A discrete Event Simulation Model of Asphalt Paving Operations, Ramzi Labban ...CCT International
The process of building a simulation model is one of the toughest and time-consuming part of the entire process.
An alternative method and a new approach for creating construction simulation models are provided in the in the presentation above which was presented at the Winter Simulation Conference 2013 in Washington D.C.
Simulation and modeling introduction.pptxShamasRehman4
This document discusses simulation and modeling. It begins by introducing systems, modeling, and simulation. Modeling creates a representation of a system, while simulation operates a model to study the behavior of the actual system. There are different types of simulation models including deterministic/stochastic and static/dynamic models. The document outlines steps for building simulation models, including defining goals, involving end users, choosing tools, and validating results. General purpose languages, simulation languages, and special purpose packages are options for developing simulation models.
Performance modeling provides important insights for capacity planning and system sizing without costly full-scale testing. While sophisticated mathematical modeling was common in the past, today's complex systems are difficult to model formally and existing tools are outdated. However, minimal modeling with common-sense approximations using metrics like resource usage per transaction and hardware capacity can still be useful. Keeping even informal models in mind helps performance engineers understand systems, but complex systems benefit from documenting models. Reviving the art of performance modeling can add value to modern continuous performance testing approaches.
This document discusses project estimation and the Constructive Cost Model (COCOMO) for estimating software development costs and schedules. It explains that inaccurate estimates often lead to cost overruns and project failures. Several estimation methods are described like expert judgment, analogy models, and algorithmic models. The COCOMO model uses variables like project size, mode (organic, semidetached, embedded), and effort adjustment factors to estimate effort (in person-months), development time, and staffing needs. The basic, intermediate, and detailed COCOMO models are explained along with the equations used for effort and schedule estimates. Factors that impact productivity like application experience, process quality, and technology are also summarized.
This document discusses implementing linear programming in a textile unit in India. It describes how the textile industry was facing problems with manual planning that did not optimize output quantity or loom usage. The study collected data on yarn needs, loom shifts, cloth production rates, and processing requirements to define the linear programming problem as maximizing contribution per loom subject to production, marketing, and buy/sell constraints. The solution increased contribution per loom but also increased fixed costs. The researchers advised continuously using the linear programming package to account for seasonal changes.
This presentation introduces the concept of Machine Learning and then discusses how Machine Learning is being used in the Predictive Maintenance domain.
A discrete Event Simulation Model of Asphalt Paving Operations, Ramzi Labban ...CCT International
The process of building a simulation model is one of the toughest and time-consuming part of the entire process.
An alternative method and a new approach for creating construction simulation models are provided in the in the presentation above which was presented at the Winter Simulation Conference 2013 in Washington D.C.
Simulation and modeling introduction.pptxShamasRehman4
This document discusses simulation and modeling. It begins by introducing systems, modeling, and simulation. Modeling creates a representation of a system, while simulation operates a model to study the behavior of the actual system. There are different types of simulation models including deterministic/stochastic and static/dynamic models. The document outlines steps for building simulation models, including defining goals, involving end users, choosing tools, and validating results. General purpose languages, simulation languages, and special purpose packages are options for developing simulation models.
Performance modeling provides important insights for capacity planning and system sizing without costly full-scale testing. While sophisticated mathematical modeling was common in the past, today's complex systems are difficult to model formally and existing tools are outdated. However, minimal modeling with common-sense approximations using metrics like resource usage per transaction and hardware capacity can still be useful. Keeping even informal models in mind helps performance engineers understand systems, but complex systems benefit from documenting models. Reviving the art of performance modeling can add value to modern continuous performance testing approaches.
This document discusses project estimation and the Constructive Cost Model (COCOMO) for estimating software development costs and schedules. It explains that inaccurate estimates often lead to cost overruns and project failures. Several estimation methods are described like expert judgment, analogy models, and algorithmic models. The COCOMO model uses variables like project size, mode (organic, semidetached, embedded), and effort adjustment factors to estimate effort (in person-months), development time, and staffing needs. The basic, intermediate, and detailed COCOMO models are explained along with the equations used for effort and schedule estimates. Factors that impact productivity like application experience, process quality, and technology are also summarized.
