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chain</title> <meta name="description" content="Search results for: Markov chain"> <meta name="keywords" content="Markov chain"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Markov chain"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2011</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Markov chain</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2011</span> Markov-Chain-Based Optimal Filtering and Smoothing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Garry%20A.%20Einicke">Garry A. Einicke</a>, <a href="https://publications.waset.org/abstracts/search?q=Langford%20B.%20White"> Langford B. White</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an optimum filter and smoother for recovering a Markov process message from noisy measurements. The developments follow from an equivalence between a state space model and a hidden Markov chain. The ensuing filter and smoother employ transition probability matrices and approximate probability distribution vectors. The properties of the optimum solutions are retained, namely, the estimates are unbiased and minimize the variance of the output estimation error, provided that the assumed parameter set are correct. Methods for estimating unknown parameters from noisy measurements are discussed. Signal recovery examples are described in which performance benefits are demonstrated at an increased calculation cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20filtering" title="optimal filtering">optimal filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=smoothing" title=" smoothing"> smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chains" title=" Markov chains"> Markov chains</a> </p> <a href="https://publications.waset.org/abstracts/20256/markov-chain-based-optimal-filtering-and-smoothing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20256.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2010</span> Bayesian Using Markov Chain Monte Carlo and Lindley's Approximation Based on Type-I Censored Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Al%20Omari%20Moahmmed%20Ahmed">Al Omari Moahmmed Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> These papers describe the Bayesian Estimator using Markov Chain Monte Carlo and Lindley’s approximation and the maximum likelihood estimation of the Weibull distribution with Type-I censored data. The maximum likelihood method can’t estimate the shape parameter in closed forms, although it can be solved by numerical methods. Moreover, the Bayesian estimates of the parameters, the survival and hazard functions cannot be solved analytically. Hence Markov Chain Monte Carlo method and Lindley’s approximation are used, where the full conditional distribution for the parameters of Weibull distribution are obtained via Gibbs sampling and Metropolis-Hastings algorithm (HM) followed by estimate the survival and hazard functions. The methods are compared to Maximum Likelihood counterparts and the comparisons are made with respect to the Mean Square Error (MSE) and absolute bias to determine the better method in scale and shape parameters, the survival and hazard functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weibull%20distribution" title="weibull distribution">weibull distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20method" title=" bayesian method"> bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain%20mote%20carlo" title=" markov chain mote carlo"> markov chain mote carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20and%20hazard%20functions" title=" survival and hazard functions"> survival and hazard functions</a> </p> <a href="https://publications.waset.org/abstracts/31291/bayesian-using-markov-chain-monte-carlo-and-lindleys-approximation-based-on-type-i-censored-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31291.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">479</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2009</span> Finite State Markov Chain Model of Pollutants from Service Stations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amina%20Boukelkoul">Amina Boukelkoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahil%20Boukelkoul"> Rahil Boukelkoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Maachia"> Leila Maachia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cumulative vapors emitted from the service stations may represent a hazard to the environment and the population. Besides fuel spill and their penetration into deep soil layers are the main contributors to soil and ground-water contamination in the vicinity of the petrol stations. The amount of the effluents from the service stations depends on strategy of maintenance and the policy adopted by the management to reduce the pollution. One key of the proposed approach is the idea of managing the effluents from the service stations which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating a probabilistic percentage of the amount of emitted pollutants is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the amount according to various options of operation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environment" title="environment">environment</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20modeling" title=" markov modeling"> markov modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=pollution" title=" pollution"> pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20station" title=" service station"> service station</a> </p> <a href="https://publications.waset.org/abstracts/35961/finite-state-markov-chain-model-of-pollutants-from-service-stations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35961.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">472</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2008</span> Mean Field Model Interaction for Computer and Communication Systems: Modeling and Analysis of Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irina%20A.%20Gudkova">Irina A. Gudkova</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousra%20Demigha"> Yousra Demigha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Scientific research is moving more and more towards the study of complex systems in several areas of economics, biology physics, and computer science. In this paper, we will work on complex systems in communication networks, Wireless Sensor Networks (WSN) that are considered as stochastic systems composed of interacting entities. The current advancements of the sensing in computing and communication systems is an investment ground for research in several tracks. A detailed presentation was made for the WSN, their use, modeling, different problems that can occur in their application and some solutions. The main goal of this work reintroduces the idea of mean field method since it is a powerful technique to solve this type of models especially systems that evolve according to a Continuous Time Markov Chain (CTMC). Modeling of a CTMC has been focused; we obtained a large system of interacting Continuous Time Markov Chain with population entities. The main idea was to work on one entity and replace the others with an average or effective interaction. In this context to make the solution easier, we consider a wireless sensor network as a multi-body problem and we reduce it to one body problem. The method was applied to a system of WSN modeled as a Markovian queue showing the results of the used technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Continuous-Time%20Markov%20Chain" title="Continuous-Time Markov Chain">Continuous-Time Markov Chain</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Chain" title=" Hidden Markov Chain"> Hidden Markov Chain</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20field%20method" title=" mean field method"> mean field method</a>, <a href="https://publications.waset.org/abstracts/search?q=Wireless%20sensor%20networks" title=" Wireless sensor networks"> Wireless sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/86965/mean-field-model-interaction-for-computer-and-communication-systems-modeling-and-analysis-of-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86965.