Queuing Approach Research Articles

A Multi-Stage Queue Approach to Solving Customer Congestion Problem in a Restaurant

The multistage queue model was developed for a situation where parallel and unrelated queues exist at the first stage only. These queues merged into single queues at the remaining stages. The parallel queues offer services that are different from one another and customers arrive to join the queue that offer services that they need. The mathematical model was developed assuming an M/M/1 queue system and the measures of effectiveness were derived. The model was applied to solve the problem of customer congestion in a restaurant in the city of Ibadan, Nigeria that serves three different local delicacies. The three local delicacies constitute three different queues at the first stage. The second stage consists of only one queue which is for purchase of drinks and the third stage which is the last stage is for payment. Every customer in the restaurant passes through the three stages. Utilization factors for the five queues were determined and found to range from 70% to 97%. The average time spent by customers in the system was found to be 543.04 minutes. A simulation study using what-if scenario analysis was performed to determine the optimum service configuration for the system. The optimum configuration reduced average time for customers in the system from 543.04 minutes to 13.47 minutes without hiring new servers.

Pemodelan Antrian Sistem Pengambilan Pesanan Produk Pada Gudang Minuman Ringan Dengan Sistem Rak Drive-In

This paper proposed a queue model for beverage order picking system produced from agricultural commodity in warehouse with drive-in rack system in order to analyze and increase the warehouse performance. A (M/M/C):(GD/N/∞) queue approach was used to build the model with the queue approach. This paper considered total operator must-be-assigned to improve queue time (Wq), queue length, and utilization in the warehouse. Stack, queue, linked list, Colour HSB (Hue, Saturation, Brightness) algorithms were used to represent drive-in rack, product queuing, and product status in the warehouses. The model was implemented in computer simulation using java programming. After build the model, a real case industrial problem was presented to test the model. The model could calculate warehouse performance including low performance in racking, transportation to loading area, and loading process for current number of operators and idle operator in the warehouse process. This result was in line with the industry condition that has over capacity and high product bottlenecks in the warehouse. The model was used to find and analyze total operator-must-assigned to increase the warehouse performance. The computer simulation showed colour changing based on density of product stack in queue area. This colour degradation helped for easier understanding and accelerating product status detection (including hold, ready-to-release, and expired beverage products in the warehouse). Keywords:beverage warehousing, drive-in rack, order picking, queue modelling, queueing theory

Using Robust Queueing to Expose the Impact of Dependence in Single-Server Queues

Queueing applications are often complicated by dependence among interarrival times and service times. Such dependence is common in networks of queues, where arrivals are departures from other queues or superpositions of such complicated processes, especially when there are multiple customer classes with class-dependent service-time distributions. We show that the robust queueing approach for single-server queues proposed in the literature can be extended to yield improved steady-state performance approximations in the standard stochastic setting that includes dependence among interarrival times and service times. We propose a new functional robust queueing formulation for the steady-state workload that is exact for the steady-state mean in the M/GI/1 model and is asymptotically correct in both heavy traffic and light traffic. Simulation experiments show that it is effective more generally. The online appendix is available at https://doi.org/10.1287/opre.2017.1649 .

On a Vacation Queue Approach to Queue Size Computation for a Signalized Traffic Intersection

We propose a vacation queue model for a signalized traffic intersection to elucidate intra-cycle queue size variations. Each incoming street to the intersection is modeled to experience Poisson arrivals, and to have finite queue capacity; vehicles arriving to full queue are dropped. Under a fixed-time policy, the queue size dynamics on each street are decoupled. Motivated by vacation queues, an imbedded Markov chain corresponding to queue sizes at the end of cycles is considered, whose transition probabilities are computed from analytical transient solutions of M/D/1/N queues. Sufficient conditions for convergence to steady-state distributions are provided for fixed-time policy. Simulations suggest consistency between the queue sizes computed by the proposed model, and the Webster and time-dependent traffic queue models.

Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times

This paper presents a joint location-inventory model for the network design of a supply chain with multiple Distribution Centers (DCs) and retailers. The developed model determines the number and location of DCs, the assignment of retailers to DCs, and the size and timing of orders for each DC. The uncertain natures of demands and replenishment lead times are incorporated into the model utilizing a queuing approach. To solve the presented model for large size problems, a hybrid solution algorithm based on simulated annealing and direct search method is adopted. The comparative analysis of the numerical results provides important modelling insights. Particularly, we demonstrate numerically that the cost savings obtained from the queuing approach can be significant.

