599 research outputs found

    Primjena ERP sustava za poboljšanje koordinacije internog dobavljačkog lanca

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    In today\u27s competitive global market, one of the most effective ways towards achieving competitive advantage has been the ability to accelerate the supply chain process through ERP (Enterprise Resource Planning) systems. ERP enables a more efficient internal and external supply chain. Enterprise resource planning system is an information system that manages all aspects of a business (production planning, sales, distribution, accounting, purchasing and customer services). Planning system is the core of an ERP system. The aim of this paper is to propose a hierarchical planning and scheduling model based on just-in-time principle to improve internal supply chain coordination for one-piece and small batch production. The model is implemented into the system ERPINS (Enterprise Resource Planning ININ Solutions) that is developed for metal processing industry, wood and food processing industry and construction industry.Na današnjem konkurentnom globalnom tržištu jedan od najučinkovitijih načina postizanja konkurentne prednosti je sposobnost ubrzanja procesa dobavljačkog lanca pomoću ERP (Enterprise Resource Planning) sustava koji omogućava učinkovitiji interni i eksterni dobavljački lanac. Enterprise resource planning sustav je informacijski sustav koji upravlja svim aspektima poslovanja (planiranje proizvodnje, prodaja, distribucija, računovodstvo, nabava i korisničke usluge). Sustav planiranja je glavni dio ERP sustava. Cilj ovog rada je dati model višerazinskog planiranja i terminiranja koji je zasnovan na just-in-time principu sa svrhom poboljšane koordinacije internog dobavljačkog lanca za pojedinačnu i maloserijsku proizvodnju. Model je primijenjen u sustavu ERPINS (Enterprise Resource Planning ININ Solutions) razvijenom za metaloprerađivačku, drvnu, prehrambenu i građevinsku industriju

    STRIPS Action Discovery

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    The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.Comment: Presented to Genplan 2020 workshop, held in the AAAI 2020 conference (https://sites.google.com/view/genplan20) (2021/03/05: included missing acknowledgments

    SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

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    The smart grid, by the integration of advanced control and optimization technologies, provides the traditional grid with an indisputable opportunity to deliver and utilize the electricity more efficiently. Building smart grid applications is a challenging task, which requires a formal modeling, integration, and validation framework for various smart grid domains. The design flow of such applications must adapt to the grid requirements and ensure the security of supply and demand. This dissertation, by proposing a formal framework for customers and operations domains in the smart grid, aims at delivering a smooth way for: i) formalizing their interactions and functionalities, ii) upgrading their components independently, and iii) evaluating their performance quantitatively and qualitatively.The framework follows an event-driven demand response program taking no historical data and forecasting service into account. A scalable neighborhood of prosumers (inside the customers domain), which are equipped with smart appliances, photovoltaics, and battery energy storage systems, are considered. They individually schedule their appliances and sell/purchase their surplus/demand to/from the grid with the purposes of maximizing their comfort and profit at each instant of time. To orchestrate such trade relations, a bilateral multi-issue negotiation approach between a virtual power plant (on behalf of prosumers) and an aggregator (inside the operations domain) in a non-cooperative environment is employed. The aggregator, with the objectives of maximizing its profit and minimizing the grid purchase, intends to match prosumers' supply with demand. As a result, this framework particularly addresses the challenges of: i) scalable and hierarchical load demand scheduling, and ii) the match between the large penetration of renewable energy sources being produced and consumed. It is comprised of two generic multi-objective mixed integer nonlinear programming models for prosumers and the aggregator. These models support different scheduling mechanisms and electricity consumption threshold policies.The effectiveness of the framework is evaluated through various case studies based on economic and environmental assessment metrics. An interactive web service for the framework has also been developed and demonstrated

    A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

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    As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

    Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem

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    Abstract Short term probabilistic load forecasting is essential for any power generating utility. This paper discusses an application of partially linear additive quantile regression models for predicting short term electricity demand during the peak demand hours (i.e. from 18:00 to 20:00) using South African data for January 2009 to June 2012. Additionally the bounded variable mixed integer linear programming technique is used on the forecasts obtained in order to find an optimal number of units to commit (switch on or off. Variable selection is done using the least absolute shrinkage and selection operator. Results from the unit commitment problem show that it is very costly to use gas fired generating units. These were not selected as part of the optimal solution. It is shown that the optimal solutions based on median forecasts ( Q 0.5 quantile forecasts) are the same as those from the 99th quantile forecasts except for generating unit g 8 c , which is a coal fired unit. This shows that for any increase in demand above the median quantile forecasts it will be economical to increase the generation of electricity from generating unit g 8 c . The main contribution of this study is in the use of nonlinear trend variables and the combining of forecasting with the unit commitment problem. The study should be useful to system operators in power utility companies in the unit commitment scheduling and dispatching of electricity at a minimal cost particularly during the peak period when the grid is constrained due to increased demand for electricity

    An adaptive load sensing priority assignment protocol for distributed real-time database systems.

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    Transaction processing in a distributed real time database system (DRTDBS) is coordinated by a concurrency control protocol (CCP). The performance of a CCP is affected by the load condition of a transaction processing system. For example, the performance of the Adaptive Speculative Locking (ASL) protocol degrades in high load conditions of the system. Priority protocols help a CCP by prioritizing transactions. The performance of the priority protocols is also affected by system load conditions, but they can be optimized by dynamically switching between priority protocols at run time when the system load changes. The objective of this research is to develop a protocol, Adaptive Priority Assignment protocol (APAP), which changes the priority protocol at run time to improve the performance of a CCP in a DRTDBS. APAP is implemented in a DRTDBS, where ASL is used as the underlying CCP to validate APAP. The performance of APAP was tested under varying system load conditions with various combinations of the database system parameters. Under the scenarios tested, APAP performed better than other priority protocols and demonstrated that dynamic selection of priority protocols during run time is an effective way to improve the performance of a CCP in a DRTDBS. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b183575

    ACHIEVING AUTONOMIC SERVICE ORIENTED ARCHITECTURE USING CASE BASED REASONING

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    Service-Oriented Architecture (SOA) enables composition of large and complex computational units out of the available atomic services. However, implementation of SOA, for its dynamic nature, could bring about challenges in terms of service discovery, service interaction, service composition, robustness, etc. In the near future, SOA will often need to dynamically re-configuring and re-organizing its topologies of interactions between the web services because of some unpredictable events, such as crashes or network problems, which will cause service unavailability. Complexity and dynamism of the current and future global network system require service architecture that is capable of autonomously changing its structure and functionality to meet dynamic changes in the requirements and environment with little human intervention. This then needs to motivate the research described throughout this thesis. In this thesis, the idea of introducing autonomy and adapting case-based reasoning into SOA in order to extend the intelligence and capability of SOA is contributed and elaborated. It is conducted by proposing architecture of an autonomic SOA framework based on case-based reasoning and the architectural considerations of autonomic computing paradigm. It is then followed by developing and analyzing formal models of the proposed architecture using Petri Net. The framework is also tested and analyzed through case studies, simulation, and prototype development. The case studies show feasibility to employing case-based reasoning and autonomic computing into SOA domain and the simulation results show believability that it would increase the intelligence, capability, usability and robustness of SOA. It was shown that SOA can be improved to cope with dynamic environment and services unavailability by incorporating case-based reasoning and autonomic computing paradigm to monitor and analyze events and service requests, then to plan and execute the appropriate actions using the knowledge stored in knowledge database
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