This article,we develop an optimal policy to control the service rate of a discrete time queueing-inventory system with finite buffer.The customers arrive according to a Bernoulli process and the service time for the ...This article,we develop an optimal policy to control the service rate of a discrete time queueing-inventory system with finite buffer.The customers arrive according to a Bernoulli process and the service time for the customers are geometric.Whenever the buffer size attains its maximum,any arriving new customers are considered to be lost.The customers are served one by one according to FCFS rule and each customers request random number of items.The inventory is replenished according to a(s,Q)inventory policy with geometric lead time.The main objectives of this article are to determine the service rates to be employed at each slot so that the long run expected cost rate is minimized for fixed inventory level and fixed buffer size and to minimize the expected waiting time for a fixed inventory level and fixed buffer size.The problems are modelled as Markov decision problem.We establish the existence of a stationary policy and employ linear programming method to find the optimal service rates.We provide some numerical examples to illustrate the behaviour of the model.展开更多
Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine...Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.展开更多
In this article, we present a continuous review （s,S） inventory system with a service facility consisting of finite buffer （capacity N ） and a single server. The customers arrive according to a Poisson process. Th...In this article, we present a continuous review （s,S） inventory system with a service facility consisting of finite buffer （capacity N ） and a single server. The customers arrive according to a Poisson process. The individual customer＇s unit demand is satisfied after a random time of service, which is assumed to be exponential. When the inventory level drops to s＇an order for Q（= S-s） items is placed. The lead time of reorder is assumed to be exponential distribution. An arriving customer, who finds the buffer is full, enters into the pool of infinite size or leaves the system according to a Bernolli trial. At the time of service completion, if the buffer size drops to a preassigned level L （1 〈 L 〈 N） or below and the inventory level is above s, we select the customers from the pool according to two different policy ： in first policy, with probability p （0 〈 p 〈 1） we select the customer from the head of the pool and we place the customer at the end of the buffer; in the second policy, with p （0 〈 p 〈 1） the customer from the pool is transferred to the buffer for immediate service and after completion of his service we provide service to the customer who is in the buffer with probability one. If at a service completion epoch the buffer turns out to be empty, there is at least one customer in the pool and the inventory level is positive, then the one ahead of all waiting in the pool gets transferred to the buffer, and his service starts immediately. The joint probability distribution of the number of customers in the pool, number of customers in the buffer and the inventory level is obtained in the steady-state case. Various stationary system performance measures are computed and total expected cost rate is calculated. A comparative result of two models is illustrate numerically.展开更多
基金The research of Ms.L.Iniya is supported by the DST-INSPIRE Fellowship,New Delhi,research award No.DST/INSPIRE Fellowship/[IF190092].
文摘This article,we develop an optimal policy to control the service rate of a discrete time queueing-inventory system with finite buffer.The customers arrive according to a Bernoulli process and the service time for the customers are geometric.Whenever the buffer size attains its maximum,any arriving new customers are considered to be lost.The customers are served one by one according to FCFS rule and each customers request random number of items.The inventory is replenished according to a(s,Q)inventory policy with geometric lead time.The main objectives of this article are to determine the service rates to be employed at each slot so that the long run expected cost rate is minimized for fixed inventory level and fixed buffer size and to minimize the expected waiting time for a fixed inventory level and fixed buffer size.The problems are modelled as Markov decision problem.We establish the existence of a stationary policy and employ linear programming method to find the optimal service rates.We provide some numerical examples to illustrate the behaviour of the model.
文摘Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.
基金supported by the INSPIRE fellowship,New Delhi,research award No.DST/INSPIRE fellowship/2010/[168],Reg.No.IF1020supported by the Council of Scientific and Industrial Research(CSIR)-India for their financial support(no.25(0813)/10/EMR-II)
文摘In this article, we present a continuous review （s,S） inventory system with a service facility consisting of finite buffer （capacity N ） and a single server. The customers arrive according to a Poisson process. The individual customer＇s unit demand is satisfied after a random time of service, which is assumed to be exponential. When the inventory level drops to s＇an order for Q（= S-s） items is placed. The lead time of reorder is assumed to be exponential distribution. An arriving customer, who finds the buffer is full, enters into the pool of infinite size or leaves the system according to a Bernolli trial. At the time of service completion, if the buffer size drops to a preassigned level L （1 〈 L 〈 N） or below and the inventory level is above s, we select the customers from the pool according to two different policy ： in first policy, with probability p （0 〈 p 〈 1） we select the customer from the head of the pool and we place the customer at the end of the buffer; in the second policy, with p （0 〈 p 〈 1） the customer from the pool is transferred to the buffer for immediate service and after completion of his service we provide service to the customer who is in the buffer with probability one. If at a service completion epoch the buffer turns out to be empty, there is at least one customer in the pool and the inventory level is positive, then the one ahead of all waiting in the pool gets transferred to the buffer, and his service starts immediately. The joint probability distribution of the number of customers in the pool, number of customers in the buffer and the inventory level is obtained in the steady-state case. Various stationary system performance measures are computed and total expected cost rate is calculated. A comparative result of two models is illustrate numerically.