A control-chart-based queueing approach for service facility maintenance with energy-delay tradeoff

Maintenance planning and energy consumption control are critical issues in facility operations management. In practice, the energy consumption of a facility, which will be affected by the operation condition, is closely connected with the associated maintenance policy. Specifically, for an energy-consuming service system, though a frequent maintenance activity can keep the facility in a good condition with low energy consumption, it makes the delay time longer and leads to a poor customer experience. In this paper, we study a single-server queueing system with different energy consumption levels in the associated running states to address the conflict between energy consumption and customer delay. Two types of maintenance activities are implemented for the server, i.e., the planned maintenance and the reactive maintenance. The planned maintenance is adopted based on a frequency parameter at the beginning of an idle period, and the reactive maintenance is initialized by the Shewhart’s individual control chart (condition-based maintenance). To capture the energy-delay tradeoff, our objective is to develop an optimal maintenance policy that minimizes the long-run expected total cost of the system under a customer waiting time constraint. Numerical experiments are conducted to analyze the problem, in which useful managerial insights are obtained for the optimal maintenance policy. The results demonstrate the robustness of the proposed maintenance model, its advantage over the model without control chart, and its applicability in general situations.

A queuing approach to model power and performance trade-off with maintenance overhead of an infrastructure-as-a-service cloud

The recently developed computing paradigm is the cloud with sophisticated design and enormous resource sharing. In a cloud data centre, a user request undergoes many stages from the time of submission to adequately served. The components of automated provisioning mechanism and dynamically changing environment make the performance analysis more difficult. We present a model that spotlights on important aspects of infrastructure-as-a-service cloud, from the time resource virtualisation to request servicing in a pool-based data centre. It analyses the equilibrium management, capacity planning considering multiple aspects through the given parameters viz., job rejection probability, system overhead, energy management, request completion time. Subsequent attempts made to generalise the data centre model. Exhaustive simulation results presented to validate proposal.

A queuing approach to model power and performance trade-off with maintenance overhead of an infrastructure-as-a-service cloud

A queuing approach to model power and performance trade-off with maintenance overhead of an infrastructure-as-a-service cloud

Multi-Objective Soft Computing-Based Approaches to Optimize Inventory-Queuing-Pricing Problem under Fuzzy Considerations

Due to uncertain environment, various parameters such as price, queuing length, warranty, and so on influence on inventory models. In this paper, an inventory-queuing-pricing problem with continuous review inventory control policy and batch arrival queuing approach, is presented. To best of our knowledge, (I) demand function is stochastic and price dependent; (II) due to the uncertainty in real-world situations, a fuzzy programming approach is applied. Therefore, the presented model with goal of maximizing total profit of system analyzes the price and order quantity decision variables. Since the proposed model belongs to NP-hard problems, Pareto-based approaches based on non-dominated ranking and sorting genetic algorithm are proposed and justified to solve the model. Several numerical illustrations are generated to demonstrate the model validity and algorithms performance. The results showed the applicability and robustness of the proposed soft-computing-based approaches to analyze the problem.

Occupancy modelling in shared spaces of buildings: a queueing approach

This paper describes the development of a queueing model for the simulation of occupancy patterns in shared spaces of buildings. Specifically, occupancy is modelled via an infinite-server queue with time-varying arrival and departure rates. In order to better capture the abrupt changes in occupancy, we also present an algorithm that efficiently learns the locally homogeneous intervals from the data and estimates the model parameters separately on each learned interval. Evaluated on the real-world occupancy data, the model has proved its capability of realistically reproducing the variations of occupancy, as well as the key properties, such as peak occupancy time, first arrival and last departure times, and occupied duration. We also compare our model with several occupancy models in the previous work, and show that our model is preferable in terms of simple structure, agile construction, minimal effort of manual calibration and the ability to reflect the occupancy patterns truthfully.

A queueing approach to a multi class [formula omitted] make-to-stock with backlog

This paper studies an M/G/1 production system serving several customer classes. We show that the Multilevel Rationing (MR) policy, that has been shown to be optimal in the M/M/1 case is not optimal in general. We propose another policy, which we call the extended MR (EMR). The EMR policy exploits the information on the number of all waiting customers at arrival epochs to assess the residual service time. We establish conditions under which the EMR policy is optimal and extend the conditions under which the MR policy is optimal.