This document discusses implementing linear programming in a textile unit in India. It describes how the textile industry was facing problems with manual planning that did not optimize output quantity or loom usage. The study collected data on yarn needs, loom shifts, cloth production rates, and processing requirements to define the linear programming problem as maximizing contribution per loom subject to production, marketing, and buy/sell constraints. The solution increased contribution per loom but also increased fixed costs. The researchers advised continuously using the linear programming package to account for seasonal changes.
This presentation introduces the concept of Machine Learning and then discusses how Machine Learning is being used in the Predictive Maintenance domain.
The document discusses manufacturing systems and lean manufacturing. It defines a manufacturing system as a collection of integrated equipment and human resources that perform processing and assembly operations on raw materials. It describes the typical input-transformation-output process. Examples of manufacturing systems include single station cells, machine clusters, and automated assembly lines. The key components of manufacturing systems are production machines, material handling systems, computer systems, and human resources. Lean manufacturing aims to eliminate waste from the manufacturing system, such as overproduction, waiting, inventory, transportation, and over-processing. It was pioneered by Toyota to increase efficiency and reduce costs.
This is a presentation of a textiles ERP (Enterprise Resource Planning) system that I wrote. It shows what a single professional programmer can do. Analysis, Design, UX Design, Database Design, Programming, Testing, Implementation, Training, Maintenance, Iteratively and using Agile, before Agile was invented.
This document summarizes a seminar presentation on project management. It defines key terms like project, management, and project management. It also discusses the software development life cycle including requirements gathering, design, implementation, testing, deployment, and maintenance. Common software development models are outlined like waterfall, V-shaped, prototyping, spiral, iterative, and agile. Data flow diagrams are introduced as a way to graphically represent data flows in a system.
The document discusses various software project life cycle models and cost estimation techniques. It begins by describing agile methods like Scrum and Extreme Programming that emphasize iterative development, communication, and customer involvement. It then covers traditional models like waterfall and incremental development. Key estimation techniques discussed include function points, COCOMO, and analogy-based estimation. The document provides details on calculating sizes and estimating effort for different models.
Textile Factory Software System and Implementation Processes Presentation. Problems solved. Amazing achievements.
Delphi & Interbase Open Source Software. Get an idea of how textile factories operate. Use the open source software in Delphi (Pascal) or use the algorithms in other environments
The document discusses facility layout and design. It defines facility layout as the arrangement of workspaces and equipment to carry out organizational operations. There are three main types of layouts - process, product, and fixed-position. Process layouts group similar processes together while product layouts are designed for specific product lines. The document outlines factors to consider for effective layouts like workflow, growth plans, and employee satisfaction. It also provides steps for designing process and product layouts, including identifying tasks, setting cycle times, and balancing lines. Group technology layouts can combine efficiencies of product and process layouts through organizing into cells. Facility layout impacts multiple organizational functions.
This document provides an introduction to discrete event simulation. It discusses key concepts like systems, processes, states, activities, continuous vs discrete vs hybrid systems, deterministic vs stochastic systems, and when simulation is an appropriate tool. It also outlines the steps in a simulation study from problem formulation to implementation. An example queueing simulation is presented to illustrate tracking customers over time. Random number generation techniques like the linear congruential method are introduced. Desired properties of pseudorandom numbers for simulation are discussed.
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Originally given as a talk at the PyData Ann Arbor meetup (https://www.meetup.com/PyData-Ann-Arbor/events/260380989/)
The document discusses various considerations for process design and facility layout including process selection based on factors like volume and variety, different types of process layouts including job shop, batch, repetitive, and continuous processes, and considerations for automation and layout types including product, process, fixed-position, group technology, and cellular layouts. It also covers concepts like line balancing, parallel workstations, and designing layouts for different environments like warehouses, retail stores, and offices.
This document discusses process design and facility layout. It begins by explaining different process types like job shops, batch processing, repetitive/assembly, and continuous processing. It then discusses factors to consider for process selection like product variety and volume. Different layout types are described like product layouts, process layouts, fixed-position layouts, and combination layouts. Cellular layouts and group technology layouts are also covered. The document concludes with a discussion of line balancing to optimize workstation efficiency.
