scholarly journals On the probability that the kth customer finds an M/M/1 queue empty

1992 ◽  
Vol 24 (1) ◽  
pp. 238-239 ◽  
Author(s):  
Harshinder Singh ◽  
Rameshwar D. Gupta
Keyword(s):  
Busy Period ◽  
Number Of Customers ◽  
The Relationship

A result relating the probability that kth customer finds the system empty to the distribution of the number of customers served in a busy period, for an M/M/1 queue, has been obtained. This relationship is similar to the relationship between the probability that the queue is empty at time t and the distribution of the length of the busy period.

scholarly journals On the probability that the kth customer finds an M/M/1 queue empty

1992 ◽  
Vol 24 (01) ◽  
pp. 238-239 ◽  
Author(s):  
Harshinder Singh ◽  
Rameshwar D. Gupta
Keyword(s):  
Busy Period ◽  
Number Of Customers ◽  
The Relationship

A result relating the probability that kth customer finds the system empty to the distribution of the number of customers served in a busy period, for an M/M/1 queue, has been obtained. This relationship is similar to the relationship between the probability that the queue is empty at time t and the distribution of the length of the busy period.

On the relationship between the distribution of maximal queue length in the M/G/1 queue and the mean busy period in the M/G/1/n queue

1976 ◽  
Vol 13 (1) ◽  
pp. 195-199 ◽  
Author(s):  
Robert B. Cooper ◽  
Borge Tilt
Keyword(s):  
Service Time ◽  
Queue Length ◽  
Busy Period ◽  
Waiting Room ◽  
The Mean ◽  
Number Of Customers ◽  
The Relationship

Takács has shown that, in the M/G/1 queue, the probability P(k | i) that the maximum number of customers present simultaneously during a busy period that begins with i customers present is P(k | i) = Qk–i/Qk, where the Q's are easily calculated by recurrence in terms of an arbitrary Q0 ≠ 0. We augment Takács's theorem by showing that P(k | i) = bk–i/bk, where bn is the mean busy period in the M/G/1 queue with finite waiting room of size n; that is, if we take Q0 equal to the mean service time, then Qn =bn.

On the relationship between the distribution of maximal queue length in the M/G/1 queue and the mean busy period in the M/G/1/n queue

1976 ◽  
Vol 13 (01) ◽  
pp. 195-199 ◽  
Author(s):  
Robert B. Cooper ◽  
Borge Tilt
Keyword(s):  
Service Time ◽  
Queue Length ◽  
Busy Period ◽  
Waiting Room ◽  
The Mean ◽  
Number Of Customers ◽  
The Relationship

Takács has shown that, in the M/G/1 queue, the probability P(k | i) that the maximum number of customers present simultaneously during a busy period that begins with i customers present is P(k | i) = Qk –i /Qk, where the Q's are easily calculated by recurrence in terms of an arbitrary Q 0 ≠ 0. We augment Takács's theorem by showing that P(k | i) = bk –i /bk, where bn is the mean busy period in the M/G/1 queue with finite waiting room of size n; that is, if we take Q 0 equal to the mean service time, then Qn =bn.

Pricing insurance policies with offsetting relationship

2021 ◽  
pp. 1-27
Author(s):  
Hanbali Hamza
Keyword(s):  
Insurance Market ◽  
Optimal Pricing ◽  
Price Discounts ◽  
Insurance Policies ◽  
Life Data ◽  
Pricing Decision ◽  
Pricing Process ◽  
Pairwise Correlations ◽  
Number Of Customers ◽  
The Relationship

Abstract This paper investigates the benefits of incorporating diversification effects into the pricing process of insurance policies from two different business lines. The paper shows that, for the same risk reduction, insurers pricing policies jointly can have a competitive advantage over those pricing them separately. However, the choice of competitiveness constrains the underwriting flexibility of joint pricers. The paper goes a step further by modelling explicitly the relationship between premiums and the number of customers in each line. Using the total collected premiums as a criterion to compare the competing strategies, the paper provides conditions for the optimal pricing decision based on policyholders’ sensitivity to price discounts. The results are illustrated for a portfolio of annuities and assurances. Further, using non-life data from the Brazilian insurance market, an empirical exploration shows that most pairs satisfy the condition for being priced jointly, even when pairwise correlations are high.

The busy period of the E k/G/1 queue by finite waiting room

1975 ◽  
Vol 7 (02) ◽  
pp. 416-430
Author(s):  
A. L. Truslove
Keyword(s):  
Markov Chain ◽  
Busy Period ◽  
Waiting Room ◽  
Queueing Process ◽  
Number Of Customers

For the E k /G/1 queue with finite waiting room the phase technique is used to analyse the Markov chain imbedded in the queueing process at successive instants at which customers complete service, and the distribution of the busy period, together with the number of customers who arrive, and the number of customers served, during that period, is obtained. The limit as the size of the waiting room becomes infinite is found.

The distribution of the maximum number of customers present simultaneously during a busy period for the queueing systems M/G/1 and G/M/1

1967 ◽  
Vol 4 (01) ◽  
pp. 162-179 ◽  
Author(s):  
J. W. Cohen
Keyword(s):  
Busy Period ◽  
Transition Probabilities ◽  
Queueing Systems ◽  
Entrance Time ◽  
Distribution Of The Maximum ◽  
Number Of Customers

The distribution of the maximum number of customers simultaneously present during a busy period is studied for the queueing systems M/G/1 and G/M/1. These distributions are obtained by using taboo probabilities. Some new relations for transition probabilities and entrance time distributions are derived.

