Different Classes Of Customers Research Articles

Robustness of the cμ-Rule for an Unreliable Single-Server Two-Class Queueing System with Constant Retrial Rates

We study the robustness of the cμ-rule for the optimal allocation of a resource consisting of one unreliable server to parallel queues with two different classes of customers. The customers in queues can be served with respect to a FIFO retrial discipline, when the customers at the heads of queues repeatedly try to occupy the server at a random time. It is proved that for scheduling problems in the system without arrivals, the cμ-rule minimizes the total average cost. For the system with arrivals, it is difficult directly to prove the optimality of the same policy with explicit relations. We derived for an infinite-buffer model a static control policy that also prescribes the service for certain values of system parameters exclusively for the class-i customers if both of the queues are not empty, with the aim to minimize the average cost per unit of time. It is also shown that in a finite buffer case, the cμ-rule fails.

Digital Transformation in the FinTech Sector

This paper mainly analyzed the specific application of fintech technology in different aspects, and the changes this emerging technology can bring to people's lives. In terms of the payment system, the formation of oligopoly may lead to serious uneven market competition. Although clients with personal advisers are less likely to trust the services of robots, robo-advice can provide better investment advice to ordinary people and expand the benefit group. For P2P lending, this is a new lending model, eliminating the tedious steps and high fees of the middleman. Virtual currency as an original application of blockchain is the financial derivative that threatened the financial money investment market. The author explained how to benefit more consumers through fintech technology, and the progress of technology enables customers of different classes to have better service experience. The digitalization process of financial services promotes economic development and can provide customers with more inclusive and professional financial services. Fintech has gradually changed the pattern of the financial industry, gradually weakened the role of financial intermediaries, and created a new operating system.

Effects of the Queue Discipline on System Performance

Queue systems are essential in the modelling of transport systems. Increasing requirements from the beneficiaries of logistic services have led to a broadening of offerings. Consequently, models need to consider transport entities with priorities being assigned in relation to the costs corresponding to different classes of customers and/or processes. Waiting lines and queue disciplines substantially affect queue system performance. This paper aims to identify a solution for decreasing the waiting time, the total time in the system, and, overall, the cost linked to queueing delays. The influence of queue discipline on the waiting time and the total time in the system is analysed for several cases: (i) service for priority classes at the same rate of service with and without interruptions, and (ii) service for several priority classes with different service rates. The presented analysis is appropriate for increasing the performance of services dedicated to freight for two priority classes. It demonstrates how priority service can increase system performance by reducing the time in the system for customers with high costs. In addition, in the considered settings, the total time in the system is reduced for all customers, which leads to resource savings for system infrastructures.

N , n -Preemptive-Priority M/G/1 Queues with Finite and Infinite Buffers

In this paper, we analyze an M/G/1 priority queueing model with finite and infinite buffers under the N , n -preemptive priority discipline, under which preemption decisions are made based on the number of high-priority customers. This priority queueing model can be used for the performance analysis of communication systems accommodating delay- and loss-sensitive packets simultaneously. To analyze the proposed model, we extend the method of delay cycle analysis and develop a queue length version of it for finite-buffer queues. Throughout our analysis, we demonstrate that by the proposed method the analysis of the complex priority queueing model can be reduced to that of simple delay cycles, so two different preemption modes of the queueing model can be dealt with in a unified way. The numerical study reveals that adjusting the decision variables N and n allows us to fine-tune system performance for different classes of customers, and N operates as a primary control variable, regardless of the preemption mode and service-time distributions.

Admission Control Game with Capacity Borrowing

We study a decentralized admission control problem with capacity borrowing. In this problem, the admission decision is made by each server who is assigned dedicated capacity. However, a server can borrow capacity from other servers if his dedicated capacity is exhausted. The borrower compensates the lender by a payment equal to the “borrowing cost.” Hence, the servers share their capacities via borrowing, but they also compete against each other for their pooled capacities. We formulate this problem as a stochastic dynamic game and characterize the equilibrium strategic admission behavior of servers. We show that the critical factors that affect the equilibrium behavior and the system revenue are the magnitude of the borrowing cost, the arrival rate of customers and the revenue difference between different classes of customers. We have identified conditions under which servers maximizing their own payoffs end up making decisions that benefit the system. Our study highlights the important roles that the borrowing cost plays in shaping servers’ behavior in service systems and provides concrete guidance for practitioners to design a decentralized admission control system.

