Multiserver Queues with Finite Capacity and Setup Time

2015 ◽  
pp. 173-187 ◽  
Author(s):  
Tuan Phung-Duc
Keyword(s):  
Setup Time ◽  
Finite Capacity ◽  
Multiserver Queues

Transient State Analysis of Queue-Size Behavior and Throughput of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Closedown Times

2015 ◽  
Vol 809-810 ◽  
pp. 1438-1443
Author(s):  
Iwona Paprocka ◽  
Wojciech M. Kempa ◽  
Damian Krenczyk
Keyword(s):  
Production System ◽  
Buffer Capacity ◽  
Transient State ◽  
Setup Time ◽  
Idle Time ◽  
Finite Buffer ◽  
Production Task ◽  
Finite Capacity ◽  
Manufacturing Line ◽  
Waiting Line

A single machine queuing system with setup/closedown times generally distributed is considered. Production tasks enter the production system with exponentially distributed interarrival times and are served by times assumed to be generally distributed. Arriving production tasks form a single waiting line and are served in the order of their arrivals. A production task is stored in a finite-capacity buffer if it arrives and the machine is busy or setup activities are done. Whenever a production system is empty, the machine is stopped by a closedown time. The machine needs a setup time before providing the service of the first task after the idle time.

scholarly journals The asymptotic variance of departures in critically loaded queues

2011 ◽  
Vol 43 (01) ◽  
pp. 243-263 ◽  
Author(s):  
A. Al Hanbali ◽  
M. Mandjes ◽  
Y. Nazarathy ◽  
W. Whitt
Keyword(s):  
Explicit Expression ◽  
Counting Process ◽  
Asymptotic Variance ◽  
Arrival Rate ◽  
Finite Capacity ◽  
System Load ◽  
Multiserver Queues ◽  
Service Patterns ◽  
Coefficients Of Variation ◽  
Service Times

We consider the asymptotic variance of the departure counting process D(t) of the GI/G/1 queue; D(t) denotes the number of departures up to time t. We focus on the case where the system load ϱ equals 1, and prove that the asymptotic variance rate satisfies lim t→∞varD(t) / t = λ(1–2/π)(c a 2 + c s 2), where λ is the arrival rate, and c a 2 and c s 2 are squared coefficients of variation of the interarrival and service times, respectively. As a consequence, the departures variability has a remarkable singularity in the case in which ϱ equals 1, in line with the BRAVO (balancing reduces asymptotic variance of outputs) effect which was previously encountered in finite-capacity birth-death queues. Under certain technical conditions, our result generalizes to multiserver queues, as well as to queues with more general arrival and service patterns. For the M/M/1 queue, we present an explicit expression of the variance of D(t) for any t.

scholarly journals The asymptotic variance of departures in critically loaded queues

2011 ◽  
Vol 43 (1) ◽  
pp. 243-263 ◽  
Author(s):  
A. Al Hanbali ◽  
M. Mandjes ◽  
Y. Nazarathy ◽  
W. Whitt
Keyword(s):  
Explicit Expression ◽  
Counting Process ◽  
Asymptotic Variance ◽  
Arrival Rate ◽  
Finite Capacity ◽  
System Load ◽  
Multiserver Queues ◽  
Service Patterns ◽  
Coefficients Of Variation ◽  
Service Times

We consider the asymptotic variance of the departure counting processD(t) of the GI/G/1 queue;D(t) denotes the number of departures up to timet. We focus on the case where the system load ϱ equals 1, and prove that the asymptotic variance rate satisfies limt→∞varD(t) /t= λ(1–2/π)(ca2+cs2), where λ is the arrival rate, andca2andcs2are squared coefficients of variation of the interarrival and service times, respectively. As a consequence, the departures variability has a remarkable singularity in the case in which ϱ equals 1, in line with the BRAVO (balancing reduces asymptotic variance of outputs) effect which was previously encountered in finite-capacity birth-death queues. Under certain technical conditions, our result generalizes to multiserver queues, as well as to queues with more general arrival and service patterns. For the M/M/1 queue, we present an explicit expression of the variance ofD(t) for anyt.

Stochastic control over finite capacity channels: Causality and feedback

2009 ◽  
Author(s):  
Charalambos D. Charalambous ◽  
Christos K. Kourtellaris ◽  
Photios Stavrou
Keyword(s):  
Stochastic Control ◽  
Finite Capacity

Designing an Energy Efficient Scheme in MANET using Reconfigurable Directional (RDA) Algorithm

2018 ◽  
Vol 8 (4) ◽  
pp. 92
Author(s):  
Rinkle Chhabra ◽  
Anuradha Saini
Keyword(s):  
Ad Hoc ◽  
Energy Savings ◽  
Setup Time ◽  
Directional Antennas ◽  
Directional Antenna ◽  
Efficient Scheme ◽  
Primary Focus ◽  
Hoc Networks ◽  
Energy Gains ◽  
Insight Into

Mobile Ad Hoc Networks (MANET) are autonomous, infrastructure less and self-configuring networks. MANETs has gained lots of popularity due to on the fly deployment i.e. small network setup time and ability to provide communication in obstreperous terrains. Major challenges in MANETs include routing, energy efficiency, network topology control, security etc. Primary focus in this article is to provide method and algorithm to ensure significant energy savings using re-configurable directional antennas. Significant energy gains can be clinched using directional antenna. Key challenges while using directional antenna are to find destination location, antenna focusing, signal power and distance calculations. Re-configurable directional antenna can ensure significant energy gains if used intelligently. This article provides a brief insight into improved energy savings using re-configurable directional antennas and an associated algorithm

Finite Capacity Queue with Postponed Customers

2018 ◽  
Vol 9 (11) ◽  
pp. 1671-1680
Author(s):  
Rinsy Thomas ◽  
Susha D.
Keyword(s):  
Finite Capacity ◽  
Finite Capacity Queue

MINIMALISASI BIAYA SIMPAN DAN BIAYA SETUP PADA MULTIPLE PRODUK: SIMULASI DENGAN CAPACITATED LOT SIZING PROBLEM

2019 ◽  
Vol 4 (2) ◽  
pp. 205-214
Author(s):  
Erika Fatma
Keyword(s):  
Production Cost ◽  
Lot Sizing ◽  
Setup Time ◽  
Production Costs ◽  
Total Production ◽  
Product Cycle ◽  
Lot Size ◽  
Setup Costs ◽  
Lot Sizing Problem ◽  
Capacitated Lot Sizing

Lot sizing problem in production planning aims to optimize production costs (processing, setup and holding cost) by fulfilling demand and resources capacity costraint. The Capacitated Lot sizing Problem (CLSP) model aims to balance the setup costs and inventory costs to obtain optimal total costs. The object of this study was a plastic component manufacturing company. This study use CLSP model, considering process costs, holding costs and setup costs, by calculating product cycle and setup time. The constraint of this model is the production time capacity and the storage capacity of the finished product. CLSP can reduce the total production cost by 4.05% and can reduce setup time by 46.75%.  Keyword: Lot size, CLSP, Total production cost.

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