MCM,MCA,MSc, MMM, MPhil, PhD (Computer Applications)
Working as Associate Professor at Zeal Education Society, Pune for MCA Progrmme.
Having 18 Years teaching experience
The Least Cost Method is another method used to obtain the initial feasible solution for the transportation problem. Here, the allocation begins with the cell which has the minimum cost. The lower cost cells are chosen over the higher-cost cell with the objective to have the least cost of transportation.
Vehicle routing and scheduling Models:
Travelling salesman problem
vehicle routing problem with time window
Pick up and delivery problem with time window
Queueing Theory- Waiting Line Model, Heizer and RenderAi Lun Wu
I HOPE IT IS HELPFUL FOR YOU> BUT PLS IWANT CREDITS> OR ADD ME AND MESSAGE ME THANKS
THERE IS A NOTE FOR PRESENTERS VIEW
HAVE A GOOD DAY
KEEP CALM AND DRINK ON
NAME: Ellen Magalona
GNDR: FML
BRTHDY: FEB. 1998
@ellenmaaee
MCM,MCA,MSc, MMM, MPhil, PhD (Computer Applications)
Working as Associate Professor at Zeal Education Society, Pune for MCA Progrmme.
Having 18 Years teaching experience
The Least Cost Method is another method used to obtain the initial feasible solution for the transportation problem. Here, the allocation begins with the cell which has the minimum cost. The lower cost cells are chosen over the higher-cost cell with the objective to have the least cost of transportation.
Vehicle routing and scheduling Models:
Travelling salesman problem
vehicle routing problem with time window
Pick up and delivery problem with time window
Queueing Theory- Waiting Line Model, Heizer and RenderAi Lun Wu
I HOPE IT IS HELPFUL FOR YOU> BUT PLS IWANT CREDITS> OR ADD ME AND MESSAGE ME THANKS
THERE IS A NOTE FOR PRESENTERS VIEW
HAVE A GOOD DAY
KEEP CALM AND DRINK ON
NAME: Ellen Magalona
GNDR: FML
BRTHDY: FEB. 1998
@ellenmaaee
To Minimize the Waiting Time and Waiting Time Cost of Dumpers, Waiting in a Q...IJERA Editor
Waiting line problems arise because there is too much demand on the facilities so that we can say that there is an excess of waiting time or inadequate number of service facilities. At the stone crusher plant mine the dumpers come to load from the loader. The crusher plant has 11 dumpers and these 11 dumpers make 88 trips during 8-hour day. The company has one loader to load all the dumpers, which results in a formation of long waiting line or queue. Due to this queue there is a long waiting time in queue of dumpers and cost associated with waiting time of dumpers. Queuing theory can quite effectively analyze such queuing phenomenon. In this research paper I have applied the queuing theory to the stone crusher plant mine, where the queue of dumpers formed at the loading station. By applying the single channel queuing theory I analyzed the current situation of the stone crusher plant mine and find the problems of the current system. To overcome the above problems I have applied the multi-channel queuing theory to minimize the waiting time in queue of dumpers and very high cost associated with waiting time of dumpers. In the new system not only waiting time in queue of dumpers and very high cost associated with waiting time of dumpers is reduced but also there is an efficient utilization of dumpers and loaders along with provide the profitable situation to the crusher plant.
In operation research this is one of the intresting area which having lot of applications to apply in our real life. it can be used for both the service and manufacturing industry.
Automated Parameterization of Performance Models from MeasurementsWeikun Wang
This is a tutorial presented in ICPE 2016 (https://icpe2016.spec.org/). In this tutorial, we present the problem of estimating parameters of performance models from measurements of real systems and discuss algorithms that can support researchers and practitioners in this task. The focus lies on performance models based on queueing systems, where the estimation of request arrival rates and service demands is a required input to the model. In the tutorial, we review existing estimation methods for service demands and present models to characterize time-varying arrival processes. The tutorial also demonstrates the use of relevant tools that automate demand estimation, such as LibRede, FG and M3A.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
2. Queuing or Waiting Line Analysis
• Queues (waiting lines) affect people
everyday
• A primary goal is finding the best level of
service
• Analytical modeling (using formulas) can
be used for many queues
• For more complex situations, computer
simulation is needed
3. Queuing System Costs
1. Cost of providing service
2. Cost of not providing service (waiting time)
4. Three Rivers Shipping Example
• Average of 5 ships arrive per 12 hr shift
• A team of stevedores unloads each ship
• Each team of stevedores costs $6000/shift
• The cost of keeping a ship waiting is
$1000/hour
• How many teams of stevedores to employ
to minimize system cost?
5. Three Rivers Waiting Line Cost Analysis
Number of Teams of Stevedores
1 2 3 4
Ave hours
waiting per ship 7 4 3 2
Cost of ship
waiting time
(per shift)
$35,000 $20,000 $15,000 $10,000
Stevedore cost
(per shift) $6000 $12,000 $18,000 $24,000
Total Cost $41,000 $32,000 $33,000 $34,000
6. Characteristics of a
Queuing System
The queuing system is determined by:
• Arrival characteristics
• Queue characteristics
• Service facility characteristics
7. Arrival Characteristics
• Size of the arrival population – either
infinite or limited
• Arrival distribution:
–Either fixed or random
–Either measured by time between
consecutive arrivals, or arrival rate
–The Poisson distribution is often used
for random arrivals
8. Poisson Distribution
• Average arrival rate is known
• Average arrival rate is constant for some
number of time periods
• Number of arrivals in each time period is
independent
• As the time interval approaches 0, the
average number of arrivals approaches 0
9. Poisson Distribution
λ = the average arrival rate per time unit
P(x) = the probability of exactly x arrivals
occurring during one time period
P(x) = e-λ
λx
x!