This document provides an overview of software project planning and estimation. It discusses the key aspects of project planning including defining the product, process, project, and people. It emphasizes the importance of understanding stakeholders and structuring an effective software development team. The document also covers estimating resources, costs, schedules, and risks. It presents different estimation techniques including problem-based decomposition, function point analysis, and process-based and use case-based approaches. Finally, it introduces various project metrics and empirical estimation models that can be used to plan and control a software project.
This document provides an overview of virtual product development and product building and structure. It discusses the benefits of virtual product development over traditional methods, including reducing costs and time to market. It also describes various virtual product development tools like 3D CAD systems and digital mockups that allow designing, simulating, and testing virtual products and prototypes without building physical ones. The document outlines techniques for building virtual product models like solid modeling and parametric modeling. It also discusses analyzing virtual product models using computer-aided engineering tools.
The document describes solving linear programming problems graphically. It provides an example maximization problem with an objective of maximizing Z=30x1 + 40x2 subject to three constraints. Graphically, the feasible region satisfying all constraints is determined by plotting the points where each constraint equation is equal to 0 for x1 and x2, and shading the correct side of the inequality sign. The optimal solution that maximizes Z can then be found within the feasible region on the graph.
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This document discusses process selection and facility layout. It begins by defining process selection as deciding how production will be organized in terms of capacity planning, facility layout, equipment, and work design. It then describes the key aspects and types of process strategies such as capital intensity, flexibility, design, volume, and technology. The main process types are defined as job shop, batch, repetitive/assembly line, continuous, and projects. Product-process matrices and examples of different industries are provided. The functions affected by process choice and examples of production life cycles are summarized. The document concludes by outlining objectives for facility layout design and describing basic layout types including product, process, and fixed-position layouts.
The document provides information about computers and computer science. It defines a computer as an electronic machine that processes data and instructions to perform computations and make logical decisions. Computer programs are sets of instructions that computers process data according to. Computer hardware is the physical devices that make up a computer system. Computer software are programs that run on computers and can be categorized into system software, middleware, and application software. The document also discusses the history of computers, what computer science entails, and how computers are used in various applications today. It defines computer science as the study of theoretical foundations of information and computation as well as practical techniques for implementing computer systems.
The document discusses manufacturing systems and lean manufacturing. It defines a manufacturing system as a collection of integrated equipment and human resources that perform processing and assembly operations on raw materials. It describes the typical input-transformation-output process. Examples of manufacturing systems include single station cells, machine clusters, and automated assembly lines. The key components of manufacturing systems are production machines, material handling systems, computer systems, and human resources. Lean manufacturing aims to eliminate waste from the manufacturing system, such as overproduction, waiting, inventory, transportation, and over-processing. It was pioneered by Toyota to increase efficiency and reduce costs.
This is a presentation of a textiles ERP (Enterprise Resource Planning) system that I wrote. It shows what a single professional programmer can do. Analysis, Design, UX Design, Database Design, Programming, Testing, Implementation, Training, Maintenance, Iteratively and using Agile, before Agile was invented.
This document summarizes a seminar presentation on project management. It defines key terms like project, management, and project management. It also discusses the software development life cycle including requirements gathering, design, implementation, testing, deployment, and maintenance. Common software development models are outlined like waterfall, V-shaped, prototyping, spiral, iterative, and agile. Data flow diagrams are introduced as a way to graphically represent data flows in a system.
The document discusses various software project life cycle models and cost estimation techniques. It begins by describing agile methods like Scrum and Extreme Programming that emphasize iterative development, communication, and customer involvement. It then covers traditional models like waterfall and incremental development. Key estimation techniques discussed include function points, COCOMO, and analogy-based estimation. The document provides details on calculating sizes and estimating effort for different models.
Textile Factory Software System and Implementation Processes Presentation. Problems solved. Amazing achievements.