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2007</span> Maintenance Alternatives Related to Costs of Wind Turbines Using Finite State Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukelkoul%20Lahcen">Boukelkoul Lahcen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cumulative costs for O&M may represent as much as 65%-90% of the turbine's investment cost. Nowadays the cost effectiveness concept becomes a decision-making and technology evaluation metric. The cost of energy metric accounts for the effect replacement cost and unscheduled maintenance cost parameters. One key of the proposed approach is the idea of maintaining the WTs which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating the cost of O&M is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the cost according to various options of maintenance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost" title="cost">cost</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20state" title=" finite state"> finite state</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20model" title=" Markov model"> Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=operation%20and%20maintenance" title=" operation and maintenance"> operation and maintenance</a> </p> <a href="https://publications.waset.org/abstracts/35860/maintenance-alternatives-related-to-costs-of-wind-turbines-using-finite-state-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35860.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">533</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2006</span> A Comparative Analysis of Geometric and Exponential Laws in Modelling the Distribution of the Duration of Daily Precipitation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mounia%20El%20Hafyani">Mounia El Hafyani</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalid%20El%20Himdi"> Khalid El Himdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Precipitation is one of the key variables in water resource planning. The importance of modeling wet and dry durations is a crucial pointer in engineering hydrology. The objective of this study is to model and analyze the distribution of wet and dry durations. For this purpose, the daily rainfall data from 1967 to 2017 of the Moroccan city of Kenitra’s station are used. Three models are implemented for the distribution of wet and dry durations, namely the first-order Markov chain, the second-order Markov chain, and the truncated negative binomial law. The adherence of the data to the proposed models is evaluated using Chi-square and Kolmogorov-Smirnov tests. The Akaike information criterion is applied to assess the most effective model distribution. We go further and study the law of the number of wet and dry days among k consecutive days. The calculation of this law is done through an algorithm that we have implemented based on conditional laws. We complete our work by comparing the observed moments of the numbers of wet/dry days among k consecutive days to the calculated moment of the three estimated models. The study shows the effectiveness of our approach in modeling wet and dry durations of daily precipitation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain" title="Markov chain">Markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=truncated%20negative%20binomial%20law" title=" truncated negative binomial law"> truncated negative binomial law</a>, <a href="https://publications.waset.org/abstracts/search?q=wet%20and%20dry%20durations" title=" wet and dry durations"> wet and dry durations</a> </p> <a href="https://publications.waset.org/abstracts/134552/a-comparative-analysis-of-geometric-and-exponential-laws-in-modelling-the-distribution-of-the-duration-of-daily-precipitation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2005</span> The Realization of a System’s State Space Based on Markov Parameters by Using Flexible Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Isapour">Ali Isapour</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Nateghi"> Ramin Nateghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> — Markov parameters are unique parameters of the system and remain unchanged under similarity transformations. Markov parameters from a power series that is convergent only if the system matrix’s eigenvalues are inside the unity circle. Therefore, Markov parameters of a stable discrete-time system are convergent. In this study, we aim to realize the system based on Markov parameters by using Artificial Neural Networks (ANN), and this end, we use Flexible Neural Networks. Realization means determining the elements of matrices A, B, C, and D. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Markov%20parameters" title="Markov parameters">Markov parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=realization" title=" realization"> realization</a>, <a href="https://publications.waset.org/abstracts/search?q=activation%20function" title=" activation function"> activation function</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20neural%20network" title=" flexible neural network"> flexible neural network</a> </p> <a href="https://publications.waset.org/abstracts/119535/the-realization-of-a-systems-state-space-based-on-markov-parameters-by-using-flexible-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2004</span> An Estimating Parameter of the Mean in Normal Distribution by Maximum Likelihood, Bayes, and Markov Chain Monte Carlo Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Autcha%20Araveeporn">Autcha Araveeporn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is to compare the parameter estimation of the mean in normal distribution by Maximum Likelihood (ML), Bayes, and Markov Chain Monte Carlo (MCMC) methods. The ML estimator is estimated by the average of data, the Bayes method is considered from the prior distribution to estimate Bayes estimator, and MCMC estimator is approximated by Gibbs sampling from posterior distribution. These methods are also to estimate a parameter then the hypothesis testing is used to check a robustness of the estimators. Data are simulated from normal distribution with the true parameter of mean 2, and variance 4, 9, and 16 when the sample sizes is set as 10, 20, 30, and 50. From the results, it can be seen that the estimation of MLE, and MCMC are perceivably different from the true parameter when the sample size is 10 and 20 with variance 16. Furthermore, the Bayes estimator is estimated from the prior distribution when mean is 1, and variance is 12 which showed the significant difference in mean with variance 9 at the sample size 10 and 20. