Post-Earthquake Restoration Planning for Los Angeles Electric Power

2006 ◽  
Vol 22 (3) ◽  
pp. 589-608 ◽  
Author(s):  
Zehra Çağnan ◽  
Rachel A. Davidson ◽  
Seth D. Guikema
Keyword(s):  
Los Angeles ◽  
Electric Power ◽  
Cost Benefit Analysis ◽  
Discrete Event ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Power Outages ◽  
Restoration Process ◽  
Restoration Planning ◽  
Number Of Customers

This paper describes the application of a new discrete-event-simulation model of the post-earthquake electric power restoration process in Los Angeles. The findings are that (1) Los Angeles residents may experience power outages lasting up to 10 days; (2) what we call the power rapidity risk (the joint probability distribution of restoration of a specified number of customers in a specified amount of time) varies throughout the area; (3) there is a relatively high likelihood that more repair materials than are currently available will be required if a large earthquake occurs; and (4) there are ways to reduce the expected duration of earthquake-initiated power outages and they should be subjected to cost-benefit analysis. These results should be useful to utilities and emergency planners in Los Angeles. The new simulation modeling approach could be used in other seismically active cities to gain insights into the restoration process that other modeling approaches cannot provide.

scholarly journals Perishable Inventory System with Postponed Demands and Negative Customers

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Paul Manuel ◽  
B. Sivakumar ◽  
G. Arivarignan
Keyword(s):  
Steady State ◽  
Joint Probability ◽  
Inventory System ◽  
Arrival Process ◽  
Inventory Level ◽  
Joint Probability Distribution ◽  
Time Interval ◽  
Negative Customers ◽  
Perishable Inventory System ◽  
Number Of Customers

This article considers a continuous review perishable (s,S) inventory system in which the demands arrive according to a Markovian arrival process (MAP). The lifetime of items in the stock and the lead time of reorder are assumed to be independently distributed as exponential. Demands that occur during the stock-out periods either enter a pool which has capacity N(<∞) or are lost. Any demand that takes place when the pool is full and the inventory level is zero is assumed to be lost. The demands in the pool are selected one by one, if the replenished stock is above s, with time interval between any two successive selections distributed as exponential with parameter depending on the number of customers in the pool. The waiting demands in the pool independently may renege the system after an exponentially distributed amount of time. In addition to the regular demands, a second flow of negative demands following MAP is also considered which will remove one of the demands waiting in the pool. The joint probability distribution of the number of customers in the pool and the inventory level is obtained in the steady state case. The measures of system performance in the steady state are calculated and the total expected cost per unit time is also considered. The results are illustrated numerically.

scholarly journals A Perishable Inventory System with Postponed Demands and Multiple Server Vacations

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
R. Jayaraman ◽  
B. Sivakumar ◽  
G. Arivarignan
Keyword(s):  
Steady State ◽  
Joint Probability ◽  
Life Time ◽  
Inventory System ◽  
Inventory Level ◽  
Joint Probability Distribution ◽  
Single Server ◽  
Cost Rate ◽  
Stochastic Inventory ◽  
Number Of Customers

A mathematical modelling of a continuous review stochastic inventory system with a single server is carried out in this work. We assume that demand time points form a Poisson process. The life time of each item is assumed to have exponential distribution. We assume(s,S)ordering policy to replenish stock with random lead time. The server goes for a vacation of an exponentially distributed duration at the time of stock depletion and may take subsequent vacation depending on the stock position. The customer who arrives during the stock-out period or during the server vacation is offered a choice of joining a pool which is of finite capacity or leaving the system. The demands in the pool are selected one by one by the server only when the inventory level is aboves, with interval time between any two successive selections distributed as exponential with parameter depending on the number of customers in the pool. The joint probability distribution of the inventory level and the number of customers in the pool is obtained in the steady-state case. Various system performance measures in the steady state are derived, and the long-run total expected cost rate is calculated.

scholarly journals On the Nonsymmetric Longer Queue Model: Joint Distribution, Asymptotic Properties, and Heavy Traffic Limits

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Charles Knessl ◽  
Haishen Yao
Keyword(s):  
Heavy Traffic ◽  
Asymptotic Properties ◽  
Joint Probability ◽  
Integral Representations ◽  
Joint Probability Distribution ◽  
Single Server ◽  
Poisson Arrival ◽  
Large Numbers ◽  
Node Network ◽  
Number Of Customers

We consider two parallel queues, each with independent Poisson arrival rates, that are tended by a single server. The exponential server devotes all of its capacity to the longer of the queues. If both queues are of equal length, the server devotesνof its capacity to the first queue and the remaining1−νto the second. We obtain exact integral representations for the joint probability distribution of the number of customers in this two-node network. Then we evaluate this distribution in various asymptotic limits, such as large numbers of customers in either/both of the queues, light traffic where arrivals are infrequent, and heavy traffic where the system is nearly unstable.

