Mobile Edge Computing

Mobile applications are becoming increasingly computational-intensive while many mobile devices still have limited battery power and cannot support computational intensive tasks Mobile edge computing MEC computing is an extension of edge computing and it refers to computing at the edge of a network In mobile edge computing computing and storage nodes are placed at the Internet s edge near mobile devices It places the edge clouds at the candidate locations This paper presents a brief introduction to ME

Operational research and simulation methods for autonomous ride-sourcing

Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems. The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method. The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage. The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces

Optimization Approaches for Mobility and Service Sharing

Mobility and service sharing is undergoing a fast rise in popularity and industrial growth in recent years. For example, in patient-centered medical home care, services are delivered to patients at home, who share a group of medical staff riding together in a vehicle that also carries shared medical devices; companies such as Amazon and Meijer have been investing tremendous human effort and money in grocery delivery to customers who share the use of delivery vehicles and staff. In such mobility and service sharing systems, decision-makers need to make a wide range of system design and operational decisions, including locating service facilities, matching supplies with demand for shared mobility services, dispatching vehicles and staff, and scheduling appointments. The complexity of the linking decisions and constraints, as well as the dimensionality of the problems in the real world, pose challenges in finding optimal strategies efficiently. In this work, we apply techniques from Operations Research to investigate the optimal and practical solution approaches to improve the quality of service, cost-effectiveness, and operational efficiency of mobility and service sharing in a variety of applications. We deploy stochastic programming, integer programming, and approximation algorithms to address the issues in decision-making for seeking fast and reliable solutions for planning and operations problems. This dissertation contains four main chapters. In Chapter 2, we consider a class of vehicle routing problems (VRPs) where the objective is to minimize the longest route taken by any vehicle as opposed to the total distance of all routes. In such a setting, the traditional decomposition approach fails to solve the problem effectively. We investigate the hardness result of the problem and develop an approximation algorithm that achieves the best approximation ratio. In Chapter 3, we focus on developing an efficient computational algorithm for the elementary shortest path problem with resource constraints, which is solved as the pricing subproblem of the column generation-based approach for many VRP variants. Inspired by the color-coding approach, we develop a randomized algorithm that can be easily implemented in parallel. We also extend the state-of-the-art pulse algorithm for elementary shortest path problem with a new bounding scheme on the load of the route. In Chapter 4, we consider a carsharing fleet location design problem with mixed vehicle types and a restriction on CO2 emission. We use a minimum-cost flow model on a spatial-temporal network and provide insights on fleet location, car-type design, and their environmental impacts. In Chapter 5, we focus on the design and operations of an integrated car-and-ride sharing system for heterogeneous users/travelers with an application of satisfying transportation needs in underserved communities. The system aims to provide self-sustained community-based shared transportation. We address the uncertain travel and service time in operations via a stochastic integer programming model and propose decomposition algorithms to solve it efficiently. Overall, our contributions are threefold: (i) providing mathematical models of various complex mobility and service sharing systems, (ii) deriving efficient solution algorithms to solve the proposed models, (iii) evaluating the solution approaches via extensive numerical experiments. The models and solution algorithms that we develop in this work can be used by practitioners to solve a variety of mobility and service sharing problems in different business contexts, and thus can generate significant societal and economic impacts.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155115/1/miaoyu_1.pd

MODELING AND ANALYSIS OF AN AUTONOMOUS MOBILITY ON DEMAND SYSTEM

Ph.DDOCTOR OF PHILOSOPH

Applications of the Internet of Things and optimization to inventory and distribution management