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayes%20method" title="Bayes method">Bayes method</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain%20Monte%20Carlo%20method" title=" Markov chain Monte Carlo method"> Markov chain Monte Carlo method</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20method" title=" maximum likelihood method"> maximum likelihood method</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title=" normal distribution"> normal distribution</a> </p> <a href="https://publications.waset.org/abstracts/51087/an-estimating-parameter-of-the-mean-in-normal-distribution-by-maximum-likelihood-bayes-and-markov-chain-monte-carlo-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51087.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2003</span> A Semi-Markov Chain-Based Model for the Prediction of Deterioration of Concrete Bridges in Quebec</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eslam%20Mohammed%20Abdelkader">Eslam Mohammed Abdelkader</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Marzouk"> Mohamed Marzouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Zayed"> Tarek Zayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Infrastructure systems are crucial to every aspect of life on Earth. Existing Infrastructure is subjected to degradation while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges play a crucial role in urban transportation networks. Moreover, they are subjected to high level of deterioration because of the variable traffic loading, extreme weather conditions, cycles of freeze and thaw, etc. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays especially in the large transportation networks due to the huge variance between the need for maintenance actions, and the available funds to perform such actions. Deterioration models represent a very important aspect for the effective use of BMSs. This paper presents a probabilistic time-based model that is capable of predicting the condition ratings of the concrete bridge decks along its service life. The deterioration process of the concrete bridge decks is modeled using semi-Markov process. One of the main challenges of the Markov Chain Decision Process (MCDP) is the construction of the transition probability matrix. Yet, the proposed model overcomes this issue by modeling the sojourn times based on some probability density functions. The sojourn times of each condition state are fitted to probability density functions based on some goodness of fit tests such as Kolmogorov-Smirnov test, Anderson Darling, and chi-squared test. The parameters of the probability density functions are obtained using maximum likelihood estimation (MLE). The condition ratings obtained from the Ministry of Transportation in Quebec (MTQ) are utilized as a database to construct the deterioration model. Finally, a comparison is conducted between the Markov Chain and semi-Markov chain to select the most feasible prediction model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bridge%20management%20system" title="bridge management system">bridge management system</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20decks" title=" bridge decks"> bridge decks</a>, <a href="https://publications.waset.org/abstracts/search?q=deterioration%20model" title=" deterioration model"> deterioration model</a>, <a href="https://publications.waset.org/abstracts/search?q=Semi-Markov%20chain" title=" Semi-Markov chain"> Semi-Markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=sojourn%20times" title=" sojourn times"> sojourn times</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a> </p> <a href="https://publications.waset.org/abstracts/83317/a-semi-markov-chain-based-model-for-the-prediction-of-deterioration-of-concrete-bridges-in-quebec" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83317.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">211</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2002</span> Modeling of Production Lines Systems with Layout Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadegh%20Abebi">Sadegh Abebi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are problems with estimating time of product process of products, especially when there is variable serving time, like control stage. These problems will cause overestimation of process time. Layout constraints, reworking constraints and inflexible product schedule in multi product lines, needs a precise planning to reduce volume in particular situation of line stock. In this article, by analyzing real queue systems with layout constraints and by using concepts and principles of Markov chain in queue theory, a hybrid model has been presented. This model can be a base to assess queue systems with probable parameters of service. Here by presenting a case study, the proposed model will be described. so, production lines of a home application manufacturer will be analyzed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Queuing%20theory" title="Queuing theory">Queuing theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Chain" title=" Markov Chain"> Markov Chain</a>, <a href="https://publications.waset.org/abstracts/search?q=layout" title=" layout"> layout</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20balance" title=" line balance"> line balance</a> </p> <a href="https://publications.waset.org/abstracts/26003/modeling-of-production-lines-systems-with-layout-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">625</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2001</span> A New Verification Based Congestion Control Scheme in Mobile Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20K.%20Guha%20Thakurta">P. K. Guha Thakurta</a>, <a href="https://publications.waset.org/abstracts/search?q=Shouvik%20Roy"> Shouvik Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhawana%20Raj"> Bhawana Raj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A congestion control scheme in mobile networks is proposed in this paper through a verification based model. The model proposed in this work is represented through performance metric like buffer Occupancy, latency and packet loss rate. Based on pre-defined values, each of the metric is introduced in terms of three different states. A Markov chain based model for the proposed work is introduced to monitor the occurrence of the corresponding state transitions. Thus, the estimation of the network status is obtained in terms of performance metric. In addition, the improved performance of our proposed model over existing works is shown with experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=congestion" title="congestion">congestion</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20networks" title=" mobile networks"> mobile networks</a>, <a href="https://publications.waset.org/abstracts/search?q=buffer" title=" buffer"> buffer</a>, <a href="https://publications.waset.org/abstracts/search?q=delay" title=" delay"> delay</a>, <a href="https://publications.waset.org/abstracts/search?q=call%20drop" title=" call drop"> call drop</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain" title=" markov chain"> markov chain</a> </p> <a href="https://publications.waset.org/abstracts/19020/a-new-verification-based-congestion-control-scheme-in-mobile-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19020.