660 research outputs found

    SGSC Framework: Smart Government in Supply Chain Based on FODA

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    Smart System has implemented in government sector. There are varies Implementation that was utilized by research activities for numerous domains is very broad. Besides that, the Industry, transportation and health, also where such a system is incredibly beneficial. This study discuss supply chain and governmental link issue, coordination of all stakeholder in supply chain has to reflect the government role. It support with the condition in Indonesian government environment is unique. It is a challenge to construct smart system based on Feature Oriented Domain Analysis (FODA) approach. It can produce software product line (SPL). We proposed framework for develop software product line for smart supply chain in government sector. It is used to enhance and improve the development of software systems by multiple software system developers. It will be a guidance for construct smart government, and more specificity in supply chain for government system area environment. It is called SGSC Framework. It consists of four layers, such as optimization layer, integration layer, supply chain layer and data layer

    Online Modified Greedy Algorithm for Storage Control under Uncertainty

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    This paper studies the general problem of operating energy storage under uncertainty. Two fundamental sources of uncertainty are considered, namely the uncertainty in the unexpected fluctuation of the net demand process and the uncertainty in the locational marginal prices. We propose a very simple algorithm termed Online Modified Greedy (OMG) algorithm for this problem. A stylized analysis for the algorithm is performed, which shows that comparing to the optimal cost of the corresponding stochastic control problem, the sub-optimality of OMG is bounded and approaches zero in various scenarios. This suggests that, albeit simple, OMG is guaranteed to have good performance in some cases; and in other cases, OMG together with the sub-optimality bound can be used to provide a lower bound for the optimal cost. Such a lower bound can be valuable in evaluating other heuristic algorithms. For the latter cases, a semidefinite program is derived to minimize the sub-optimality bound of OMG. Numerical experiments are conducted to verify our theoretical analysis and to demonstrate the use of the algorithm.Comment: 14 page version of a paper submitted to IEEE trans on Power System

    Improving supply chain delivery reliability.

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    Operational Research: Methods and Applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Developing service supply chains by using agent based simulation

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    The Master thesis present a novel approach to model a service supply chain with agent based simulation. Also, the case study of thesis is related to healthcare services and research problem includes facility location of healthcare centers in Vaasa region by considering the demand, resource units and service quality. Geographical information system is utilized for locating population, agent based simulation for patients and their illness status probability, and discrete event simulation for healthcare services modelling. Health centers are located on predefined sites based on managers’ preference, then each patient based on the distance to health centers, move to the nearest point for receiving the healthcare services. For evaluating cost and services condition, various key performance indicators have defined in the modelling such as Number of patient in queue, patients waiting time, resource utilization, and number of patients ratio yielded by different of inflow and outflow. Healthcare managers would be able to experiment different scenarios based on changing number of resource units or location of healthcare centers, and subsequently evaluate the results without necessity of implementation in real life.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

    Data Driven Synthetic Load Modeling for Smart City Energy Management Studies

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    The primary aim of this dissertation is to provide synthetic residential load models with granular level information on the customers having information about the appliances that constitute each individual residential customer through time. The synthetic load model is capable of being widely utilized by the power system research community since only publicly available data is utilized for its generation. This gives researcher’s access to how the synthetic load was made and also how accurate the model is in representing real power system regions. As the title of the dissertation suggests, the synthetic residential load models are intended for smart city energy management studies. Smart city energy management studies have the ability to control tens of thousands of electricity customers in a coordinated manner to enact system-wide electric load changes. Such load changes have the potential to reduce congestion (i.e. stress on power system components) and peak demand (i.e. the need for peaking generation), among other benefits. For smart city energy management studies to have the capability of evaluating how their strategies would impact the actual power system, datasets that accurately characterize the system load are required that also contain individual loads of all buildings in a given area. Currently, such data is publicly unavailable due to privacy concerns. This dissertation’s synthetic residential load model combines a top down and bottom up approach for modeling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in smart city energy management studies. The dissertation presents three queueing residential load models that make use of only publicly available data to alleviate privacy concerns. The proposed approach is mainly driven by the aggregated distribution companies load. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results comparing the three queueing synthetic load models consider the ComEd region (utility company from Chicago, IL) to demonstrate the model’s characteristics, impact of the choice of model parameters, and scalability performance of the Python tool. The developed residential synthetic queueing load models are utilized to create the Midwest 240-Node distribution test case system, which generates appliance-level synthetic residential load for 1,120 homes for the Iowa State distribution system test case with 193 load nodes over three feeders. The Midwest 240-Node is a real distribution system from the Midwest region of the U.S. with real one-year smart meter data at the hourly aggregated node level resolution for 2017 available in an OpenDSS model. The synthetic residential queueing load model generated for the Midwest 240-Node one-year date has a mean absolute percentage error of 2.5828% in relation to the real smart meter data. The Midwest 240-Node distribution system OpenDSS model was converted to GridLAB-D to enable smart grid and transactive energy studies. The percentage of maximum error observed on voltage magnitude from the OpenDSS to GridLAB-D model is below 0.0009%. The GridLAB-D model and the generated synthetic residential load is made publicly available. The Midwest 240-Node real distribution system with the synthetic residential load that follows the real data from smart meters is intended to be a distributed energy active consumer test system network. The focus of the developed synthetic residential load models is smart city energy management studies; however, they can be utilized in many power systems studies to evaluate economic and technical impacts of distributed energy resources. For example, this dissertation also presents the utilization of the synthetic models for a PV rich low voltage network. The main component of the smart grid is demand response. Demand response, or energy management, utilizes commonly passive load in to active power system resources. Residential demand response, when aggregated, is capable of performing system-wide changes that enable its participation in the power system markets. This dissertation developed residential synthetic models to enable the standardization of approaches and allow different approaches to be compared under the same environment

    Joint Online Thesis and Research System (JOTARS)

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    The purpose of this thesis is to develop a web-enabled database which facilitates research related connections and communication among Naval Postgraduate School (NPS) students, professors and DOD organizations. The proposed name for the prototype website is the Joint Online Thesis and Research System (JOTARS). The specific functional objectives of JOTARS are to establish standard infrastructure and processes that allow DOD organizations to dynamically propose research topics, view research in progress, and a means to suggest topics for class projects. JOTARS will also enable NPS students to conduct refined searches of proposed research topics.http://archive.org/details/jointonlinethesi109452544Approved for public release; distribution is unlimited

    Structuring postponement strategies in the supply chain by analytical modeling

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