Delphi & Interbase Open Source Software. Get an idea of how textile factories operate. Use the open source software in Delphi (Pascal) or use the algorithms in other environments
The document discusses facility layout and design. It defines facility layout as the arrangement of workspaces and equipment to carry out organizational operations. There are three main types of layouts - process, product, and fixed-position. Process layouts group similar processes together while product layouts are designed for specific product lines. The document outlines factors to consider for effective layouts like workflow, growth plans, and employee satisfaction. It also provides steps for designing process and product layouts, including identifying tasks, setting cycle times, and balancing lines. Group technology layouts can combine efficiencies of product and process layouts through organizing into cells. Facility layout impacts multiple organizational functions.
This document provides an introduction to discrete event simulation. It discusses key concepts like systems, processes, states, activities, continuous vs discrete vs hybrid systems, deterministic vs stochastic systems, and when simulation is an appropriate tool. It also outlines the steps in a simulation study from problem formulation to implementation. An example queueing simulation is presented to illustrate tracking customers over time. Random number generation techniques like the linear congruential method are introduced. Desired properties of pseudorandom numbers for simulation are discussed.
The document discusses customization and 3D printing from a software product line perspective. The researchers observed the Thingiverse community to see how they interact and collaborate to customize and produce 3D models. They found that while variability concepts are present, there is no constraints modeling and configuration leads to many issues due to huge complexity with 38 parameters across 8 tabs and 10^28 possible configurations. Software product line engineering techniques like variability modeling and implementation could help address challenges of complexity and cognitive effort for non-software developers customizing 3D models, but may not provide clear benefits for small communities in garages. Future work includes automated techniques to better analyze large datasets and help communities manage complexity.
As data science workloads grow, so does their need for infrastructure. But, is it fair to ask data scientists to also become infrastructure experts? If not the data scientists, then, who is responsible for spinning up and managing data science infrastructure? This talk will address the context in which ML infrastructure is emerging, walk through two examples of ML infrastructure tools for launching hyperparameter optimization jobs, and end with some thoughts for building better tools in the future.
Originally given as a talk at the PyData Ann Arbor meetup (https://www.meetup.com/PyData-Ann-Arbor/events/260380989/)
The document discusses various considerations for process design and facility layout including process selection based on factors like volume and variety, different types of process layouts including job shop, batch, repetitive, and continuous processes, and considerations for automation and layout types including product, process, fixed-position, group technology, and cellular layouts. It also covers concepts like line balancing, parallel workstations, and designing layouts for different environments like warehouses, retail stores, and offices.
This document discusses process design and facility layout. It begins by explaining different process types like job shops, batch processing, repetitive/assembly, and continuous processing. It then discusses factors to consider for process selection like product variety and volume. Different layout types are described like product layouts, process layouts, fixed-position layouts, and combination layouts. Cellular layouts and group technology layouts are also covered. The document concludes with a discussion of line balancing to optimize workstation efficiency.
This document provides an overview of software project planning and estimation. It discusses the key aspects of project planning including defining the product, process, project, and people. It emphasizes the importance of understanding stakeholders and structuring an effective software development team. The document also covers estimating resources, costs, schedules, and risks. It presents different estimation techniques including problem-based decomposition, function point analysis, and process-based and use case-based approaches. Finally, it introduces various project metrics and empirical estimation models that can be used to plan and control a software project.
This document provides an overview of virtual product development and product building and structure. It discusses the benefits of virtual product development over traditional methods, including reducing costs and time to market. It also describes various virtual product development tools like 3D CAD systems and digital mockups that allow designing, simulating, and testing virtual products and prototypes without building physical ones. The document outlines techniques for building virtual product models like solid modeling and parametric modeling. It also discusses analyzing virtual product models using computer-aided engineering tools.
The document describes solving linear programming problems graphically. It provides an example maximization problem with an objective of maximizing Z=30x1 + 40x2 subject to three constraints. Graphically, the feasible region satisfying all constraints is determined by plotting the points where each constraint equation is equal to 0 for x1 and x2, and shading the correct side of the inequality sign. The optimal solution that maximizes Z can then be found within the feasible region on the graph.
This course introduces students to operations research and its applications. The course covers several optimization techniques including linear programming, network flows, and transportation problems. Students will learn how to formulate mathematical models of real-world systems and solve them to determine optimal resource allocation. The goal is for students to be able to apply operations research methods to decision-making problems in fields like manufacturing, transportation, and public services. Students will be assessed through assignments, tests, and a final exam.
