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Shib Sankar Sana - One of the best experts on this subject based on the ideXlab platform.
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an imperfect production process for time varying demand with inflation and time value of money an emq model
Expert Systems With Applications, 2011Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:Abstract The paper deals with an economic manufacturing quantity (EMQ) model for time-dependent (quadratic) demand pattern. Every manufacturing sector wants to produce perfect quality items. But in long run process, there may arise different types of difficulties like labor problem, machinery capabilities problems, etc., due to that the machinery systems shift from in-control state to out-of-control state as a result the manufacturing systems produce imperfect quality items. The imperfect items are reworked at a cost to become the perfect one. The rework cost may be reduced by improvements in product Reliability i.e., the production process depend on time and also the Reliability Parameter. We want to determine the optimal product Reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process using Euler–Lagrange theory to build up the necessary and sufficient conditions for optimality of the dynamic variables. Finally, a numerical example is discussed to test the model which is illustrated graphically also.
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a stock dependent inventory model in an imperfect production process
International Journal of Procurement Management, 2010Co-Authors: Biswajit Sarkar, K S Chaudhuri, Shib Sankar SanaAbstract:The paper deals with an economic manufacturing quantity model for stock-dependent demand in an imperfect production process. In long run of a manufacturing process, the system undergoes out-of-control state and the process begins to produce imperfect quality products. The production of imperfect quality items depends on time and Reliability Parameter. These imperfect items are reworked at a cost to restore to its original quality. Moreover, the development cost that varies with Reliability Parameter of the manufacturing system is introduced to reduce the percentage of imperfect quality items. In our model, the unit production cost is a function of Reliability Parameter and production rate. The profit function is maximised by Euler-Lagrange method by considering different type's costs. The unit production cost and development cost which are functions of Reliability Parameter vary with changes with technology and resources. Concavity of the profit function in the numerical example establishes the existence of the global maximum solution which is graphically illustrated. The inventory/production versus time and optimal development cost versus optimal Reliability are also demonstrated graphically.
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optimal Reliability production lot size and safety stock in an imperfect production system
International Journal of Mathematics in Operational Research, 2010Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:This paper is concerned with the joint determination of optimal production lot size, safety stock and Reliability Parameter under the realistic assumptions that the production facility is subject to random breakdown of machinery system and also to change in the variable Reliability Parameter. Reliability of a machinery system is a decision variable that can be increased by more investment in production technology. In this model, preventive and corrective maintenance, safety stock for repair times and shortages are adopted to generalise the model. Except machinery breakdown, the manufacturing system may shift from in-control state to out-of-control state. During out-of-control state, a certain percentage of total production consists of defective items which can be reworked immediately at a cost to make as good as perfect quality items. The cost function is maximised by Khun-Tucker method. Numerical results are provided to illustrate both the study of the optimal solutions and sensitivity of different changes in key Parameters.
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a production inventory model in an imperfect production process
European Journal of Operational Research, 2010Co-Authors: Shib Sankar SanaAbstract:Abstract The paper develops a model to determine the optimal product Reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process. The basic assumption of the classical Economic Manufacturing Quantity (EMQ) model is that all manufacturing items are of perfect quality. The assumption is not true in practice. Most of the production system produces perfect and imperfect quality items. In some cases the imperfect quality (non conforming) items are reworked at a cost to restore its quality to the original one. Rework cost may be reduced by improvements in product Reliability (i.e., decreasing in product Reliability Parameter). Lower value of product Reliability Parameter results in increase development cost of production and also smaller quantity of nonconforming products. The unit production cost is a function of product Reliability Parameter and production rate. As a result, higher development cost increases unit production cost. The problem of optimal planning work and rework processes belongs to the broad field of production–inventory model which deals with all kinds of reuse processes in supply chains. These processes aim to recover defective product items in such a way that they meet the quality level of ‘good item’. The benefits from imperfect quality items are: regaining the material and value added on defective items and improving the environment protection. In this point of view, a model is introduced here to guide a firm/industry in addressing variable product Reliability factor, variable unit production cost and dynamic production rate for time-varying demand. The paper provides an optimal control formulation of the problem and develops necessary and sufficient conditions for optimality of the dynamic variables. In this purpose, the Euler–Lagrange method is used to obtain optimal solutions for product Reliability Parameter and dynamic production rate. Finally, numerical examples are given to illustrate the proposed model.