10. Behavior of Arrivals
• Most queuing formulas assume that all
arrivals stay until service is completed
• Balking refers to customers who do not
join the queue
• Reneging refers to customers who join
the queue but give up and leave before
completing service
11. Queue Characteristics
• Queue length (max possible queue length)
– either limited or unlimited
• Service discipline – usually FIFO (First In
First Out)
12. Service Facility Characteristics
1. Configuration of service facility
• Number of servers (or channels)
• Number of phases (or service stops)
2. Service distribution
• The time it takes to serve 1 arrival
• Can be fixed or random
• Exponential distribution is often used
13. Exponential Distribution
μ = average service time
t = the length of service time (t > 0)
P(t) = probability that service time will be
greater than t
P(t) = e- μt
14. Measuring Queue Performance
• ρ = utilization factor (probability of all
servers being busy)
• Lq = average number in the queue
• L = average number in the system
• Wq = average waiting time
• W = average time in the system
• P0 = probability of 0 customers in system
• Pn = probability of exactly n customers in
system
15. Kendall’s Notation
A / B / s
A = Arrival distribution
(M for Poisson, D for deterministic, and
G for general)
B = Service time distribution
(M for exponential, D for deterministic,
and G for general)
S = number of servers
16. The Queuing Models
Covered Here All Assume
1. Arrivals follow the Poisson distribution
2. FIFO service
3. Single phase
4. Unlimited queue length
5. Steady state conditions
We will look at 5 of the most commonly used
queuing systems.
17. Models CoveredName
(Kendall Notation) Example
Simple system
(M / M / 1)
Customer service desk in a
store
Multiple server
(M / M / s)
Airline ticket counter
Constant service
(M / D / 1)
Automated car wash
General service
(M / G / 1)
Auto repair shop
Limited population
(M / M / s / ∞ / N)
An operation with only 12
machines that might break
18. Single Server Queuing System (M/M/1)
• Poisson arrivals
• Arrival population is unlimited
• Exponential service times
• All arrivals wait to be served
• λ is constant
• μ > λ (average service rate > average
arrival rate)
19. Operating Characteristics for M/M/1 Queue
1. Average server utilization
ρ = λ / μ
2. Average number of customers waiting
Lq = λ2
μ(μ – λ)
2. Average number in system
L = Lq + λ / μ
20. 4. Average waiting time
Wq = Lq = λ
λ μ(μ – λ)
5. Average time in the system
W = Wq + 1/ μ
5. Probability of 0 customers in system
P0 = 1 – λ/μ
7. Probability of exactly n customers in
system
Pn = (λ/μ )n
P0
21. Arnold’s Muffler Shop Example
• Customers arrive on average 2 per hour
(λ = 2 per hour)
• Average service time is 20 minutes
(μ = 3 per hour)
Install ExcelModules
Go to file 9-2.xls
22. Total Cost of Queuing System
Total Cost = Cw x L + Cs x s
Cw = cost of customer waiting time per
time period
L = average number customers in
system
Cs = cost of servers per time period
s = number of servers
23. Multiple Server System (M / M / s)
• Poisson arrivals
• Exponential service times
• s servers
• Total service rate must exceed arrival rate
( sμ > λ)
• Many of the operating characteristic
formulas are more complicated
24. Arnold’s Muffler Shop
With Multiple Servers
Two options have already been considered:
System
Cost
• Keep the current system (s=1) $32/hr
• Get a faster mechanic (s=1) $25/hr
Multi-server option
3. Have 2 mechanics (s=2) ?
Go to file 9-3.xls
25. Single Server System With
Constant Service Time (M/D/1)
• Poisson arrivals
• Constant service times (not random)
• Has shorter queues than M/M/1 system
- Lq and Wq are one-half as large
26. Garcia-Golding Recycling Example
• λ = 8 trucks per hour (random)
• μ = 12 trucks per hour (fixed)
• Truck & driver waiting cost is $60/hour
• New compactor will be amortized at
$3/unload
• Total cost per unload = ?
Go to file 9-4.xls
27. Single Server System With
General Service Time (M/G/1)
• Poisson arrivals
• General service time distribution with
known mean (μ) and standard deviation (σ)
• μ > λ
28. Professor Crino Office Hours
• Students arrive randomly at an average
rate of, λ = 5 per hour
• Service (advising) time is random at an
average rate of, μ = 6 per hour
• The service time standard deviation is,
σ = 0.0833 hours
Go to file 9-5.xls
29. Muti-Server System With
Finite Population (M/M/s/∞/N)
• Poisson arrivals
• Exponential service times
• s servers with identical service time
distributions
• Limited population of size N
• Arrival rate decreases as queue lengthens
30. Department of Commerce Example
• Uses 5 printers (N=5)
• Printers breakdown on average every 20
hours
λ = 1 printer = 0.05 printers per hour
20 hours
• Average service time is 2 hours
μ = 1 printer = 0.5 printers per hour
2 hours
Go to file 9-6.xls
31. More Complex Queuing Systems
• When a queuing system is more complex,
formulas may not be available
• The only option may be to use computer
simulation, which we will study in the next
chapter