Reliability and Optimization Measures of Retrial Queue with Different Classes of Customers under a Working Vacation Schedule

A working vacation queueing model is described in this work, with three different classes of customers: regular, priority, and disaster. The regular server serves all arriving customers, whereas the optional reservice is only provided to those who request it. The Bernoulli working vacation (BWV) schedule is considered. In WV time, the server serves at a slower rate. The generating functions (GF) technique is used to determine the system capacity of various server states. Different system performances, reliability indices, and cost optimization values are numerically shown. For the current COVID‐19 pandemic situation, the motivation for this approach is presented in a telephonic communication system.

Admission Control Game with Capacity Borrowing

We study a decentralized admission control problem with capacity borrowing. In this problem, the admission decision is made by each server who is assigned dedicated capacity. However, a server can borrow capacity from other servers if his dedicated capacity is exhausted. The borrower compensates the lender by a payment equal to the ‘‘borrowing cost’’. Hence, the servers share their capacities via borrowing, but they also compete against each other for their pooled capacities. We formulate this problem as a stochastic dynamic game and characterize the equilibrium strategic admission behavior of servers. We show that the critical factors that affect the equilibrium behavior and the system revenue are the magnitude of the borrowing cost, the arrival rate of customers and the revenue difference between different classes of customers. We have identified conditions under which servers maximizing their own payoffs end up making decisions that benefit the system. Our study highlights the important roles that the borrowing cost plays in shaping servers' behavior in service systems and provides concrete guidance for practitioners to design a decentralized admission control system.

A survey on skill-based routing with applications to service operations management

Service systems often feature multiple classes of customers with different service needs and multiple pools of servers with different skillsets. How to efficiently match customers of different classes with servers of different skillsets is of great importance to the management of these systems. In this survey, we review works on skill-based routing in queues. We first summarize key insights on routing/scheduling policies developed in the literature. We then discuss complications brought by modern service operations management problems, particularly healthcare systems. These complications stimulate a growing body of literature on new modeling and analysis tools. Lastly, we provide additional numerical experiments to highlight the complex nature of a routing problem motivated from hospital patient-flow management, and provide some useful intuition to develop good skill-based routing policies in practice. Our goal is to provide a brief overview of the skill-based routing research landscape and to help generate interesting research ideas.

Elective course assignment problem: a revenue management based approach

The current paper aims to provide a novel approach for the assignment of students to elective courses using historical records of course capacities and student demand. With this aim, we establish a link between the course assignment problem and the airline seat allocation modeling of Revenue Management. An analogy is set up relying on the assumption that the students form distinct groups and intend to enroll in the elective courses with limited capacities, which is similar to different classes of customers to purchase tickets for the flights with limited capacities. We illustrate the proposed idea in a real-world case utilizing the historical demand and capacity data of a public university in which the senior students have priority in enrolling. The results of the proposed modeling can serve not only for capacity allocations to the courses but also offers suggestions to evaluate the potential capacity augmentation or reduction decisions.

Dynamic inventory rationing: How to allocate stock according to managerial priorities. An empirical study

In spare part distribution, contracts characterized by different unit prices, lead times, minimum order quantities are usually offered to different types of customers. In this context, one of the main problems is the decision on the allocation of the on–hand inventory to the different customers in order to minimize the total back–order cost. In this paper, we propose two allocation policies that use a dynamic critical level to reserve on-hand inventory for high priority customers. The first policy sets the critical level according to a target service level for high priority demand, while the other sets the critical level according to the back-order costs for the different classes of customers. The effectiveness of the two policies with respect to no rationing or stock piling polices is shown using the data of a European spare part company. Moreover, the relationship between the two policies is evaluated from a managerial perspective.

The difference of mean waiting times between two classes of customers in a single-server FIFO queue: An experimental study

AbstractPrevious studies have shown that the distribution of mean waiting times of different classes of customers is different for each class in a GI/G/1 queue with FIFO discipline. This experimental simulation study is motivated by those results as it investigates which are the conditions causing a difference of mean waiting times between two classes of customers using a FIFO discipline. Results from the study show that having a difference in the squared coefficient of variation of inter-arrival times between classes is the most important factor to determine whether a difference of mean waiting times between two classes exists. Furthermore, if a difference of mean service times between the two classes exists, the class with the highest mean service time will tend to have the smallest mean waiting time between the two classes, resulting in an unfair policy.

