåraste problemen att bemöta för stora metropoler. I denna avhandling närmar vi oss problemet från det snabba utvecklingsperspektivet av ITlandskapet i städer vilket möjliggör byggandet av rörlighetslösningar utan stora stora investeringar eller sofistikerad sensortenkik. I synnerhet föreslår vi utnyttjandet av den mobila rörlighetsavkännings, eng. Mobile Crowdsensing (MCS), paradigmen i vilken befolkningen exploaterar sin mobilkommunikation och/eller mobilasensorer med syftet att frivilligt samla, distribuera, lokalt processera och analysera geospecifik information. Rörlighetavkänningssdata (t.ex. händelser, trafikintensitet, buller och luftföroreningar etc.) inhämtad från frivilliga i befolkningen kan ge värdefull information om aktuella rörelsesförhållanden i stad vilka, med adekvata databehandlingsalgoriter, kan användas för att planera människors rörelseflöden inom stadsmiljön. Såtillvida kombineras i denna avhandling två mycket lovande smarta rörlighetsmöjliggörare, eng. Smart Mobility Enablers, nämligen MCS och rese/ruttplanering. Vi kan därmed till viss utsträckning sammanföra forskningsutmaningar från dessa två delar. Vi väljer att separera våra forskningsmål i två delar, dvs forskningssteg: (1) arkitektoniska utmaningar vid design av MCS-system och (2) algoritmiska utmaningar för tillämpningar av MCS-driven ruttplanering. Vi ämnar att visa en logisk forskningsprogression över tiden, med avstamp i mänskligt dirigerade rörelseavkänningssystem som MCS och ett avslut i automatiserade ruttoptimeringsalgoritmer skräddarsydda för specifika MCS-applikationer. Även om vi förlitar oss på heuristiska lösningar och algoritmer för NP-svåra ruttproblem förlitar vi oss på äkta applikationer med syftet att visa på fördelarna med algoritm- och infrastrukturförslagen.La movilidad urbana es considerada una de las principales desencadenantes de un desarrollo urbano sostenible. Sin embargo, hoy en día se requiere una transición hacia un transporte urbano más limpio y más eficiente que soporte una concentración de recursos sociales y económicos cada vez mayor en las ciudades. Una de las principales prioridades para las ciudades de todo el mundo es facilitar la movilidad de los ciudadanos dentro de los entornos urbanos, al mismo tiempo que se reduce la congestión, los accidentes y la contaminación. Sin embargo, desarrollar una movilidad urbana más eficiente y más verde (o en una palabra, más inteligente) es uno de los temas más difíciles de afrontar para las grandes áreas metropolitanas. En esta tesis, abordamos este problema desde la perspectiva de un panorama TIC en rápida evolución que nos permite construir movilidad sin la necesidad de grandes inversiones ni sofisticadas tecnologías de sensores. En particular, proponemos aprovechar el paradigma Mobile Crowdsensing (MCS) en el que los ciudadanos utilizan sus teléfonos móviles y dispositivos, para nosotros recopilar, procesar y analizar localmente información georreferenciada, distribuida voluntariamente. Los datos de movilidad recopilados de ciudadanos que voluntariamente quieren compartirlos (por ejemplo, eventos, intensidad del tráfico, ruido y contaminación del aire, etc.) pueden proporcionar información valiosa sobre las condiciones de movilidad actuales en la ciudad, que con el algoritmo de procesamiento de datos adecuado, pueden utilizarse para enrutar y gestionar el flujo de gente en entornos urbanos. Por lo tanto, en esta tesis combinamos dos prometedoras fuentes de movilidad inteligente: MCS y la planificación de viajes/rutas, uniendo en cierta medida los distintos desafíos de investigación. Hemos dividido nuestros objetivos de investigación en dos etapas: (1) Desafíos arquitectónicos en el diseño de sistemas MCS y (2) Desafíos algorítmicos en la planificación de rutas aprovechando la información del MCS. Nuestro objetivo es demostrar una progresión lógica de la investigación a lo largo del tiempo, comenzando desde los fundamentos de los sistemas de detección centrados en personas, como el MCS, hasta los algoritmos de optimización de rutas diseñados específicamente para la aplicación de estos. Si bien nos centramos en algoritmos y heurísticas para resolver problemas de enrutamiento de clase NP-hard, utilizamos ejemplos de aplicaciones en el mundo real para mostrar las ventajas de los algoritmos e infraestructuras propuestas

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Graph-based Algorithms for Smart Mobility Planning and Large-scale Network Discovery

Graph theory has become a hot topic in the past two decades as evidenced by the increasing number of citations in research. Its applications are found in many fields, e.g. database, clustering, routing, etc. In this thesis, two novel graph-based algorithms are presented. The first algorithm finds itself in the thriving carsharing service, while the second algorithm is about large graph discovery to unearth the unknown graph before any analyses can be performed. In the first scenario, the automatisation of the fleet planning process in carsharing is proposed. The proposed work enhances the accuracy of the planning to the next level by taking an advantage of the open data movement such as street networks, building footprints, and demographic data. By using the street network (based on graph), it solves the questionable aspect in many previous works, feasibility as they tended to use rasterisation to simplify the map, but that comes with the price of accuracy and feasibility. A benchmark suite for further research in this problem is also provided. Along with it, two optimisation models with different sets of objectives and contexts are proposed. Through a series of experiment, a novel hybrid metaheuristic algorithm is proposed. The algorithm is called NGAP, which is based on Reference Point based Non-dominated Sorting genetic Algorithm (NSGA-III) and Pareto Local Search (PLS) and a novel problem specific local search operator designed for the fleet placement problem in carsharing called Extensible Neighbourhood Search (ENS). The designed local search operator exploits the graph structure of the street network and utilises the local knowledge to improve the exploration capability. The results show that the proposed hybrid algorithm outperforms the original NSGA-III in convergence under the same execution time. The work in smart mobility is done on city scale graphs which are considered to be medium size. However, the scale of the graphs in other fields in the real-world can be much larger than that which is why the large graph discovery algorithm is proposed as the second algorithm. To elaborate on the definition of large, some examples are required. The internet graph has over 30 billion nodes. Another one is a human brain network contains around 1011 nodes. Apart of the size, there is another aspect in real-world graph and that is the unknown. With the dynamic nature of the real-world graphs, it is almost impossible to have a complete knowledge of the graph to perform an analysis that is why graph traversal is crucial as the preparation process. I propose a novel memoryless chaos-based graph traversal algorithm called Chaotic Traversal (CHAT). CHAT is the first graph traversal algorithm that utilises the chaotic attractor directly. An experiment with two well-known chaotic attractors, Lozi map and Rössler system is conducted. The proposed algorithm is compared against the memoryless state-of-the-art algorithm, Random Walk. The results demonstrate the superior performance in coverage rate over Random Walk on five tested topologies; ring, small world, random, grid and power-law. In summary, the contribution of this research is twofold. Firstly, it contributes to the research society by introducing new study problems and novel approaches to propel the advance of the current state-of-the-art. And Secondly, it demonstrates a strong case for the conversion of research to the industrial sector to solve a real-world problem