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2000</span> Finite Dynamic Programming to Decision Making in the Use of Industrial Residual Water Treatment Plants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oscar%20Vega%20Camacho">Oscar Vega Camacho</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Vargas"> Andrea Vargas</a>, <a href="https://publications.waset.org/abstracts/search?q=Ellery%20Ariza"> Ellery Ariza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the application of finite dynamic programming, specifically the "Markov Chain" model, as part of the decision making process of a company in the cosmetics sector located in the vicinity of Bogota DC. The objective of this process was to decide whether the company should completely reconstruct its waste water treatment plant or instead optimize the plant through the addition of equipment. The goal of both of these options was to make the required improvements in order to comply with parameters established by national legislation regarding the treatment of waste before it is released into the environment. This technique will allow the company to select the best option and implement a solution for the processing of waste to minimize environmental damage and the acquisition and implementation costs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title="decision making">decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain" title=" markov chain"> markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=waste%20water" title=" waste water"> waste water</a> </p> <a href="https://publications.waset.org/abstracts/12123/finite-dynamic-programming-to-decision-making-in-the-use-of-industrial-residual-water-treatment-plants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1999</span> Application of Finite Dynamic Programming to Decision Making in the Use of Industrial Residual Water Treatment Plants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oscar%20Vega%20Camacho">Oscar Vega Camacho</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Vargas%20Guevara"> Andrea Vargas Guevara</a>, <a href="https://publications.waset.org/abstracts/search?q=Ellery%20Rowina%20Ariza"> Ellery Rowina Ariza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the application of finite dynamic programming, specifically the "Markov Chain" model, as part of the decision making process of a company in the cosmetics sector located in the vicinity of Bogota DC. The objective of this process was to decide whether the company should completely reconstruct its wastewater treatment plant or instead optimize the plant through the addition of equipment. The goal of both of these options was to make the required improvements in order to comply with parameters established by national legislation regarding the treatment of waste before it is released into the environment. This technique will allow the company to select the best option and implement a solution for the processing of waste to minimize environmental damage and the acquisition and implementation costs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title="decision making">decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain" title=" Markov chain"> Markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=wastewater" title=" wastewater"> wastewater</a> </p> <a href="https://publications.waset.org/abstracts/12122/application-of-finite-dynamic-programming-to-decision-making-in-the-use-of-industrial-residual-water-treatment-plants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12122.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1998</span> Statistical Design of Synthetic VP X-bar Control Chat Using Markov Chain Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akbar%20Heydari">Ali Akbar Heydari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain%20approach" title=" markov chain approach"> markov chain approach</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20design" title=" statistical design"> statistical design</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic" title=" synthetic"> synthetic</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20parameter" title=" variable parameter"> variable parameter</a> </p> <a href="https://publications.waset.org/abstracts/146094/statistical-design-of-synthetic-vp-x-bar-control-chat-using-markov-chain-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146094.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1997</span> Extended Kalman Filter and Markov Chain Monte Carlo Method for Uncertainty Estimation: Application to X-Ray Fluorescence Machine Calibration and Metal Testing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Bouhouche">S. Bouhouche</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Drai"> R. Drai</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Bast"> J. Bast</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is concerned with a method for uncertainty evaluation of steel sample content using X-Ray Fluorescence method. The considered method of analysis is a comparative technique based on the X-Ray Fluorescence; the calibration step assumes the adequate chemical composition of metallic analyzed sample. It is proposed in this work a new combined approach using the Kalman Filter and Markov Chain Monte Carlo (MCMC) for uncertainty estimation of steel content analysis. The Kalman filter algorithm is extended to the model identification of the chemical analysis process using the main factors affecting the analysis results; in this case, the estimated states are reduced to the model parameters. The MCMC is a stochastic method that computes the statistical properties of the considered states such as the probability distribution function (PDF) according to the initial state and the target distribution using Monte Carlo simulation algorithm. Conventional approach is based on the linear correlation, the uncertainty budget is established for steel Mn(wt%), Cr(wt%), Ni(wt%) and Mo(wt%) content respectively. A comparative study between the conventional procedure and the proposed method is given. This kind of approaches is applied for constructing an accurate computing procedure of uncertainty measurement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title="Kalman filter">Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain%20Monte%20Carlo" title=" Markov chain Monte Carlo"> Markov chain Monte Carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=x-ray%20fluorescence%20calibration%20and%20testing" title=" x-ray fluorescence calibration and testing"> x-ray fluorescence calibration and testing</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20content%20measurement" title=" steel content measurement"> steel content measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20measurement" title=" uncertainty measurement"> uncertainty measurement</a> </p> <a href="https://publications.waset.org/abstracts/88897/extended-kalman-filter-and-markov-chain-monte-carlo-method-for-uncertainty-estimation-application-to-x-ray-fluorescence-machine-calibration-and-metal-testing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88897.