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optimal Reliability production lotsize and safety stock an economic manufacturing quantity model
international journal of management science and engineering management, 2010Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:Abstract Reliability based maintenance provides sound guidance for managers who wish to attain high standards of maintenance at their operating systems. Basically, the amount and the type of maintenance applied for the system depend strongly on the age of components of a machinery system. The present paper describes a production policy (resumption and non-resumption) in order to find out an optimal safety stock, production lotsize and Reliability Parameters. A production-inventory model with stochastic machine breakdown and its corrective and regular (preventive) repairs with safety stock has been developed incorporating variable Reliability Parameters. The Reliability Parameter (θ) is a design variable. Learning effects result in the cost of technology for the design variable which is an decreasing function of (θ) and this, in turn, has an impact on the average cost. Hence the setting of optimal values of Reliability Parameters and production lotsize with safety stock needs to be considered jointly that ...
Biswajit Sarkar - One of the best experts on this subject based on the ideXlab platform.
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An economic manufacturing quantity model with probabilistic deterioration in a production system
Economic Modelling, 2013Co-Authors: Mitali Sarkar, Biswajit SarkarAbstract:This paper develops an economic manufacturing quantity (EMQ) model with deterioration and exponential demand in a production system over a finite time horizon under the effect of inflation and time value of money. The production rate is a dynamic variable (varying with time) in a production system. Due to a long run process, the machinery system is converted from in-control state to out-of-control state which results the production of improper items. The improper items are reworked at a fixed cost to make it as proper. With the increasing value of time, the production of improper item also increases. To reduce the production of the improper items, the systems have to be more reliable and with less amount of failure. In this direction, the model considers that the development cost, production cost, and material cost are dependent on the Reliability Parameter. The deterioration of the product is considered probabilistic to make the research a more realistic one. By considering the Reliability Parameter as a decision variable, we try to obtain the associated profit of the system which we have to maximize. To derive the maximization procedure, we use Euler–Lagrange formula from control theory. We outline some numerical examples along with graphical representations and sensitivity analysis to illustrate the model.
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an inventory model with Reliability in an imperfect production process
Applied Mathematics and Computation, 2012Co-Authors: Biswajit SarkarAbstract:Abstract The paper analyzes an economic manufacturing quantity (EMQ) model with price and advertising demand pattern in an imperfect production process under the effect of inflation. If the machine goes through a long-run process, it may shift from in-control state to out-of-control state. As a result, the system produces imperfect items. The imperfect items are reworked at a cost to make it as new. The production of imperfect quality items increases with time. To reduce the production of the imperfect items, the systems have to more reliable and the produced items depend on the Reliability of the machinery system. In this direction, the author considers that the development cost, production cost, material cost are dependent on Reliability Parameter. Considering Reliability as a decision variable, the author constructs an integrated profit function which is maximized by control theory. A numerical example along with graphical representation and sensitivity analysis are provided to illustrate the model.
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an imperfect production process for time varying demand with inflation and time value of money an emq model
Expert Systems With Applications, 2011Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:Abstract The paper deals with an economic manufacturing quantity (EMQ) model for time-dependent (quadratic) demand pattern. Every manufacturing sector wants to produce perfect quality items. But in long run process, there may arise different types of difficulties like labor problem, machinery capabilities problems, etc., due to that the machinery systems shift from in-control state to out-of-control state as a result the manufacturing systems produce imperfect quality items. The imperfect items are reworked at a cost to become the perfect one. The rework cost may be reduced by improvements in product Reliability i.e., the production process depend on time and also the Reliability Parameter. We want to determine the optimal product Reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process using Euler–Lagrange theory to build up the necessary and sufficient conditions for optimality of the dynamic variables. Finally, a numerical example is discussed to test the model which is illustrated graphically also.
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a stock dependent inventory model in an imperfect production process
International Journal of Procurement Management, 2010Co-Authors: Biswajit Sarkar, K S Chaudhuri, Shib Sankar SanaAbstract:The paper deals with an economic manufacturing quantity model for stock-dependent demand in an imperfect production process. In long run of a manufacturing process, the system undergoes out-of-control state and the process begins to produce imperfect quality products. The production of imperfect quality items depends on time and Reliability Parameter. These imperfect items are reworked at a cost to restore to its original quality. Moreover, the development cost that varies with Reliability Parameter of the manufacturing system is introduced to reduce the percentage of imperfect quality items. In our model, the unit production cost is a function of Reliability Parameter and production rate. The profit function is maximised by Euler-Lagrange method by considering different type's costs. The unit production cost and development cost which are functions of Reliability Parameter vary with changes with technology and resources. Concavity of the profit function in the numerical example establishes the existence of the global maximum solution which is graphically illustrated. The inventory/production versus time and optimal development cost versus optimal Reliability are also demonstrated graphically.
