Transcript Process Planning and Its Integration with Design and

Swarm Intelligence:
A new way to think about
business
Professor Kesheng Wang
Department of Production and Quality engineering
Norwegian University of Science and Technology,
Trondheim Norway
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2015/7/17
Outlines
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2.
3.
4.
5.
6.
7.
Introduction
Foraging for solution
The task of dividing tasks
Simple rules rule
Raiding new markets
A swarm of possibilities
Conclusion
knowledge management, Calpe
2015/7/17
1. Introduction
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Swarm Smarts/Intelligence are based Natural examples, which
include ant colonies, bird flocking, animal herding, bacterial
growth, and fish schooling.
Insects that live in colonies — ants, bees, wasps, termites birds
and fish —have long fascinated everyone from naturalists to
artists
Dumb parts, properly connected into a swarm, yield smart results.
Using ants and other social insects as models, computer scientists
have created software agents that cooperate to solve complex
problems, such as the rerouting of traffic in a busy telecom
network
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2015/7/17
What is swarm intelligence?
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The term Swarm Intelligence (SI) was coined in the late 1980s
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Social insects work without supervision. In fact, their teamwork is
largely self-organized, and coordination arises from the different
interactions among individuals in the colony. Although these
interactions might be primitive (one ant merely following the trail
left by another, for instance), taken together they result in efficient
solutions to difficult problems (such as finding the shortest route to
a food source among myriad possible paths).
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The collective behavior that emerges from a group of social insects
has been called “swarm intelligence”.
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Some Definitions of Swarm Intelligence
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Swarm Intelligence (SI) is the property of a system
whereby the collective behaviors of unsophisticated
agents interacting locally with their environment
cause coherent functional global patterns to emerge.
- Ramos, Fernandes et al. 2005
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Computational Swarm Intelligence (CSI) refers to
algorithmic models.
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2015/7/17
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“Swarm” = swarm, flock, herd, colony, gaggle, group,
etc.
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Any collection of agents where, if each agent enacts
“simple” rules, the swarm exhibits a “complex”
behavior.
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Motivations of using SI
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Dealing too complex problems:
 Incapable to solve by human proposed solution
 Absence of complete mathematical model
Existing of similar problems in nature:
 Adaptation
 Self-organization
 Communication
 Optimization
Characteristics of a swarm:
 Distributed, no central control or data source;
 Limited communication
 No (explicit) model of the environment;
 Perception of environment (sensing)
 Ability to react to environment changes
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Characteristics of SI
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Initially inspired by how social insects operate –shaped by millions
of years of evolution.
Swarm Intelligence (SI) is the property of a system whereby the
collective behaviors of (unsophisticated) agents interacting locally
with their environment cause coherent functional global patterns to
emerge.
It is a mindset rather than a technology.
It is a bottom-up approach to controlling and optimizing distributed
systems
It use resilient, decentralized, self-organized techniques
It has limited communication
No (explicit) model of the environment
Perception of environment (sensing)
Ability to react to environment changes
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2015/7/17
The advantages of Swarm Intelligence (SI)
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Flexibility: the group (swarm) can quickly respond to internal
perturbations and external challenges.
Adaptability: The group can adapt to a changing environment.
Robustness: even if one or more individuals in the group fail, the
group can still complete its tasks.
Self-organization: Paths to solutions are emergent rather than
predefined.
Decentralized: the group needs relatively little supervision or topdown control. In other words, there is no central control(ler) in the
colony.
Scalability: the control mechanisms used are not dependent on the
number of agents in the swarm
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2015/7/17
2. Foraging for solution
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Ant foraging model: The way ants forage food holds valuable
insights (Ant Colony Optimization)
Ants are able to find the shortest path form nest to a food source
by laying and following chemical trails.
Individual ants emit a chemical substance – a pheromone –
which then attracts other ants.
Probability of choosing a branch of a path at a certain time
depends on the total amount of pheromone on the branch.
The choice is proportional to the number of ants that have used
the branches.
Basic Rules: Lay pheromone and Follow the trails of others.
Food
source
Nest
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How does it function?
food
Foraging
area
Foraging
area
Nest
Nest
4 mn
8 mn
12.5 cm
2
1
nest
Ants collectively select the shortest path to
the food source.
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2015/7/17
Examples of Ant foraging models
(Ant colony optimization in part III):
Variations of this simple yet powerful approach can
help solve a number of business problem:
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Unpredictable environment of a telecommunication
network
Effective cargo and vehicles routing.
The efficiency of factory scheduling.