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1996</span> On the convergence of the Mixed Integer Randomized Pattern Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebert%20Brea">Ebert Brea</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a novel direct search algorithm for identifying at least a local minimum of mixed integer nonlinear unconstrained optimization problems. The Mixed Integer Randomized Pattern Search Algorithm (MIRPSA), so-called by the author, is based on a randomized pattern search, which is modified by the MIRPSA for finding at least a local minimum of our problem. The MIRPSA has two main operations over the randomized pattern search: moving operation and shrinking operation. Each operation is carried out by the algorithm when a set of conditions is held. The convergence properties of the MIRPSA is analyzed using a Markov chain approach, which is represented by an infinite countable set of state space λ, where each state d(q) is defined by a measure of the qth randomized pattern search Hq, for all q in N. According to the algorithm, when a moving operation is carried out on the qth randomized pattern search Hq, the MIRPSA holds its state. Meanwhile, if the MIRPSA carries out a shrinking operation over the qth randomized pattern search Hq, the algorithm will visit the next state, this is, a shrinking operation at the qth state causes a changing of the qth state into (q+1)th state. It is worthwhile pointing out that the MIRPSA never goes back to any visited states because the MIRPSA only visits any qth by shrinking operations. In this article, we describe the MIRPSA for mixed integer nonlinear unconstrained optimization problems for doing a deep study of its convergence properties using Markov chain viewpoint. We herein include a low dimension case for showing more details of the MIRPSA, when the algorithm is used for identifying the minimum of a mixed integer quadratic function. Besides, numerical examples are also shown in order to measure the performance of the MIRPSA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=direct%20search" title="direct search">direct search</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20optimization" title=" mixed integer optimization"> mixed integer optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20search" title=" random search"> random search</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain" title=" Markov chain"> Markov chain</a> </p> <a href="https://publications.waset.org/abstracts/33175/on-the-convergence-of-the-mixed-integer-randomized-pattern-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33175.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">470</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1995</span> Modeling of System Availability and Bayesian Analysis of Bivariate Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Farooq">Muhammad Farooq</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahtasham%20Gul"> Ahtasham Gul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To meet the desired standard, it is important to monitor and analyze different engineering processes to get desired output. The bivariate distributions got a lot of attention in recent years to describe the randomness of natural as well as artificial mechanisms. In this article, a bivariate model is constructed using two independent models developed by the nesting approach to study the effect of each component on reliability for better understanding. Further, the Bayes analysis of system availability is studied by considering prior parametric variations in the failure time and repair time distributions. Basic statistical characteristics of marginal distribution, like mean median and quantile function, are discussed. We use inverse Gamma prior to study its frequentist properties by conducting Monte Carlo Markov Chain (MCMC) sampling scheme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20availability%20Weibull" title=" system availability Weibull"> system availability Weibull</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20Lomax" title=" inverse Lomax"> inverse Lomax</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20Markov%20Chain" title=" Monte Carlo Markov Chain"> Monte Carlo Markov Chain</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a> </p> <a href="https://publications.waset.org/abstracts/158945/modeling-of-system-availability-and-bayesian-analysis-of-bivariate-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1994</span> Markov Characteristics of the Power Line Communication Channels in China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming-Yue%20Zhai">Ming-Yue Zhai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the multipath and pulse noise nature, power line communications(PLC) channel can be modelled as a memory one with the finite states Markov model(FSMC). As the most important parameter modelling a Markov channel,the memory order in an FSMC is not solved in PLC systems yet. In the paper, the mutual information is used as a measure of the dependence between the different symbols, treated as the received SNA or amplitude of the current channel symbol or that of previous symbols. The joint distribution probabilities of the envelopes in PLC systems are computed based on the multi-path channel model, which is commonly used in PLC. we confirm that given the information of the symbol immediately preceding the current one, any other previous symbol is independent of the current one in PLC systems, which means the PLC channels is a Markov chain with the first-order. The field test is also performed to model the received OFDM signals with the help of AR model. The results show that the first-order AR model is enough to model the fading channel in PLC systems, which means the amount of uncertainty remaining in the current symbol should be negligible, given the information corresponding to the immediately preceding one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20line%20communication" title="power line communication">power line communication</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20model" title=" channel model"> channel model</a>, <a href="https://publications.waset.org/abstracts/search?q=markovian" title=" markovian"> markovian</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20theory" title=" information theory"> information theory</a>, <a href="https://publications.waset.org/abstracts/search?q=first-order" title=" first-order"> first-order</a> </p> <a href="https://publications.waset.org/abstracts/10405/markov-characteristics-of-the-power-line-communication-channels-in-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10405.