A Stochastic Inventory Model with Multiple Vacations and N - Policy

2016 ◽  
pp. 106-123
Author(s):  
Jeganathan Kathirvel
Keyword(s):  
Poisson Process ◽  
Finite Size ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Stochastic Inventory ◽  
Left Behind ◽  
Stochastic Inventory Model ◽  
Waiting Area ◽  
Perishable Inventory System ◽  
Number Of Customers

We consider a perishable inventory system under continuous review at a service facility in which a waiting area for customers is of finite size. We assume that the replenishment of inventory is instantaneous. The items of inventory have exponential life times. The service starts only when the customer level reaches a prefixed level, starting from the epoch at which no customer is left behind in the system. The arrivals of customers to the service station form a Poisson process. The server goes for a vacation of an exponentially distributed duration whenever the waiting area is zero. The service process is subject to interruptions, which occurs according to a Poisson process. The interrupted server is repaired at an exponential rate. Also the waiting customer independently reneges the system after an exponentially distributed amount of time. The joint probability distribution of the number of customers in the system and the inventory levels is obtained in steady state case. The results are illustrated with numerical examples.

ANALYSIS OF FINITE CAPACITY QUEUE WITH NEGATIVE CUSTOMERS AND BUNKER FOR OUSTED CUSTOMERS USING CHEBYSHEV AND GEGENBAUER POLYNOMIALS

2014 ◽  
Vol 31 (04) ◽  
pp. 1450029 ◽  
Author(s):  
ROSTISLAV V. RAZUMCHIK
Keyword(s):  
Finite Size ◽  
Joint Probability ◽  
Gegenbauer Polynomials ◽  
Joint Probability Distribution ◽  
Finite Capacity ◽  
Negative Customers ◽  
Blocking Probabilities ◽  
Regular Customer ◽  
Number Of Customers ◽  
Finite Capacity Queue

Consideration is given to the queueing system with incoming Poisson flows of regular and negative customers. Regular customers await service in buffer of finite size r. Each negative customer upon arrival pushes a regular customer out of the queue in buffer (if it is not empty) and moves it to another queue of finite capacity r (bunker). Customers from both queues are served according to exponential distribution with parameter μ, first-come, first-served discipline, but customers in bunker are served with relative priority. Using method based on Chebyshev and Gegenbauer polynomials the algorithm for computation of stationary blocking probabilities and joint probability distribution of the number of customers in buffer and bunker is obtained. Numerical example is provided.

scholarly journals Markovian inventory model with two parallel queues, jockeying and impatient customers

2016 ◽  
Vol 26 (4) ◽  
pp. 467-506 ◽  
Author(s):  
K. Jeganathan ◽  
J. Sumathi ◽  
G. Mahalakshmi
Keyword(s):  
Steady State ◽  
Finite Size ◽  
Joint Probability ◽  
Life Time ◽  
The Other ◽  
Inventory Level ◽  
Joint Probability Distribution ◽  
Cost Rate ◽  
Parallel Queues ◽  
Number Of Customers

This article presents a perishable stochastic inventory system under continuous review at a service facility consisting of two parallel queues with jockeying. Each server has its own queue, and jockeying among the queues is permitted. The capacity of each queue is of finite size L. The inventory is replenished according to an (s; S) inventory policy and the replenishing times are assumed to be exponentially distributed. The individual customer is issued a demanded item after a random service time, which is distributed as negative exponential. The life time of each item is assumed to be exponential. Customers arrive according to a Poisson process and on arrival; they join the shortest feasible queue. Moreover, if the inventory level is more than one and one queue is empty while in the other queue, more than one customer are waiting, then the customer who has to be received after the customer being served in that queue is transferred to the empty queue. This will prevent one server from being idle while the customers are waiting in the other queue. The waiting customer independently reneges the system after an exponentially distributed amount of time. The joint probability distribution of the inventory level, the number of customers in both queues, and the status of the server are obtained in the steady state. Some important system performance measures in the steady state are derived, so as the long-run total expected cost rate.