This document discusses process selection and facility layout. It begins by defining process selection as deciding how production will be organized in terms of capacity planning, facility layout, equipment, and work design. It then describes the key aspects and types of process strategies such as capital intensity, flexibility, design, volume, and technology. The main process types are defined as job shop, batch, repetitive/assembly line, continuous, and projects. Product-process matrices and examples of different industries are provided. The functions affected by process choice and examples of production life cycles are summarized. The document concludes by outlining objectives for facility layout design and describing basic layout types including product, process, and fixed-position layouts.
The document provides information about computers and computer science. It defines a computer as an electronic machine that processes data and instructions to perform computations and make logical decisions. Computer programs are sets of instructions that computers process data according to. Computer hardware is the physical devices that make up a computer system. Computer software are programs that run on computers and can be categorized into system software, middleware, and application software. The document also discusses the history of computers, what computer science entails, and how computers are used in various applications today. It defines computer science as the study of theoretical foundations of information and computation as well as practical techniques for implementing computer systems.
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Could Digital DIY Break Manufacturing Hierarchies?
1. Could
Digital
DIY
Break
Manufacturing
Hierarchies?
Ruth
Meyer,
Magnus
Josefsson
Centre
for
Policy
Modelling,
Manchester
Metropolitan
University
www.didiy.eu
2. What
is
Digital
DIY?
• Digital
DIY
=
the
set
of
all
manufacturing
activities
(and
mindsets)
that
are
made
possible
by
digital
technologies
– enables
people
to
do
things
they
could
not
do
before
• “sculpting”
with
3D
printer
– gives
more
opportunities
to
do
things
together
by
freely
sharing
designs
and
know-‐how
• DiDIY
Project
investigated
impact
on
several
domains
– Education
and
research
– Work
and
organisation
– Legal
systems
– Creative
Society
3. Digital
DIY
in
the
Workplace
• How
will
the
work
of
a
worker
in
a
manufacturing
firm
be
reshaped
due
to
the
influence
of
DiDIY?
– Direct
access
to
relevant
information
(e.g.
current
status
of
machines)
could
overcome
traditionally
strict
organisational
hierarchies
• Applying
an
ABM
of
an
abstract
factory
with
supervisor,
workers,
machines
and
tasks
to
investigate
the
following
research
questions:
– Allowing
workers
autonomy
in
deciding
which
task
to
do
next,
does
this
improve
the
effectiveness
of
the
production
process?
– Do
supervisors
become
superfluous?
4. StringWorld
Problem
Space
• A
world
of
strings
(things)
made
up
of
a
sequence
of
letters
(elements),
e.g.
“A”,
“AA”,
“ABAC”,
“BBC”
• Agents
(makers)
try
to
make
things
out
of
things
they
find
in
their
environment
(resources)
plus
things
they
might
get
from
other
agents,
using
a
limited
set
of
operations,
possibly
applying
specific
tools.