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1993</span> Application of Multilayer Perceptron and Markov Chain Analysis Based Hybrid-Approach for Predicting and Monitoring the Pattern of LULC Using Random Forest Classification in Jhelum District, Punjab, Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Basit%20Aftab">Basit Aftab</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhichao%20Wang"> Zhichao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Zhongke"> Feng Zhongke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Land Use and Land Cover Change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the spatiotemporal dynamics of land use and land cover (LULC) across a three-decade period (1992–2022) in a district area. The goal is to support sustainable land management and urban planning by utilizing the combination of remote sensing, GIS data, and observations from Landsat satellites 5 and 8 to provide precise predictions of the trajectory of urban sprawl. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the Random Forest method with Multilayer Perceptron (MLP) and Markov Chain analysis. To predict the dynamics of LULC change for the year 2035, a hybrid technique based on multilayer Perceptron and Markov Chain Model Analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. The study also discovered that between 1998 and 2023, the built-up area increased by 468 km² as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will be increased by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. It provides valuable insights for policymakers, land managers, and researchers to support sustainable land use planning, conservation efforts, and climate change mitigation strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20use%20land%20cover" title="land use land cover">land use land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain%20model" title=" Markov chain model"> Markov chain model</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20land" title=" sustainable land"> sustainable land</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing." title=" remote sensing."> remote sensing.</a> </p> <a href="https://publications.waset.org/abstracts/188578/application-of-multilayer-perceptron-and-markov-chain-analysis-based-hybrid-approach-for-predicting-and-monitoring-the-pattern-of-lulc-using-random-forest-classification-in-jhelum-district-punjab-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188578.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">33</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1992</span> Bayesian Parameter Inference for Continuous Time Markov Chains with Intractable Likelihood</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Randa%20Alharbi">Randa Alharbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladislav%20Vyshemirsky"> Vladislav Vyshemirsky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Approximate%20Bayesian%20computation%28ABC%29" title="Approximate Bayesian computation(ABC)">Approximate Bayesian computation(ABC)</a>, <a href="https://publications.waset.org/abstracts/search?q=Continuous-Time%20Markov%20Chains" title=" Continuous-Time Markov Chains"> Continuous-Time Markov Chains</a>, <a href="https://publications.waset.org/abstracts/search?q=Sequential%20Monte%20Carlo" title=" Sequential Monte Carlo"> Sequential Monte Carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=Particle%20Markov%20chain%20Monte%20Carlo%20%28PMCMC%29" title=" Particle Markov chain Monte Carlo (PMCMC)"> Particle Markov chain Monte Carlo (PMCMC)</a> </p> <a href="https://publications.waset.org/abstracts/82129/bayesian-parameter-inference-for-continuous-time-markov-chains-with-intractable-likelihood" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82129.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1991</span> Estimating Bridge Deterioration for Small Data Sets Using Regression and Markov Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yina%20F.%20Mu%C3%B1oz">Yina F. Muñoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Paz"> Alexander Paz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanns%20De%20La%20Fuente-Mella"> Hanns De La Fuente-Mella</a>, <a href="https://publications.waset.org/abstracts/search?q=Joaquin%20V.%20Fari%C3%B1a"> Joaquin V. Fariña</a>, <a href="https://publications.waset.org/abstracts/search?q=Guilherme%20M.%20Sales"> Guilherme M. Sales</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concrete%20bridges" title="concrete bridges">concrete bridges</a>, <a href="https://publications.waset.org/abstracts/search?q=deterioration" title=" deterioration"> deterioration</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chains" title=" Markov chains"> Markov chains</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20matrix" title=" probability matrix"> probability matrix</a> </p> <a href="https://publications.waset.org/abstracts/43375/estimating-bridge-deterioration-for-small-data-sets-using-regression-and-markov-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43375.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1990</span> New Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=regression" title="regression">regression</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise" title=" piecewise"> piecewise</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20Jump%20MCMC" title=" reversible Jump MCMC"> reversible Jump MCMC</a> </p> <a href="https://publications.waset.org/abstracts/31651/new-segmentation-of-piecewise-linear-regression-models-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31651.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">521</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1989</span> On-Farm Diversification in Vietnam: Determinants and Trends</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diep%20Thanh%20Tung">Diep Thanh Tung</a>, <a href="https://publications.waset.org/abstracts/search?q=Joachim%20Aurbacher"> Joachim Aurbacher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to measure the level of on-farm diversification in Vietnam. The empirical results of the research carried out reflect regional differences in terms of on-farm diversification and its determinants. Households in the northern regions have adapted to the fragmented and small-sized parcels of land held by diversifying their on-farm activities. In contrast, the Mekong delta region in the south of Vietnam is characterized by larger agricultural parcels and a specialization in rice production. Land use fragmentation, as reflected by a large number of plots in a given area, is one of the most important reasons for the high levels of on-farm diversification seen, while the higher share of non-farm income in total income is the reason of lower levels of on-farm diversification. Households have reacted to natural and economic shocks by diversifying their on-farm activities. The non-stationary Markov chain model used here shows various diversification scenarios and trends. In most cases, on-farm diversification generally tends to reduce over the next few years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diversification" title="diversification">diversification</a>, <a href="https://publications.waset.org/abstracts/search?q=simpson%20index" title=" simpson index"> simpson index</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20effects" title=" fixed effects"> fixed effects</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary%20markov%20chain" title=" non-stationary markov chain"> non-stationary markov chain</a> </p> <a href="https://publications.waset.org/abstracts/22799/on-farm-diversification-in-vietnam-determinants-and-trends" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22799.