scholarly journals Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiangfu Zhao ◽  
Gianfranco Lamperti ◽  
Dantong Ouyang ◽  
Xiangrong Tong
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Sufficient Conditions ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Control Engineering ◽  
Event Systems ◽  
Active Research ◽  
Necessary And Sufficient ◽  
Static Systems

In the last several decades, the model-based diagnosis of discrete-event systems (DESs) has increasingly become an active research topic in both control engineering and artificial intelligence. However, in contrast with the widely applied minimal diagnosis of static systems, in most approaches to the diagnosis of DESs, all possible candidate diagnoses are computed, including nonminimal candidates, which may cause intractable complexity when the number of nonminimal diagnoses is very large. According to the principle of parsimony and the principle of joint-probability distribution, generally, the minimal diagnosis of DESs is preferable to a nonminimal diagnosis. To generate more likely diagnoses, the notion of the minimal diagnosis of DESs is presented, which is supported by a minimal diagnoser for the generation of minimal diagnoses. Moreover, to either strongly or weakly decide whether a minimal set of faulty events has definitely occurred or not, two notions of minimal diagnosability are proposed. Necessary and sufficient conditions for determining the minimal diagnosability of DESs are proven. The relationships between the two types of minimal diagnosability and the classical diagnosability are analysed in depth.

scholarly journals A Perishable Inventory System with Postponed Demands and Finite

2016 ◽  
Vol 12 (8) ◽  
pp. 6500-6515
Author(s):  
R Jayaraman
Keyword(s):  
Steady State ◽  
Joint Probability ◽  
Life Time ◽  
Inventory System ◽  
Inventory Level ◽  
Joint Probability Distribution ◽  
Single Item ◽  
Perishable Inventory ◽  
Perishable Inventory System ◽  
Number Of Customers

In this article, we consider a continuous review perishable inventory system with a finite number of homogeneous sources generating demands. The demand time points form quasi random process and demand is for single item. The maximum storage capacity is assumed to be The life time of each item is assumed to have exponential distribution. The order policy is policy, that is, whenever the inventory level drops to a prefixed level an order for items is placed. The ordered items are received after a random time which is distributed as exponential. We assume that the demands that occur during the stock out periods either enter a pool or leave the system which is according to a Bernoulli trial. The demands in the pool are selected one by one, while the stock is above the level with interval time between any two successive selections is distributed as exponential. The joint probability distribution of the number of customers in the pool and the inventory level is obtained in the steady state case. Various system performance measures are derived to compute the total expected cost per unit time in the steady state. The optimal cost function and the optimal are studied numerically.

A Perishable Inventory Model with Bonus Service for Certain Customers, Balking and N + 1 Policy

2014 ◽  
Vol 2 (3-4) ◽  
pp. 83-104
Author(s):  
Kathirvel Jeganathan
Keyword(s):  
Steady State ◽  
Joint Probability ◽  
Life Time ◽  
Inventory Level ◽  
Joint Probability Distribution ◽  
Perishable Inventory ◽  
Cost Rate ◽  
Waiting Area ◽  
Waiting Line ◽  
Number Of Customers

AbstractWe consider a perishable inventory system with bonus service for certain customers. The maximum inventory level is S. The ordering policy is (0, S) policy and the lead time is zero. The life time of each items is assumed to be exponential. Arrivals of customers follow a Poisson process with parameter λ. Arriving customers form a single waiting line based on the order of their arrivals. The capacity of the waiting line is restricted to M including the one being served. The first N ≥ 1 customers who arrive after the system becomes empty must wait for service to begin. We have assumed that these N customers received an additional service, called bonus service, in addition to the essential service rendered to all customers. The service times are exponentially distributed. An arriving customer who finds the server busy or waiting area size ≥ N, proceeds to the waiting area with probability p and is lost forever with probability (1 − p). The Laplace–Stieltjes transform of the waiting time of the tagged customer is derived. The joint probability distribution of the number of customers in the waiting area and the inventory level is obtained for the steady state case. Some important system performance measures and the long-run total expected cost rate are derived in the steady state.

Sign in / Sign up

Export Citation Format

Share Document