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optimal Reliability production lot size and safety stock in an imperfect production system
International Journal of Mathematics in Operational Research, 2010Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:This paper is concerned with the joint determination of optimal production lot size, safety stock and Reliability Parameter under the realistic assumptions that the production facility is subject to random breakdown of machinery system and also to change in the variable Reliability Parameter. Reliability of a machinery system is a decision variable that can be increased by more investment in production technology. In this model, preventive and corrective maintenance, safety stock for repair times and shortages are adopted to generalise the model. Except machinery breakdown, the manufacturing system may shift from in-control state to out-of-control state. During out-of-control state, a certain percentage of total production consists of defective items which can be reworked immediately at a cost to make as good as perfect quality items. The cost function is maximised by Khun-Tucker method. Numerical results are provided to illustrate both the study of the optimal solutions and sensitivity of different changes in key Parameters.
K S Chaudhuri - One of the best experts on this subject based on the ideXlab platform.
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an imperfect production process for time varying demand with inflation and time value of money an emq model
Expert Systems With Applications, 2011Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:Abstract The paper deals with an economic manufacturing quantity (EMQ) model for time-dependent (quadratic) demand pattern. Every manufacturing sector wants to produce perfect quality items. But in long run process, there may arise different types of difficulties like labor problem, machinery capabilities problems, etc., due to that the machinery systems shift from in-control state to out-of-control state as a result the manufacturing systems produce imperfect quality items. The imperfect items are reworked at a cost to become the perfect one. The rework cost may be reduced by improvements in product Reliability i.e., the production process depend on time and also the Reliability Parameter. We want to determine the optimal product Reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process using Euler–Lagrange theory to build up the necessary and sufficient conditions for optimality of the dynamic variables. Finally, a numerical example is discussed to test the model which is illustrated graphically also.
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a stock dependent inventory model in an imperfect production process
International Journal of Procurement Management, 2010Co-Authors: Biswajit Sarkar, K S Chaudhuri, Shib Sankar SanaAbstract:The paper deals with an economic manufacturing quantity model for stock-dependent demand in an imperfect production process. In long run of a manufacturing process, the system undergoes out-of-control state and the process begins to produce imperfect quality products. The production of imperfect quality items depends on time and Reliability Parameter. These imperfect items are reworked at a cost to restore to its original quality. Moreover, the development cost that varies with Reliability Parameter of the manufacturing system is introduced to reduce the percentage of imperfect quality items. In our model, the unit production cost is a function of Reliability Parameter and production rate. The profit function is maximised by Euler-Lagrange method by considering different type's costs. The unit production cost and development cost which are functions of Reliability Parameter vary with changes with technology and resources. Concavity of the profit function in the numerical example establishes the existence of the global maximum solution which is graphically illustrated. The inventory/production versus time and optimal development cost versus optimal Reliability are also demonstrated graphically.
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optimal Reliability production lot size and safety stock in an imperfect production system
International Journal of Mathematics in Operational Research, 2010Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:This paper is concerned with the joint determination of optimal production lot size, safety stock and Reliability Parameter under the realistic assumptions that the production facility is subject to random breakdown of machinery system and also to change in the variable Reliability Parameter. Reliability of a machinery system is a decision variable that can be increased by more investment in production technology. In this model, preventive and corrective maintenance, safety stock for repair times and shortages are adopted to generalise the model. Except machinery breakdown, the manufacturing system may shift from in-control state to out-of-control state. During out-of-control state, a certain percentage of total production consists of defective items which can be reworked immediately at a cost to make as good as perfect quality items. The cost function is maximised by Khun-Tucker method. Numerical results are provided to illustrate both the study of the optimal solutions and sensitivity of different changes in key Parameters.
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optimal Reliability production lotsize and safety stock an economic manufacturing quantity model
international journal of management science and engineering management, 2010Co-Authors: Biswajit Sarkar, Shib Sankar Sana, K S ChaudhuriAbstract:Abstract Reliability based maintenance provides sound guidance for managers who wish to attain high standards of maintenance at their operating systems. Basically, the amount and the type of maintenance applied for the system depend strongly on the age of components of a machinery system. The present paper describes a production policy (resumption and non-resumption) in order to find out an optimal safety stock, production lotsize and Reliability Parameters. A production-inventory model with stochastic machine breakdown and its corrective and regular (preventive) repairs with safety stock has been developed incorporating variable Reliability Parameters. The Reliability Parameter (θ) is a design variable. Learning effects result in the cost of technology for the design variable which is an decreasing function of (θ) and this, in turn, has an impact on the average cost. Hence the setting of optimal values of Reliability Parameters and production lotsize with safety stock needs to be considered jointly that ...