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1988</span> Segmentation of Piecewise Polynomial Regression Model by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=piecewise%20regression" title="piecewise regression">piecewise regression</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/46201/segmentation-of-piecewise-polynomial-regression-model-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46201.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">373</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1987</span> Valuation of Caps and Floors in a LIBOR Market Model with Markov Jump Risks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shih-Kuei%20Lin">Shih-Kuei Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The characterization of the arbitrage-free dynamics of interest rates is developed in this study under the presence of Markov jump risks, when the term structure of the interest rates is modeled through simple forward rates. We consider Markov jump risks by allowing randomness in jump sizes, independence between jump sizes and jump times. The Markov jump diffusion model is used to capture empirical phenomena and to accurately describe interest jump risks in a financial market. We derive the arbitrage-free model of simple forward rates under the spot measure. Moreover, the analytical pricing formulas for a cap and a floor are derived under the forward measure when the jump size follows a lognormal distribution. In our empirical analysis, we find that the LIBOR market model with Markov jump risk better accounts for changes from/to different states and different rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arbitrage-free" title="arbitrage-free">arbitrage-free</a>, <a href="https://publications.waset.org/abstracts/search?q=cap%20and%20floor" title=" cap and floor"> cap and floor</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20jump%20diffusion%20model" title=" Markov jump diffusion model"> Markov jump diffusion model</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20forward%20rate%20model" title=" simple forward rate model"> simple forward rate model</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20smile" title=" volatility smile"> volatility smile</a>, <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title=" EM algorithm"> EM algorithm</a> </p> <a href="https://publications.waset.org/abstracts/11690/valuation-of-caps-and-floors-in-a-libor-market-model-with-markov-jump-risks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11690.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1986</span> New Estimation in Autoregressive Models with Exponential White Noise by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman%20Suparman">Suparman Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A white noise in autoregressive (AR) model is often assumed to be normally distributed. In application, the white noise usually do not follows a normal distribution. This paper aims to estimate a parameter of AR model that has a exponential white noise. A Bayesian method is adopted. A prior distribution of the parameter of AR model is selected and then this prior distribution is combined with a likelihood function of data to get a posterior distribution. Based on this posterior distribution, a Bayesian estimator for the parameter of AR model is estimated. Because the order of AR model is considered a parameter, this Bayesian estimator cannot be explicitly calculated. To resolve this problem, a method of reversible jump Markov Chain Monte Carlo (MCMC) is adopted. A result is a estimation of the parameter AR model can be simultaneously calculated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20%28AR%29%20model" title="autoregressive (AR) model">autoregressive (AR) model</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20white%20Noise" title=" exponential white Noise"> exponential white Noise</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20Markov%20Chain%20Monte%20Carlo%20%28MCMC%29" title=" reversible jump Markov Chain Monte Carlo (MCMC)"> reversible jump Markov Chain Monte Carlo (MCMC)</a> </p> <a href="https://publications.waset.org/abstracts/71720/new-estimation-in-autoregressive-models-with-exponential-white-noise-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1985</span> Stability Analysis of Green Coffee Export Markets of Ethiopia: Markov-Chain Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Woldu">Gabriel Woldu</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Sassi"> Maria Sassi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Coffee performs a pivotal role in Ethiopia's GDP, revenue, employment, domestic demand, and export earnings. Ethiopia's coffee production and exports show high variability in the amount of production and export earnings. Despite being the continent's fifth-largest coffee producer, Ethiopia has not developed its ability to shine as a major exporter in the globe's green coffee exports. Ethiopian coffee exports were not stable and had high volume and earnings fluctuations. The main aim of this study was to analyze the dynamics of the export of coffee variation to different importing nations using a first-order Markov Chain model. 14 years of time-series data has been used to examine the direction and structural change in the export of coffee. A compound annual growth rate (CAGR) was used to determine the annual growth rate in the coffee export quantity, value, and per-unit price over the study period. The major export markets for Ethiopian coffee were Germany, Japan, and the USA, which were more stable, while countries such as France, Italy, Belgium, and Saudi Arabia were less stable and had low retention rates for Ethiopian coffee. The study, therefore, recommends that Ethiopia should again revitalize its market to France, Italy, Belgium, and Saudi Arabia, as these countries are the major coffee-consuming countries in the world to boost its export stake to the global coffee markets in the future. In order to further enhance export stability, the Ethiopian Government and other stakeholders in the coffee sector should have to work on reducing the volatility of coffee output and exports in order to improve production and quality efficiency, so that stabilize markets as well as to make the product attractive and price competitive in the importing countries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coffee" title="coffee">coffee</a>, <a href="https://publications.waset.org/abstracts/search?q=CAGR" title=" CAGR"> CAGR</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain" title=" Markov chain"> Markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=direction%20of%20trade" title=" direction of trade"> direction of trade</a>, <a href="https://publications.waset.org/abstracts/search?q=Ethiopia" title=" Ethiopia"> Ethiopia</a> </p> <a href="https://publications.waset.org/abstracts/130336/stability-analysis-of-green-coffee-export-markets-of-ethiopia-markov-chain-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130336.