From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Routing Applications in Newspaper Delivery

-The goal of this report is to give an up-to-date account of routing applications in the newspaper business. We describe the newspaper supply chain, and focus on the “last mile” distribution that has been advocated as an application of arc routing in the literature. A literature survey is provided, followed by a discussion of the arc routing model and its adequacy to newspaper applications. A more general and normally more adequate model: The Node, Edge, and Arc Routing Problem, is discussed. Characteristics of routing problems in carrier delivery are presented, together with a case study from the development of a web-based route design and revision system. Finally, summary, conclusions, and prospects for the future are given

Selective Trajectory Memory Network andits application in Vehicle DestinationPrediction

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. Cho, Sungzoon.Predicting efficiently the final destinations of moving vehicles can be of significant usefulness for several applications. Many probabilistic methods have been developed to address it but often include heavy feature engineering and do not generalize well to new datasets. To face these limitations, Deep-Learning models present the advantage of automating processing steps and can therefore be easily adapted to new input data. De Brébisson et al. proposed clustering based deep-learning approaches to solve it in the specific case of the prediction of Taxis destinations with remarkable performances, alongside with a proposition of a novel architecture inspired by Memory-Networks used in Natural Language Processing, and requiring no preliminary clustering. A large room for improvement was however left for the latter approach : the necessity of a relevant selection function retrieving historical trajectories similar to partial trips to predict was indeed outlined by the authors. In this work we propose to use the Segment-Path distance, introduced by Besse et al. in former works on trajectory clustering, to come up with an improved architecture of this memory model. A review of several Memory Networks architecture and their applications in time-series prediction is provided to give an overview of the different structural alternatives existing for the design of our model architecture. Finally, our model is confronted to individual car data and we propose a personalized user-by-user prediction of destinations. We discuss the suitability and limits of the type of model in this specific problem and conclude that the promising obtained results are penalized by infrequent destinations cases inducing noise whose effect could be reduced by turning our approach into a classification problem.Abstract i Contents List of Tables vi List of Figures viii Chapter 1 Introduction 1 1.1 Motivations, background . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Description : destination forecasting problem . . . . . . . . 2 1.2.1 General context . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.2 Specific problem tackled . . . . . . . . . . . . . . . . . . . . . 2 1.3 Existing models and methods . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Research Motivation and Contributions . . . . . . . . . . . . . . . . 6 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 2 Related works 8 2.1 Artificial neural network models for trajectory prediction . . . . . . 8 2.1.1 Encoding and clustering approach . . . . . . . . . . . . . . . 8 2.1.2 "Memory network" model for taxi trajectory prediction . . . 11 2.2 Memory networks and applications . . . . . . . . . . . . . . . . . . . 13 2.2.1 MemNN models . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 End-to-end memory networks (MemN2N) . . . . . . . . . . . 16 2.2.3 Memory networks for multi-dimensional time-series forecasting (MTNnet) . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Analogies and comparisons between the memory models introduced . 19 2.4 Distances measures for vehicle trajectories . . . . . . . . . . . . . . . 22 2.4.1 Segment-Path Distance (SPD) . . . . . . . . . . . . . . . . . 23 2.5 Personalized predictions on car manufacturer data . . . . . . . . . . 26 2.5.1 Problem approach and redefinition . . . . . . . . . . . . . . . 26 2.5.2 Method and model . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 3 Proposed Model 28 3.1 Overall architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Memory storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Trajectory encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Encoding architecture . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 Metadata and embedding . . . . . . . . . . . . . . . . . . . . 31 3.4.3 Distinctions between encoders, weight-sharing . . . . . . . . . 31 3.5 Memory selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5.1 Attention mechanism . . . . . . . . . . . . . . . . . . . . . . 32 3.5.2 Data used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.6 Query-memory association . . . . . . . . . . . . . . . . . . . . . . . . 33 3.7 Final prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Chapter 4 Experiments 35 4.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.1 Variability and predictability . . . . . . . . . . . . . . . . . . 36 4.2.2 Considered vehicles . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 Experimental settings . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.1 Training and testing set . . . . . . . . . . . . . . . . . . . . . 39 4.3.2 Test methodology and parameters . . . . . . . . . . . . . . . 40 4.3.3 Baseline model : simple encoding . . . . . . . . . . . . . . . . 42 4.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.1 General results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.2 Factors of influence on models performances . . . . . . . . . . 45 4.4.3 Case studies : 5 example vehicles analysis . . . . . . . . . . . 49 4.4.4 Baseline model . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Chapter 5 Conclusion 56 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Bibliography 58 감사의 글 62Maste

Transportation Life Cycle Assessment Synthesis: Life Cycle Assessment Learning Module Series

The Life Cycle Assessment Learning Module Series is a set of narrated, self-advancing slideshows on various topics related to environmental life cycle assessment (LCA). This research project produced the first 27 of such modules, which are freely available for download on the CESTiCC website http://cem.uaf.edu/cesticc/publications/lca.aspx. Each module is roughly 15- 20 minutes in length and is intended for various uses such as course components, as the main lecture material in a dedicated LCA course, or for independent learning in support of research projects. The series is organized into four overall topical areas, each of which contain a group of overview modules and a group of detailed modules. The A and α groups cover the international standards that define LCA. The B and β groups focus on environmental impact categories. The G and γ groups identify software tools for LCA and provide some tutorials for their use. The T and τ groups introduce topics of interest in the field of transportation LCA. This includes overviews of how LCA is frequently applied in that sector, literature reviews, specific considerations, and software tutorials. Future modules in this category will feature methodological developments and case studies specific to the transportation sector

Modelling public transport accessibility with Monte Carlo stochastic simulations: A case study of Ostrava

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin-destination (O-D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance-decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.Web of Science1124art. no. 709

Work-based and work-related learning in Higher National Certificates and Diplomas in Scotland and Foundation Degrees in England: a comparative study: final report

This final report draws on findings from the four stages of a comparative study of Higher National Certificates/Diplomas (HNC/Ds) in Scotland and Foundation Degrees (FDs) in England that was undertaken jointly by researchers in the Centre for Research in Lifelong Learning (CRLL) at Glasgow Caledonian University (GCU) and the Open University (OU). The overall study has examined and explored the following issues: the demand drivers and how far they differ in both countries; the differing policy and funding frameworks in place in Scotland and England; the different types of provision which have emerged and the roles of different stakeholders in shaping this provision; the consequences of these models for the experiences of the learners involved; and progression of students into further study or employment

Engage D3.10 Research and innovation insights

Engage is the SESAR 2020 Knowledge Transfer Network (KTN). It is managed by a consortium of academia and industry, with the support of the SESAR Joint Undertaking. This report highlights future research opportunities for ATM. The basic framework is structured around three research pillars. Each research pillar has a dedicated section in this report. SESAR’s Strategic Research and Innovation Agenda, Digital European Sky is a focal point of comparison. Much of the work is underpinned by the building and successful launch of the Engage wiki, which comprises an interactive research map, an ATM concepts roadmap and a research repository. Extensive lessons learned are presented. Detailed proposals for future research, plus research enablers and platforms are suggested for SESAR 3