Technology related logistic trends and technology usage in humanitarian logistics

Onan, Kıvanç (Dogus Author)Humanitarian logistics (HL) is an emerging field of research since the frequency and severity of disasters and the number of people in need is continuously increasing. And because of this fact proper planning is getting more crucial. Efficiency is another important issue when it comes to disaster relief operations. Keeping these operations efficient is the key to successful implementation and therefore technology usage is essential. In this study, a framework is proposed to benefit from the technology and volunteers for HL. This study emphasizes the role of technology and people for more efficient humanitarian logistics

A Tandem Drone-ground Vehicle for Accessing Isolated Locations for First Aid Emergency Response in Case of Disaster

The collapse of infrastructures is very often a complicating factor for the early emergency actuations after a disaster. A proper plan to better cover the needs of the affected people within the disaster area while maintaining life-saving relief operations is mandatory hence. In this paper, we use a drone for flying over a set of difficult-to-access locations for imaging issues to get information to build a risk assessment as the earliest stage of the emergency operations. While the drone provides the flexibility required to visit subsequently a sort of isolated locations, it needs a commando vehicle in ground for (i) monitoring the deployment of operations and (ii) being a recharging station where the drone gets fresh batteries. This work proposes a decision-making process to plan the mission, which is composed by the ground vehicle stopping points and the sequence of locations visited for each drone route. We propose a Genetic Algorithm (GA) which has proven to be helpful in finding good solutions in short computing times. We provide experimental analysis on the factors effecting the performance of the output solutions, around an illustrative test instance. Results show the applicability of these techniques for providing proper solutions to the studied problem

Cyber-Physical Systems Can Make Emergency Response Smart

This paper from the Humanitarian Technology: Science, Systems and Global Impact 2015 conference proceedings discusses the Smart Emergency Response System prototype built in the context of the SmartAmerica Challenge 2013-2014 by a team of nine organizations led by MathWorks

An assessment on the use of stationary vehicles to support cooperative positioning systems

In this paper, we evaluate the ability of stationary vehicles (e.g. parked or temporary stopped cars) as tools to enhance the capabilities of existing cooperative positioning algorithms in vehicular networks. First, some real-world facts are provided to support the feasibility of our ideas. Then, we examine the idea in greater details in terms of the technical requirements and methodological analysis, and provide a comprehensive experimental evaluation using dedicated simulations. The routing of a drone through an urban scenario is presented as a non-traditional application case, where the benefits of the proposed approach are reflected in a better utilisation of the flight time

Separation of distributed coordination and control for programming reliable robotics

A robot's code needs to sense the environment, control the hardware, and communicate with other robots. Current programming languages do not provide the necessary hardware platform-independent abstractions, and therefore, developing robot applications require detailed knowledge of signal processing, control, path planning, network protocols, and various platform-specific details. Further, porting applications across hardware platforms becomes tedious. With the aim of separating these hardware dependent and independent concerns, we have developed Koord: a domain specific language for distributed robotics. Koord abstracts platform-specific functions for sensing, communication, and low-level control. Koord makes the platform-independent control and coordination code portable and modularly verifiable. It raises the level of abstraction in programming by providing distributed shared memory for coordination and port interfaces for sensing and control. We have developed the formal executable semantics of Koord in the K framework. With this symbolic execution engine, we can identify proof obligations for gaining high assurance from Koord applications. Koord is deployed on CyPhyHouse---a toolchain that aims to provide programming, debugging, and deployment benefits for distributed mobile robotic applications. The modular, platform-independent middleware of CyPhyHouse implements these functionalities using standard algorithms for path planning (RRT), control (MPC), mutual exclusion, etc. A high-fidelity, scalable, multi-threaded simulator for Koord applications is developed to simulate the same application code for dozens of heterogeneous agents. The same compiled code can also be deployed on heterogeneous mobile platforms. This thesis outlines the design, implementation and formalization of the Koord language and the main components of CyPhyHouse that it is deployed on

Participation of distributed loads in power markets that co-optimize energy and reserves

Thesis (Ph.D.)--Boston UniversityAs the integration of Renewable Generation into today's Power Systems is progressing rapidly, capacity reserve requirements needed to compensate for the intermittency of renewable generation is increasing equally rapidly. A major objective of this thesis is to promote the affordability of incremental reserves by enabling loads to provide them through demand response. Regulation Service (RS) reserves, a critical type of bi-directional Capacity Reserves, are provided today by expensive and environmentally unfriendly centralized fossil fuel generators. In contrast, we investigate the provision of low-cost RS reserves by the demand-side. This is a challenging undertaking since loads must first promise reserves in the Hour Ahead Markets, and then be capable of responding to the dynamic ISO signals by adjusting their consumption effectively and efficiently. To this end, we use Stochastic Control, Optimization Theory, and Approximate Dynamic Programming to develop a decision support framework that assists Smart Neighborhood Operators or Smart Building Operators (SNOs/SBOs) to become demand-side-providers of RS reserve. We first address the SNO/SBO short time scale operational task of responding to the Independent System Operator's (ISO) dynamic RS requests. We start by developing a model-based Markovian decision problem that trades off ISO RS tracking against demand response related utility loss. Starting with a model based approach we obtain near optimal operational policies through a novel approximate policy iteration technique and an actor critic approach which is robust to partial knowledge of the underlying system dynamics. We then abandon the model based terrain and solve the dynamic operational problem through reinforcement learning that is capable of modeling a population of duty cycle appliances with realistic thermodynamics. We finally propose a smart thermostat design and develop an adaptive control policy that can drive the smart thermostat effectively. The latter approach is particularly suited for systems whose dynamics and dynamically changing consumer preferences are not known or observed beyond the total power consumption. We then address the SNO/SBO task of bidding RS reserves to the hour ahead market. This task determines the maximal RS reserves that the SNO/SBO can promise based on information available at the beginning of an hour, so as to maximize the associated hour-ahead revenues minus the expected average operating cost that will be incurred during the operational task to follow. To accomplish this task, we (i) develop probabilistic constraints that model the feasible maximum reserves which can be offered to the market without exceeding the SNO/SBO's ability to later track the unanticipated dynamic ISO RS signal, and (ii) calibrate a describing function that approximates the average operational cost as a function of the maximal reserves that can be feasibly offered in the day ahead market. The above is made possible by statistical analysis of the controlled system's stochastic dynamics and properties of the optimal dynamic policies that we derive. The contribution of the thesis is twofold: The solution of a difficult stochastic control problem that is crucial for effective demand-response-based provision of regulation service, and, the characterization of key properties of the stochastic control problem solution, which allow its integration into the hour-ahead market bidding problem