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1984</span> Vulnerability Assessment of Healthcare Interdependent Critical Infrastructure Coloured Petri Net Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Nivedita">N. Nivedita</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Durbha"> S. Durbha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Critical Infrastructure (CI) consists of services and technological networks such as healthcare, transport, water supply, electricity supply, information technology etc. These systems are necessary for the well-being and to maintain effective functioning of society. Critical Infrastructures can be represented as nodes in a network where they are connected through a set of links depicting the logical relationship among them; these nodes are interdependent on each other and interact with each at other at various levels, such that the state of each infrastructure influences or is correlated to the state of another. Disruption in the service of one infrastructure nodes of the network during a disaster would lead to cascading and escalating disruptions across other infrastructures nodes in the network. The operation of Healthcare Infrastructure is one such Critical Infrastructure that depends upon a complex interdependent network of other Critical Infrastructure, and during disasters it is very vital for the Healthcare Infrastructure to be protected, accessible and prepared for a mass casualty. To reduce the consequences of a disaster on the Critical Infrastructure and to ensure a resilient Critical Health Infrastructure network, knowledge, understanding, modeling, and analyzing the inter-dependencies between the infrastructures is required. The paper would present inter-dependencies related to Healthcare Critical Infrastructure based on Hierarchical Coloured Petri Nets modeling approach, given a flood scenario as the disaster which would disrupt the infrastructure nodes. The model properties are being analyzed for the various state changes which occur when there is a disruption or damage to any of the Critical Infrastructure. The failure probabilities for the failure risk of interconnected systems are calculated by deriving a reachability graph, which is later mapped to a Markov chain. By analytically solving and analyzing the Markov chain, the overall vulnerability of the Healthcare CI HCPN model is demonstrated. The entire model would be integrated with Geographic information-based decision support system to visualize the dynamic behavior of the interdependency of the Healthcare and related CI network in a geographically based environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=critical%20infrastructure%20interdependency" title="critical infrastructure interdependency">critical infrastructure interdependency</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20coloured%20petrinet" title=" hierarchical coloured petrinet"> hierarchical coloured petrinet</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20critical%20infrastructure" title=" healthcare critical infrastructure"> healthcare critical infrastructure</a>, <a href="https://publications.waset.org/abstracts/search?q=Petri%20Nets" title=" Petri Nets"> Petri Nets</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain" title=" Markov chain"> Markov chain</a> </p> <a href="https://publications.waset.org/abstracts/21872/vulnerability-assessment-of-healthcare-interdependent-critical-infrastructure-coloured-petri-net-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21872.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1983</span> 3D Printing: Rebounding from Global Supply Chain Disruption Due to Natural Disaster</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurjinder%20Singh">Gurjinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasmeen%20Kaur"> Jasmeen Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Mukul%20Dhiman"> Mukul Dhiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper mainly describes the significance of 3D printing in the supply chain management in a scenario when there is disruption in global supply chain. Furthermore, the development and implementation of supply chain strategies in context of 3D printing technology is framed to make supply chain of an organization resilient to disruption caused by natural disasters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20printing" title="3D printing">3D printing</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20supply%20chain" title=" global supply chain"> global supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20strategies" title=" supply chain strategies"> supply chain strategies</a> </p> <a href="https://publications.waset.org/abstracts/24079/3d-printing-rebounding-from-global-supply-chain-disruption-due-to-natural-disaster" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">476</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1982</span> Supply Chain Competitiveness with the Perspective of Service Performance Between Supply Chain Actors and Functions: A Theoretical Model </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umer%20Mukhtar">Umer Mukhtar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supply Chain Competitiveness is the capability of a supply chain to deliver value to the customer for the sake of competitive advantage. Service Performance and Quality intervene between supply chain actors including functions inside the firm in a significant way for the supply chain to achieve a competitive position in the market to gain competitive advantage. Supply Chain competitiveness is the current issue of interest because of supply chains’ competition for competitive advantage rather than firms’. A proposed theoretical model is developed by extracting and integrating different theories to pursue further inquiry based on case studies and survey design. It is also intended to develop a scale of service performance for functions of the focal firm that is a revolving center for a whole supply chain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20competitiveness" title="supply chain competitiveness">supply chain competitiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20performance%20in%20supply%20chain" title=" service performance in supply chain"> service performance in supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20quality%20in%20supply%20chain" title=" service quality in supply chain"> service quality in supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=competitive%20advantage%20by%20supply%20chain" title=" competitive advantage by supply chain"> competitive advantage by supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=networks%20and%20supply%20chain" title=" networks and supply chain"> networks and supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20value" title=" customer value"> customer value</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20supply%20chain" title=" value supply chain"> value supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20chain" title=" value chain"> value chain</a> </p> <a href="https://publications.waset.org/abstracts/16908/supply-chain-competitiveness-with-the-perspective-of-service-performance-between-supply-chain-actors-and-functions-a-theoretical-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">610</span> 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