Benabed K. - One of the best experts on this subject based on the ideXlab platform.
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Planck intermediate results: LV. Reliability and thermal properties of high-frequency sources in the Second Planck Catalogue of Compact Sources
'EDP Sciences', 2020Co-Authors: Akrami Y., Ashdown M., Aumont J., Baccigalupi C., Ballardini M., Banday A. J., Barreiro R. B., Bartolo N., Basak S., Benabed K.Abstract:International audienceWe describe an extension of the most recent version of the Planck Catalogue of Compact Sources (PCCS2), produced using a new multi-band Bayesian Extraction and Estimation Package (BeeP). BeeP assumes that the compact sources present in PCCS2 at 857 GHz have a dust-like spectral energy distribution (SED), which leads to emission at both lower and higher frequencies, and adjusts the Parameters of the source and its SED to fit the emission observed in Planck’s three highest frequency channels at 353, 545, and 857 GHz, as well as the IRIS map at 3000 GHz. In order to reduce confusion regarding diffuse cirrus emission, BeeP’s data model includes a description of the background emission surrounding each source, and it adjusts the confidence in the source Parameter extraction based on the statistical properties of the spatial distribution of the background emission. BeeP produces the following three new sets of Parameters for each source: (a) fits to a modified blackbody (MBB) thermal emission model of the source; (b) SED-independent source flux densities at each frequency considered; and (c) fits to an MBB model of the background in which the source is embedded. BeeP also calculates, for each source, a Reliability Parameter, which takes into account confusion due to the surrounding cirrus. This Parameter can be used to extract sub-samples of high-frequency sources with statistically well-understood properties. We define a high-Reliability subset (BeeP/base), containing 26 083 sources (54.1% of the total PCCS2 catalogue), the majority of which have no information on Reliability in the PCCS2. We describe the characteristics of this specific high-quality subset of PCCS2 and its validation against other data sets, specifically for: the sub-sample of PCCS2 located in low-cirrus areas; the Planck Catalogue of Galactic Cold Clumps; the Herschel GAMA15-field catalogue; and the temperature- and spectral-index-reconstructed dust maps obtained with Planck’s Generalized Needlet Internal Linear Combination method. The results of the BeeP extension of PCCS2, which are made publicly available via the Planck Legacy Archive, will enable the study of the thermal properties of well-defined samples of compact Galactic and extragalactic dusty sources
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Planck intermediate results
'EDP Sciences', 2020Co-Authors: Akrami Y., Ashdown M., Aumont J., Baccigalupi C., Ballardini M., Banday A. J., Barreiro R. B., Bartolo N., Basak S., Benabed K.Abstract:We describe an extension of the most recent version of the Planck Catalogue of Compact Sources (PCCS2), produced using a new multi-band Bayesian Extraction and Estimation Package (BeeP). BeeP assumes that the compact sources present in PCCS2 at 857 GHz have a dust-like spectral energy distribution (SED), which leads to emission at both lower and higher frequencies, and adjusts the Parameters of the source and its SED to fit the emission observed in Planck's three highest frequency channels at 353, 545, and 857 GHz, as well as the IRIS map at 3000 GHz. In order to reduce confusion regarding diffuse cirrus emission, BeeP's data model includes a description of the background emission surrounding each source, and it adjusts the confidence in the source Parameter extraction based on the statistical properties of the spatial distribution of the background emission. BeeP produces the following three new sets of Parameters for each source: (a) fits to a modified blackbody (MBB) thermal emission model of the source; (b) SED-independent source flux densities at each frequency considered; and (c) fits to an MBB model of the background in which the source is embedded. BeeP also calculates, for each source, a Reliability Parameter, which takes into account confusion due to the surrounding cirrus. This Parameter can be used to extract sub-samples of high-frequency sources with statistically well-understood properties. We define a high-Reliability subset (BeeP/base), containing 26 083 sources (54.1% of the total PCCS2 catalogue), the majority of which have no information on Reliability in the PCCS2. We describe the characteristics of this specific high-quality subset of PCCS2 and its validation against other data sets, specifically for: the sub-sample of PCCS2 located in low-cirrus areas; the Planck Catalogue of Galactic Cold Clumps; the Herschel GAMA15-field catalogue; and the temperature- and spectral-index-reconstructed dust maps obtained with Planck's Generalized Needlet Internal Linear Combination method. The results of the BeeP extension of PCCS2, which are made publicly available via the Planck Legacy Archive, will enable the study of the thermal properties of well-defined samples of compact Galactic and extragalactic dusty sources.Peer reviewe