A framework to manage business-to-business branding strategies

2019 ◽  
Vol 14 (2) ◽  
pp. 110-122 ◽  
Author(s):  
Fabio Cassia ◽  
Francesca Magno
Keyword(s):  
Design Methodology ◽  
Specific Situation ◽  
Content Type ◽  
The Past ◽  
Branding Strategies ◽  
B2b Branding ◽  
Number Of Customers ◽  
The Relationship ◽  
Business To Business ◽  
Practical Implications

PurposeIn the past decades, a growing body of studies has assessed the importance of brands in business-to-business (B2B) markets. However, until date, a comprehensive understanding of B2B branding strategies is lacking. Hence, the purpose of this paper is to develop a framework to select and manage B2B branding strategies.Design/methodology/approachThis study’s arguments are developed in line with MacInnis’s (2011) guidelines on conceptual contributions in marketing.FindingsAs a result of the arguments of this study, a framework is developed to identify the relationships between the types of B2B contexts and effective B2B branding strategies.Research limitations/implicationsDespite deriving from an extensive analysis of the literature, the framework requires future empirical validation. Moreover, the relationship linking a supplier to its customer is unique, and hence, each supplier should carefully select a branding strategy depending on the specific situation.Practical implicationsThe suggested framework provides actionable insights to inform managers’ decisions about the most effective B2B strategy for their firm, based on the relational complexity (number of customers, intensity of co-production and co-creation, and dyadic vs multiple-actor view).Originality/valueThis is the first study to provide a comprehensive model of B2B branding strategies. Therefore, it contributes to both advance theoretical knowledge and managerial practice.

Superior Customer Value and Network size in Markets Characterized by Network effects

2011 ◽  
pp. 84-97 ◽  
Author(s):  
Fan-Chen Tseng ◽  
Ching-I Teng ◽  
David Chiang
Keyword(s):  
Customer Value ◽  
Network Effects ◽  
Perceived Value ◽  
Network Size ◽  
Network Effect ◽  
Customer Perceived Value ◽  
Business Practitioners ◽  
Winner Take All ◽  
Number Of Customers ◽  
The Relationship

Network effect indicates that the value of connecting to a network is positively associated with the current number of customers connected to that network. Network effect strengthens the strong firms, weakens the weak firms, and may lead to a winner-take-all market. Thus, managing customer perceived value is crucial in markets with network effects. This article models customer perceived value, presents ways to improve the value, and discusses the relationship between customer perceived value and network size. Implications for e-business practitioners are discussed.

scholarly journals Towards innovation, co-creation and customers’ satisfaction: a banking sector perspective

2019 ◽  
Vol 13 (3) ◽  
pp. 311-325 ◽  
Author(s):  
Muhammad Imran Malik ◽  
Rizwan Ahsan
Keyword(s):  
Risk Assessment ◽  
Customer Satisfaction ◽  
Banking Sector ◽  
Equation Modeling ◽  
Access To Information ◽  
Content Type ◽  
Information Risk ◽  
Relationship Of ◽  
Number Of Customers ◽  
The Relationship

Purpose Co-creation fosters customer’s involvement for innovation in products/services and is used as a tool to develop competitive edge for better entrepreneurship. Based on limited evidence, the study aims to examine the factors contributing to the co-creation and the relationship of co-creation with customer satisfaction. Design/methodology/approach A sample of 384 customers from selected banks in Pakistan was selected. The study adopted quantitative, explanatory and cross-sectional research design. Structural equation modeling is used for analysis. Findings The results revealed a positive and significant relationship between co-creation with customer satisfaction. Further results revealed that access to information, risk assessment and transparency have a positive relationship with co-creation for innovation. The study is significant for customers and management of banks to understand the implications of co-creation to increase customer satisfaction. Research limitations/implications Few banks with a small number of customers were selected for the study. Practical implications Managers must consider customer’s access to information, risk assessment and transparency of information as necessary factors for co-creation that foster innovation and entrepreneurial opportunities because co-creation strengthens customer satisfaction. Social implications Adopting the co-creation process brings long-lasting harmony between customers and banks, and customers may consider the banks as being socially responsible by inviting the opinions of their customers. Originality/value Model is re-tested in the context of Pakistani banks with selected variables affecting co-creation for innovation. Moreover, the relationship of co-creation with customer satisfaction is examined.

The Trivariate Distribution of the Maximum Queue Length, the Number of Customers Served and the Duration of the Busy Period for the M/G/1 Queueing System

1969 ◽  
Vol 6 (1) ◽  
pp. 154-161 ◽  
Author(s):  
E.G. Enns
Keyword(s):  
Queue Length ◽  
Busy Period ◽  
Queueing System ◽  
Single Server ◽  
List Type ◽  
Type Number ◽  
Distribution Of The Maximum ◽  
Maximum Queue Length ◽  
Number Of Customers

In the study of the busy period for a single server queueing system, three variables that have been investigated individually or at most in pairs are:1.The duration of the busy period.2.The number of customers served during the busy period.3.The maximum number of customers in the queue during the busy period.

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