An integrated approach for distributed resource allocation and network reconfiguration considering load diversity among customers

Abstract In this paper a new dedicated search teaching learning based optimization (DSTLBO) method is proposed for simultaneous allocation of distributed resources keeping in view the practical aspect of network reconfiguration to maximize annual energy loss reduction while maintaining a better node voltage profiles. The accuracy of distributed resource allocation and their benefits depend on proper modeling of system loads. Distribution system planners usually provide dedicated feeders to different classes of customers; each has its own characteristic load pattern which varies hourly and seasonally. There exists diversity in customers’ load demands. Therefore proper modeling of annual load profile is crucial to obtain realistic allocation of distribution resources (shunt capacitors and distributed generations). In the proposed formulation of the optimization problems the diversity in customers’ load patterns has been taken into account. In the proposed dedicated search teaching learning based optimization (DSTLBO), new learning approaches are suggested to enhance convergence, accuracy and efficiency of the standard TLBO. The proposed method is investigated on the benchmark IEEE 33-bus test distribution system. The application results are presented.

A Method to Evaluate Total Supply Capability of Distribution Systems Considering Network Reconfiguration and Daily Load Curves

The total supply capability (TSC) is an important index for assessing the reliability of a distribution power system. In this paper, two models to evaluate the TSC are established. In the first, the TSC is acquired with the conditions that all load outages can be restored via network reconfiguration with transformers' N-1 contingencies, i.e., that all constraints related to branch thermal ratings and bus-voltage limits can be satisfied following restoration for each N-1 contingency. The second model, which is revision of the first, considers the daily load curves for different classes of customers, e.g., residential, commercial and industrial. Both models can be formulated as mixed integer problems with second-order cone programming (MISOCP), which can be solved using commercially available optimization software. Two test systems are used to demonstrate the applicability of the presented models. Numerical results show that the presented model is more accurate than the previously published models. This proposed analytical approach can be applied in a range of network planning studies, e.g., for selecting appropriate ratings of transformers, or for optimal locating of circuit breakers and distributed energy resources.

Methodology for Estimation of Dynamic Response of Demand Using Limited Data

The paper presents a methodology for estimation of dynamic response of aggregate load at distribution network buses by taking into account daily variation in demand composition and generic dynamic responses of different types of load. Dynamic load models for different load categories obtained from field or laboratory measurements or through appropriate mathematical modeling are used in combination with hourly load composition at the given bus. The load composition is derived based on past demand surveys, and daily loading curves for different classes of customers. Uncertainties in both dynamic load models/responses of individual loads and load compositions at different times of the day are modeled probabilistically. With established dynamic signatures of different load categories and load compositions at different times of the day, Monte Carlo simulations are used to estimate probabilistic real and reactive power responses, including ranges of variation of these responses, for every hour of the day for a given or anticipated mix of demand.

Selection Andservice Quality Perceptions of Software Firms with Bank in Bangalore

This paper explores on the wake of globalization and liberalization along with heightened competition, continuing deregulation and technological advancements. This paper analyses about banks and different classes of customers. Paper describing the corporate customers, segments, their volume and value of dealings with the banks. This paper also focuses on the factors the software sector would consider while selecting their banks and their perceptions regarding quality of services offered by their bankers.

Modeling and evaluating the AVS/RS with tier-to-tier vehicles using a semi-open queueing network

The Autonomous Vehicle Storage and Retrieval System (AVS/RS) with tier-to-tier vehicles is modeled as a semi-open queueing network (SOQN). Different storage/retrieval requests in the AVS/RS are modeled as different classes of customers in the SOQN model. Analyzing multiple configurations of an SOQN via computer simulation is time-consuming. Therefore, this article uses some analytical methods to evaluate the performances of a multi-class, multi-stage SOQN with general service time and interarrival time distributions. Two synchronization policies are also compared and the results show that an AVS/RS with virtual synchronization has a better performance than that with physical synchronization.