Burst‐by‐burst dynamic bandwidth allocation for XG‐PONs

The authors propose and evaluate the performance of a dynamic bandwidth allocation algorithm, namely burst-by-bUrst dynamic bandwidth allocation (BUDA), which enables the fair and efficient utilisation of the available upstream resources in 10-Gigabit-capable passive optical networks (XG-PONs). The proposed algorithm emulates a bit-by-bit burst multiplexing process based on the fair queuing scheduling principle to fairly allocate the available upstream bandwidth among several end-users with varying traffic demands. The fair queuing approach is well-suited to the strictly synchronous communication model of XG-PONs, where fixed duration downstream and upstream frames are utilised to delineate transmissions, and the authors demonstrate via simulations that BUDA achieves low and bounded latency, as well as resource allocation fairness over a set of different traffic loads. Furthermore, BUDA is compared with well-known bandwidth allocation algorithms and a significant latency and fairness benefit is demonstrated.

Performance analysis of cognitive radio networks under spectrum sharing using queuing approach

Cognitive radio is considered to be an emerging technology that provides a platform to share the spectrum between primary and secondary users. As a result, the quality of service and the network usage is bound to improve. In order to evaluate the performance of such a system queuing theory can be considered as an important tool. In the aforementioned context, the central focus of this work is on evaluating and analyzing the network performance using queuing approach. This approach uses the Dimensional Reduction Scheme to reduce the dimension of the Markov chain and the matrix-analytic method for calculating the stationary probabilities. The performance of the system is evaluated on four performance measures namely, utilization, mean response time, mean number of jobs and waiting time. The simulation results show that the performance of the system is satisfactory when primary and secondary users share the network.

Application of Dynamic Instance Queuing to Activity Sequences in Cooperative Business Process Scenarios

The optimization of their business processes is a crucial challenge for many enterprises. This applies especially for organizations using complex cooperative information systems to support human work, production lines, or computing services. Optimizations can touch different aspects such as costs, throughput times, and quality. Nowadays, improvements in workflows are mostly achieved by restructuring the process model. However, in many applications there is a huge potential for optimizations during runtime as well. This holds particularly true for collaborative processes with critical activities, i.e. activities that require a high setup or changeover time, typically leading to waiting queues in instance processing. What is usually suggested in this situation is to bundle several instances in order to execute them as a batch. How the batching is achieved, however, has been only decided on static rules so far. In this paper, we feature dynamic instance queuing (DIQ) as an approach towards clustering and batching instances based on the current conditions in the process, e.g. attribute values of the instances. Specifically, we extend our previous work on applying DIQ at single activities towards a queuing approach that spans activity sequences (DIQS). The approach is evaluated based on a real-world case study from the manufacturing domain. We discuss limitations and further applications of the DIQ idea, e.g. with respect to collaborative human tasks.

SURVEY ON EFFICIENT PLUG-IN HYBRID VEHICLE CHARGING

Our future depends upon the renewable energy sources. Since many years vehicles have relied on combustion fuel like oil and diesel, which will create problem in near future as there are limited reserves of fossil fuels. Hence the most important fact is to preserve energy. So, plug-in hybrid vehicle (PHEV) is good and effective solution for eco-friendly transportation system. Therefore, in this paper a plug-in hybrid vehicle approach is presented along with portable solar panel mounted on it to charge battery. As solar panel is less efficient, a new technique called external light trapping is presented. A 3D printed parabolic concentrator is used for this purpose. It redirects reflected photons back to solar cells and hence due to multiple reflections its power conversion efficiency increases. If in any case, battery run out of charge then to charge it mobile chargers (MC) are used. An android application is developed to support PHEV mobility. It provides full support to driver through its various functions like monitoring of battery, prediction of range it can travel with current battery state and its location. The mobile server based on queuing approach determines design parameter for such mobile charging system. An NJN (nearest-job-next) strategy is used to serve this purpose. In NJN, MC serves next closest PHEV when its current job is done. Moreover, driver can reserve charging slot based on their availability. So, in short our main purpose is to reduce carbon dioxide emission and mitigate PHEV driver range anxiety problem.