This thesis is part of the IoFEED (EU funded) project, which aims to monitor approximately 325 farm bins and investigates business processes carried out between farmers and animal feed producers. We propose a computer-aided system to control and optimize the supply chain to deliver animal feed to livestock farms. Orders can be of multiple types of feed, shipped from multiple depots using a fleet of heterogeneous vehicles with multiple compartments. Additionally, this case considers some business-specific constraints, such as product compatibility, facility accessibility restrictions, prioritized locations, or bio-security constraints. A digital twin based approach is implemented at the farm level by installing sensors to remotely measure the inventories. This thesis also embraces these sensors' design and manufacturing process, seeking the required precision and easy deployability at scale. Our approach combines biased-randomization techniques with a simheuristic framework to make use of data provided by the sensors. The analysis of results is based on these two real pilots, and showcases the insights obtained during the IoFEED project. The results of this thesis show how the Internet of Things and simulation-based optimization methods combine successfully to optimize deliveries of feed to livestock farms.Esta tesis forma parte del proyecto IoFeeD, financiado por la Unión Europea, que tiene como objetivo monitorizar remotamente el stock de 325 contenedores agrícolas e investigar los procesos comerciales llevados a cabo entre agricultores y productores de pienso. Proponemos un sistema de ayuda a la toma de decisiones para controlar y optimizar la cadena de suministro de pienso en las explotaciones ganaderas. Los pedidos pueden ser de varios tipos de pienso y pueden enviarse desde varios centros de fabricación mediante el uso de una flota de vehículos heterogéneos con varios compartimentos. Además, se tienen en cuenta algunas restricciones específicas de la empresa, como, por ejemplo, la compatibilidad del producto, las restricciones de accesibilidad en las instalaciones, las ubicaciones priorizadas o las restricciones de bioseguridad. A escala de granja, se implementa un enfoque basado en gemelos digitales mediante la instalación de sensores para medir los inventarios de forma remota. En el marco de esta tesis, se desarrollan estos sensores buscando la precisión requerida, así como las características oportunas que permitan su instalación a gran escala. Nuestro enfoque combina técnicas de aleatorización sesgada con un marco simheurístico para hacer uso de los datos proporcionados por los sensores. El análisis de los resultados se basa en estos dos pilotos reales y muestra las ideas obtenidas durante el proyecto IoFeeD. Los resultados de esta tesis muestran cómo la internet de las cosas y los métodos de optimización basados en simulación se combinan con éxito para optimizar las operaciones de suministro de pienso para el consumo animal en las explotaciones ganaderas.Aquesta tesi forma part del projecte IoFeeD, finançat per la Unió Europea, que té com a objectiu controlar remotament l'estoc de 325 sitges i investigar els processos de negoci duts a terme entre agricultors i productors de pinso. Proposem un sistema d'ajuda a la presa de decisions per controlar i optimitzar la cadena de subministrament de pinso a les explotacions ramaderes. Les comandes poden ser de diversos tipus de pinso i es poden enviar des de diversos centres de fabricació mitjançant l'ús d'una flota de vehicles heterogenis amb diversos compartiments. A més, es tenen en compte algunes restriccions específiques de l'empresa, com ara la compatibilitat del producte, les restriccions d'accessibilitat a les instal·lacions, les ubicacions prioritzades o les restriccions de bioseguretat. A escala de granja, s'implementa un enfocament basat en bessons digitals mitjançant la instal·lació de sensors per mesurar remotament els inventaris. En el marc de la tesi, es desenvolupa aquest sensor cercant la precisió requerida i les característiques oportunes que en permetin la instal·lació a gran escala. El nostre enfocament combina tècniques d'aleatorització esbiaixada amb un marc simheurístic per fer ús de les dades proporcionades pels sensors. L'anàlisi dels resultats es basa en aquests dos pilots reals i mostra les idees obtingudes durant el projecte IoFeeD. Els resultats d'aquesta tesi mostren com la internet de les coses i els mètodes d'optimització basats en simulació es combinen amb èxit per optimitzar les operacions de subministrament de pinso per al consum animal a les explotacions ramaderes.Tecnologies de la informació i de xarxe

Mathematical Optimization for Routing and Logistic Problems

In this thesis, we focus on mathematical optimization models and algorithms for solving routing and logistic problems. The first contribution regards a path and mission planning problem, called Carrier-Vehicle Traveling Salesman Problem (CVTSP), for a system of heterogeneous vehicles. A Mixed-Integer Second Order Conic Programming (MISOCP) model and a Benders-like enumeration algorithm are presented for solving CVTSP. The second work concerns a class of routing problems, referred to as Interceptor Vehicle Routing Problems (IVRPs). They generalize VRPs in the sense that target points are allowed to move from their initial location according to a known motion. We present a novel MISOCP formulation and a Branch-and-Price algorithm based on a Lagrangian Relaxation of the vehicle-assignment constraints. Other two contributions focus on waste flow management problems: the former considers a deterministic setting in which a Mixed-Integer Linear Programming (MILP) formulation is used as a Decision Support System for a real-world waste operator, whereas the latter deals with the uncertainty of the waste generation amounts by means of Two-Stage Multiperiod Stochastic Mixed-Integer Programming formulations. Finally, we give an overview on the optimization challenges arising in electric car-sharing systems, both at strategic and tactical planning level