Impact of class clustering in a multiclass FCFS queue with order-dependent service times

In multi-class queueing systems, customers of different classes can enter the system. When studying such systems, it is traditionally assumed that the different classes of customers occur randomly and independently in the arrival stream of customers in the system. This is often in contrast to the actual situation. Therefore, we study a multi-class system with so-called class clustering in the customer arrival stream, i.e., (Markovian) correlation occurs in the classes of consecutive customers. The system under investigation consists of one server that is able to serve two classes of customers. In addition, the service-time distribution of a customer depends on the equality or non-equality of its class with the class of the previous customer. This latter feature occurs frequently in practice. For instance, execution of the same task again can lead to both faster or slower processing times. The first case can occur when the execution of a different task entails resetting a machine, or loading new data, et cetera. The opposite situation appears, for instance, when execution of the same task requires postprocessing (such as cooling down or reinitialization of a machine). We deduce the probability generating function (pgf) of the system content, from which we can extract various performance measures, among which the mean values of the system content and the customer delay. We demonstrate that class clustering has a tremendous impact on the system performance, which highlights the necessity to include it in the performance assessment of any system in which it occurs.

Optimal Uniform Pricing Strategy of a Service Firm When Facing Two Classes of Customers

When facing heterogeneous customers, how should a service firm make its pricing decision to maximize revenue? If discrimination is allowed, then priority schemes and differentiated pricing are often used to achieve that. In many applications, however, the firm cannot or is not allowed to set discriminatory prices, for example, list price in retail stores, online shopping, and gas stations; thus a uniform price must be applied to all customers. This study addresses the optimal uniform pricing problem of a service firm using a queueing system with two classes of customers. Our result shows that the potential pool of customers plays a central role in the firm's optimal decision. Depending on the range of system parameters, which are determined explicitly by the primitive data, the firm's optimal strategy may choose to serve only one class of customers, a subset of a class of customers, or a combination of different classes of customers. In addition, the optimal price is in general not monotonic with respect to the potential market sizes because their changes may lead to a major shift in the firm's decision on which customer class to serve. However, unless such a shift occurs, the optimal price is weakly decreasing in the potential market sizes.

CONTAINER TERMINAL AS A QUEENING TRANSPORT NETWORKS

The queuing networks consist of several connected simple queuing system. In container terminal as an open network containers enter the queuing network from sea and landside, received adequately service at system and leave the network. Jackson’s network is simplest form of queuing networks, characterized by unlimited overall numbers of customers belong to the one class of customers. Customer – containers arrived' system with exponential arrival pattern (Poison or Erlang) and are served on single or multi-channel servers with exponential service time. Another case of queuing networks is network of Kelly with different classes of customers (containers) but each class of containers has fixed route (service model) in the transport system. The Baskett, Chandy, Muntz and Palacios – BCMP networks include different class of customers, with different queuing discipline and generally distributed time. Routes (service model) through the network may depend on the class of customers – containers, and the containers can change its class while passing through the system. This article describes different queuing networks, which can be used to modelling of the container terminal as a queuing transport network. The Queuing Theory may be used to determine the appropriate level of capacity required at container terminal and the staffing levels required at service facilities, over the nominal average capacity required to service expected demand. The model of the container terminal called a queuing network had better represent the real structure then a single system.

Waiting time distributions in the accumulating priority queue

We are interested in queues in which customers of different classes arrive to a service facility, and where performance targets are specified for each class. The manager of such a queue has the task of implementing a queueing discipline that results in the performance targets for all classes being met simultaneously. For the case where the performance targets are specified in terms of ratios of mean waiting times, as long ago as the 1960s, Kleinrock suggested a queueing discipline to ensure that the targets are achieved. He proposed that customers accumulate priority as a linear function of their time in the queue: the higher the urgency of the customer's class, the greater the rate at which that customer accumulates priority. When the server becomes free, the customer (if any) with the highest accumulated priority at that time point is the one that is selected for service. Kleinrock called such a queue a time-dependent priority queue, but we shall refer to it as the accumulating priority queue. Recognising that the performance of many queues, particularly in the healthcare and human services sectors, is specified in terms of tails of waiting time distributions for customers of different classes, we revisit the accumulating priority queue to derive its waiting time distributions, rather than just the mean waiting times. We believe that some elements of our analysis, particularly the process that we call the maximum priority process, are of mathematical interest in their own right.