• Agents
aim
to
produce
certain
things
(targets)
–
using
trial
and
error
– following
a
plan
• Problem
space
to
address
DiDIY
simulation
issues
– Complex
enough
à
plans
are
worth
sharing
– still
computationally
feasible
• Prototype
implementation:
Model
of
Making
5. Factory
Model
Overview
• Workers
(agents)
realised
as
patches,
coloured
brown
• Supervisor
marked
by
red
square
• Other
patches
hold
• Resources
• Targets
• Machines
• Machines
are
tools
which
provide
a
particular
string
operation
• “add-‐B”
• “join”
• “envelope”
using
up
input
things
to
produce
a
new
output
thing
• Factory
has
to
produce
a
certain
number
of
targets
6. Specific
problems
to
solve
• Need
to
ensure
that
it
is
always
possible
to
produce
the
targets
from
the
resources
with
the
available
machine
operations
• Solution:
‘Possible
products’
network
inspired
by
firm
skills
universe
(Taylor
&
Morone
2005)
– build
network
of
nodes
(products)
and
links
(necessary
inputs),
starting
from
resources
– total
number
of
nodes
defined
by
model
parameters
num-‐resources,
num-‐targets,
num-‐
machine-‐types
7. Possible
Products
Network
Example:
3
resources,
5
machine
types,
3
targets
• Max
18
nodes
• Each
with
1,
2,
or
3
inputs
• Random
distribution
based
on
pre-‐defined
string
operations
Pick
3
of
the
5
potential
targets,
covering
all
resources
• 13,
15
and
17
Assign
operations
to
(bundles
of)
input
links
Assign
strings
to
resources
à
Derive
target
strings
8. Model
Variants
• With
supervisor
– Supervisor
assigns
jobs
to
workers
based
on
• which
target
is
the
most
outstanding
• which
machines
are
free
(for
starting
on
the
job)
– Workers
follow
the
plan
to
make
the
target
– Once
finished,
they
ask
the
supervisor
for
their
next
job
• Without
supervisor
– Workers
know
the
current
status
of
all
machines
and
which
tasks
produce
what
from
which
inputs
– Workers
decide
on
the
next
task
based
on
• which
machines
are
free
• what
things
they
have
(prefer
to
use
own
stuff
over
resources)
• Pick
most
outstanding
target
if
nothing
else
possible
10. Discussion
• First
experiments
focused
on
overall
effectiveness
of
production
process
while
varying
number
of
agents
– Production
time
(total
simulation
time
until
all
targets
achieved)
– Average
time
workers
spent
waiting
for
a
free
machine
• Introducing
simple
form
of
cooperation
– When
deliberating
possible
next
tasks,
a
worker
may
consider
not
only
the
things
(s)he
has
themselves
but
also
things
other
agents
have
• Subsequent
experiments
showed
that
results
are
very
dependent
on
the
factory
setup
(number
of
resources,
targets,
machine
types,
machines
per
type,
processing
times,
number
of
agents)
11. Introducing
Garbage
Can
Measures
• Wanted:
output
measures
to
gauge
impact
of
organisational
change
• Garbage
Can
Model:
influential
model
of
organisational
behaviour
– Problems,
participants,
opportunities,
solutions
• Three
indicators
– Problem
latency:
time
spent
by
problems
in
the
system
before
a
participant
attempts
to
solve
them
– Unsolved
problems:
number
of
problems
left
at
the
end
of
the
simulation
– Waiting
time:
time
opportunities
stay
in
the
system
waiting
to
be
used
12. GC
Indicators
for
Factory
• Translate
GC
terminology
to
factory
world
– Job
latency:
Time
spent
by
jobs
(‘problems’)
in
the
factory
before
a
worker
starts
working
on
them
– Unfinished
jobs:
number
of
unfinished
jobs
at
the
end
of
the
simulation
– Waiting
time:
Time
spent
by
free
machines
/
free
workers
waiting
to
be
used
/
start
working
on
a
new
job
13. Model
AdaptaIons
• GC
Model
assumes
streams
of
objects
• Factory
Model
– Incoming
stream
of
jobs,
with
mean
arrival
time
and
different
probabilities
for
the
different
types
– Each
job
specifies
which
target
to
produce
(a
sequence
of
tasks)
– For
cooperation,
jobs
are
split
into
the
separate
tasks
14. Preliminary
Results
(1)
Factory
1,
averages
of
20
runs
with
the
same
random
seeds,
mean
arrival
time
0.2,
simulation
stops
20
ticks
after
all
jobs
arrived
15. Preliminary
Results
(2)
Factory
1,
averages
of
20
runs
with
the
same
random
seeds,
mean
arrival
time
0.3,
simulation
stops
20
ticks
after
all
jobs
arrived
16. Preliminary
Results
(3)
Different
factory
setup
(more
complex
tasks)
with
higher
cooperation,
mean
arrival
time
0.2,
simulation
stops
20
ticks
after
all
jobs
arrived
17. Conclusion
and
Outlook
• Introduction
of
Garbage
Can
measures
helpful
in
assessing
the
factory
model
• Cooperation
manages
to
outperform
supervision
when
– Jobs
are
fairly
complex
(e.g.
with
intermediate
products
used
in
several
tasks)
– Frequency
of
jobs
is
high
• Investigation
will
continue…