The finite/infinite horizon ruin problem with multi-threshold premiums: a Markov fluid queue approach

We present a new numerical method to obtain the finite- and infinite-horizon ruin probabilities for a general continuous-time risk problem. We assume the claim arrivals are modeled by the versatile Markovian arrival process, the claim sizes are PH-distributed, and the premium rate is allowed to depend on the instantaneous risk reserve in a piecewise-constant manner driven by a number of thresholds, i.e., multi-threshold premiums. We introduce a novel sample path technique by which the ruin problems are shown to reduce to the steady-state solution of a certain multi-regime Markov fluid queue. We propose to use the already existing numerically efficient and stable numerical algorithms for such Markov fluid queues. Numerical results are presented to validate the effectiveness of the proposed method regarding the computation of the finite- and infinite-horizon ruin probabilities for risk models including those with relatively large number of thresholds.

A Group-Ordered Fast Iterative Method for Eikonal Equations

In the past decade, many numerical algorithms for the Eikonal equation have been proposed. Recently, the research of Eikonal equation solver has focused more on developing efficient parallel algorithms in order to leverage the computing power of parallel systems, such as multi-core CPUs and GPUs (Graphics Processing Units). In this paper, we introduce an efficient parallel algorithm that extends Jeong et al . ’s FIM (Fast Iterative Method, [1] ), originally developed for the GPU, for multi-core shared memory systems. First, we propose a parallel implementation of FIM using a lock-free local queue approach and provide an in-depth analysis of the parallel performance of the method. Second, we propose a new parallel algorithm, Group-Ordered Fast Iterative Method (GO-FIM) , that exploits causality of grid blocks to reduce redundant computations, which was the main drawback of the original FIM. In addition, the proposed GO-FIM method employs clustering of blocks based on the updating order where each cluster can be updated in parallel using multi-core parallel architectures. We discuss the performance of GO-FIM and compare with the state-of-the-art parallel Eikonal equation solvers.

Applying queuing approach for a stochastic location-inventory problem with two different mean inventory considerations

This paper studies a three-level supply chain network, which includes a single supplier, multiple potential distribution centers (DCs) and multiple retailers. The problem is to optimize the facility location, allocation retailers’ demands, and inventory replenishment decisions simultaneously such that the total expected cost of location, transportation and inventory are minimized. In order to make the problem more realistic, we consider uncertain demand and lead-time, which follow Poisson and Exponential distributions, respectively. Hence, a queueing approach is used to obtain the amount of annual ordering, purchase and shortage as well as the mean inventory in the steady-state condition. Then, according to the results of queuing analysis, we propose a mixed integer nonlinear programming model (MINLP) to address the location-inventory problem. Moreover, the expected mean inventory is calculated using two different methods and the results are also compared through different criteria.

Enhancing TCP Fairness in Wireless Networks using Dual Queue Approach with Optimal Queue Selection

Fairness is one of the important issues when Transmission Control Protocol is used in an asymmetric wireless network. The throughput of uplink or downlink flow abruptly degrades when there is a simultaneous uplink and downlink TCP flow. The per-flow queuing set up to improve the TCP fairness is very difficult to manage for all the uplink and downlink flows and the scalability is degraded. To resolve such constraints, a novel and pragmatic dual queue approach combined with Optimal Queue Selection, TCP-Optimal Queue Selection, is proposed. The two different queues, one for the TCP data packet and the other for the Acknowledgement packet are used. The optimal queue is selected based on the priority or probability, to achieve fairness. The optimal queue size and the queuing delay of the Acknowledgement queue for probability based scheduling are derived and the behavior of dual queue scheme is analyzed. The proposed work is also evaluated using simulation and the results obtained show that dual queue schemes are effective in mitigating the unfairness issue of TCP.

AN EFFICIENT COMBINED CALL ADMISSION CONTROL AND SCHEDULING ALGORITHM TO IMPROVE QUALITY OF SERVICE FOR MULTIMEDIA SERVICES OVER NEXT GENERATION WIRELESS NETWORKS

We nominate a weighted service queuing approach, each service queue is allotted a preset priority weight and Call Admission Control scheme for service classes defined in IEEE 802.16 standard. Different group of queues have been implemented to transmit different service classes. Allocation of bandwidth usage of each service classes are relying on maximum data that could take part in connection between different networks. The Quality of Service is achieved by considering throughput and delay as a major parameters along with the corresponding service classes taken into account. The scheduled CAC model with the token bucket mechanism allows increasing the performance of Quality of Service for multimedia services. By applying the weighted queuing approach, the acceptance of new connection request is increased to achieve the better Quality of Service in multimedia services over next generation wireless networks.