Search results for "Discrete event simulation"

showing 4 items of 14 documents

Pricing Policies of Excess Capacity in Make to Order Production Systems

2014

The research concerns a make to order manufacturing environment and two classes of customers who submit orders. The core customers have a contract with the firm at fixed services level (price and due date). The short-term customers submit the orders based on the price set by the firm. In this paper, it is proposed a pricing policy based on fuzzy logic to set the price for the short-term customers. The fuzzy approach captures the state of the manufacturing systems in terms of congestion in order to set the price. The policy proposed is compared to a fixed price, and to a state-dependent policy based on three levels of price. A discrete event simulation environment is used to test the propose…

Operations researchOrder (exchange)Build to orderFixed priceProduction (economics)Capacity utilizationBusinessDiscrete event simulationMarketingSettore ING-IND/35 - Ingegneria Economico-GestionaleFuzzy logicexcess of capacity make to order fuzzy logic discrete event simulationLimit price
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Does ns2 Really Simulate MPLS Networks?

2008

Multi Protocol Label Switching (MPLS) is being used in many corporate networks and public infrastructures and as a backbone technology of many Autonomous Systems. Because of its importance, what is needed is to find out simulators able to simulate MPLS networks whose results reflect the real environment as much as possible. In this article, we will show real measurements from the Railway Infrastructure Administrator (ADIF, the Spanish railway infrastructure manager) MPLS network and we will compare them with the results obtained by the freeware simulator ns2. We will check the level of reliability provided by the simulator and we will know under which parameters its results will be similar …

computer.internet_protocolbusiness.industryComputer scienceReliability (computer networking)ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSMultiprotocol Label SwitchingDiscrete event simulationbusinesscomputerComputer networkFourth International Conference on Autonomic and Autonomous Systems (ICAS'08)
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Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system

2012

Published version of an article in the journal: The International Journal of Advanced Manufacturing Technology. Also available from the publisher at: http://dx.doi.org/10.1007/s00170-012-4057-8 Nowadays, material planning and control strategies are becoming continuously complex tasks spanning from individual plants to logistic networks. In fact, this is the consequence of increasing intricacy in product variants and their respective convolution in networks’ structures. Customers ask for specific products with individual characteristics that force companies for more clever performances by more flexibility. For doing so, the existing planning and control systems, which work based on central m…

fuzzy systemVDP::Mathematics and natural science: 400::Mathematics: 410::Applied mathematics: 413Flexibility (engineering)CONWIPEngineeringautonomous controlMaterial requirements planningbusiness.industryMechanical EngineeringVDP::Technology: 500Fuzzy control systemlearning palletsDecentralised systemFuzzy logicIndustrial engineeringIndustrial and Manufacturing EngineeringComputer Science ApplicationsControl and Systems EngineeringControl systemArtificial intelligenceDiscrete event simulationbusinessConwipSoftwareThe International Journal of Advanced Manufacturing Technology
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Developing and testing a discrete event simulation model to evaluate budget impacts of diabetes prevention programs

2020

Type 2 diabetes (T2D) is one of the most rapidly increasing non-communicable diseases worldwide. Lifestyle interventions are effective in preventing T2D but also resource intensive. This study evaluated with discrete event simulation (DES) the relative budget impacts of three hypothetical diabetes prevention programs (DPP), including group-based contact intervention, digital program with human coaching and fully automated program. The data for simulation were derived from research literature and national health and population statistics. The model was constructed using the iGrafx Process for Six Sigma software and simulations were carried out for 10 years. All simulated interventions produc…

mallintaminenProcess (engineering)Computer scienceCost-Benefit AnalysisPsychological interventiondiabetes preventionHealth InformaticsCoaching03 medical and health sciences0302 clinical medicineResource (project management)Cost SavingsIntervention (counseling)Humansbudget impact analysissimulointiComputer Simulation030212 general & internal medicineDiscrete event simulationLife Style030304 developmental biologydiscrete event simulation0303 health sciencesdiabetesbusiness.industryBehavior changeSix SigmaComputer Science Applicationshealth behavior changeDiabetes Mellitus Type 2Risk analysis (engineering)terveyskäyttäytymineninterventiohoitoennaltaehkäisybusinessaikuistyypin diabetesJournal of Biomedical Informatics
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