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Planck intermediate results. LV. Reliability and thermal properties of high-frequency sources in the Second Planck Catalogue of Compact Sources
'EDP Sciences', 2020Co-Authors: Akrami Y., Ashdown M., Aumont J., Baccigalupi C., Ballardini M., Banday A. J., Barreiro R. B., Bartolo N., Basak S., Benabed K.Abstract:We describe an extension of the most recent version of the Planck Catalogue of Compact Sources (PCCS2), produced using a new multi-band Bayesian Extraction and Estimation Package (BeeP). BeeP assumes that the compact sources present in PCCS2 at 857 GHz have a dust-like spectral energy distribution (SED), which leads to emission at both lower and higher frequencies, and adjusts the Parameters of the source and its SED to fit the emission observed in Planck’s three highest frequency channels at 353, 545, and 857 GHz, as well as the IRIS map at 3000 GHz. In order to reduce confusion regarding diffuse cirrus emission, BeeP’s data model includes a description of the background emission surrounding each source, and it adjusts the confidence in the source Parameter extraction based on the statistical properties of the spatial distribution of the background emission. BeeP produces the following three new sets of Parameters for each source: (a) fits to a modified blackbody (MBB) thermal emission model of the source; (b) SED-independent source flux densities at each frequency considered; and (c) fits to an MBB model of the background in which the source is embedded. BeeP also calculates, for each source, a Reliability Parameter, which takes into account confusion due to the surrounding cirrus. This Parameter can be used to extract sub-samples of high-frequency sources with statistically well-understood properties. We define a high-Reliability subset (BeeP/base), containing 26 083 sources (54.1% of the total PCCS2 catalogue), the majority of which have no information on Reliability in the PCCS2. We describe the characteristics of this specific high-quality subset of PCCS2 and its validation against other data sets, specifically for: the sub-sample of PCCS2 located in low-cirrus areas; the Planck Catalogue of Galactic Cold Clumps; the Herschel GAMA15-field catalogue; and the temperature- and spectral-index-reconstructed dust maps obtained with Planck’s Generalized Needlet Internal Linear Combination method. The results of the BeeP extension of PCCS2, which are made publicly available via the Planck Legacy Archive, will enable the study of the thermal properties of well-defined samples of compact Galactic and extragalactic dusty sources
Umesh Singh - One of the best experts on this subject based on the ideXlab platform.
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bayesian estimation of r p y x r p y x for inverse lomax distribution under progressive type ii censoring scheme
International Journal of Systems Assurance Engineering and Management, 2019Co-Authors: Sanjay Singh, Umesh SinghAbstract:In this article, we propose the estimation procedure to estimate the stress-strength Reliability Parameter $$R=P[Y
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estimation of stress strength Reliability for inverse weibull distribution under progressive type ii censoring scheme
Journal of Industrial and Production Engineering, 2018Co-Authors: Abhimanyu Singh Yadav, Sanjay Kumar Singh, Umesh SinghAbstract:This paper aims to estimate the stress–strength Reliability Parameter R = P(Y < X) using progressive type-II censored data, when X and Y are two independent inverse Weibull variates with different ...
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on the estimation of stress strength Reliability Parameter of inverted exponential distribution
International Journal of Advances in Scientific Research, 2015Co-Authors: Sanjay Singh, Umesh Singh, Abhimanyu Singh Yadav, Pradeep Kumar ViswkarmaAbstract:This paper aims to estimate the stress-strength Reliability Parameter R = P(Y < X), when X and Y are independent inverted exponential random variable. We have also discussed some fundamental properties of the considered distribution. The maximum likelihood estimator (MLE) of R and its asymptotic distribution are obtained. The Bayesian estimation of the Reliability Parameter has been also discussed under the assumption of independent gamma prior. Numerical integration technique is used for Bayesian computation. The proposed estimators are compared in terms of their mean squared errors through the simulation study. Two real data sets representing survival of head and neck cancer patients are fitted using the inverted exponential distribution and used to estimate the stress-strength Parameters and Reliability.