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Some Applications
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Vehicle Routing Problem (VRP): The VRP is similar to the TSP, but is
complicated by multiple vehicles, vehicle capacity, pick-up and drop off
points (which can dictate vehicle packing and scheduling). Bernd
Mullenheimer, Richard Hartl and Christine Strauss developed an Ant
Colony algorithm for solving the VRP; and Pina Petroli truck routing
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Scheduling : Scheduling is a widespread problem of practical importance.
Paul Forsyth & Anthony Wren, University of Leeds Computer Science
department developed a bus driver scheduling application using ant colony
concepts. Air Liquide supply chain optimization and control; Unilever
plant scheduling
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Telecommunication Networks : Network routing refers to the activity of
creating, maintaining and using routing tables (one for each node in the
network) to determine where to direct an incoming data stream so that it
can continue its travel through the network. In telecommunications, this is
an extremely difficult problem because of the constant changes in network
traffic load. The Ant Colony algorithm provides adaptive advantages that
can adjust to traffic load. British Telecom, France Telecom, MCI routing in
communications networks
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3. The task of dividing tasks
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Honeybee model: The way insects allocate
labor holds Valuable insights
 Dividing tasks: In a honeybee colony,
individuals specialize in certain tasks, and
yet the allocation of work is very flexible.
When food is scarce, nurse bees will help by
foraging.
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Case 1: Scheduling paint booths
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In the factory, the booths must paint truck coming off
an assembly line. When necessary, a booth can be
change the color it’s using, but doing so is timeconsuming and costly.
The booths can be thought of as honeybees governed
by the following rule:
An individual performs the tasks for which it is
specialized unless it perceive an important need to
perform another function.
A booth with red paint will continue to handle orders
of that color unless a job marked “urgent” requires a
white truck and the queues at the other booths,
particularly those specializing in white, are much
longer
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Case 1: Scheduling paint booths (cont.)
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Although this basic rule sounds simplistic, in
practice it is very effective.
It enables the paint booths to determine their own
schedules with higher efficiency—specifically,
fewer color changes—than a centralized computer
can provide.
And the method is adept at responding to changes
in consumer demand. If the number of trucks that
need to be painted blue surges unexpectedly, other
booths can quickly forgo their specialty colors to
accommodate the unassigned vehicles.
Furthermore, the system copes easily with glitches.
When a paint booth breaks down, other stations
compensate swiftly by immediately divvying up the
additional load.
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Case 2: “Bucket brigade” model
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Another useful model of work allocation comes from seedharvester ants carrying food back to their nest.
Like runners transferring a baton in a relay race, the ants
pass food down a chain.
But the ants are not stationary, and their transfer points are
not fixed: an ant carries the food down the chain until it
reaches the next ant, and after transferring the food, it turns
back until it meets the previous ant in the chain to receive its
next load.
The only fixed location in this operation are start (the food
source) and the end (the nest)..
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Case 2: “Bucket brigade” model (cont.)
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The simple approach can dramatically increase the
efficiency of operations in which work is passed from one
person to another. For example, it can be applied to order
pickers at a large distribution center of a major retail chain
or to allocate workers in an product assembly line.
The warehouse used Zone approach, in which each worker
was responsible for a particular part of the order, and next
person could not begin until the first person complete that
task.
One problem with zone approaches is the wide variation in
the rates at which different employees work.
A supervisor had to monitor each aisle to correct the
congestions that inevitably occurred.
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Case 2: “Bucket brigade” model (cont.)
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Set up a new simple rule: “Continue picking out products to fill the
order until the person downstream from you takes over you work;
then head upstram to take over the next person’s work”
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The optimum sequence of workers is from the slowest to the
fastest.
 “Bucket brigade” model allows a work line to balance itself – that
is, the optimum solution emerges without any intervention by
managers
 In assembly line, it can function as a self-organizing system that
spontaneously achieves its own optimum configuration, without
special equipment, time-motion studies, work-content models,
management, or software control systems.
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2015/7/17
4. Simple rules rule
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The most powerful-and fascinating-insight from
swarm intelligence is that complex collective
behavior can emerge from individuals following
simple rules.
For social insects, millions of years of evolution
have fine-tuned those rules for great efficiency ,
flexibility , and robustness. Can managers
develop similar rules to shape the behavior of their
organizations and replace rigid command-andcontrol structures?
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An Example: Bird flocking
“Boids” model: “bird-oid” objects (also
schooling fish)
 Model: biologically and physically sound
* Individual has only local knowledge
* Has certain cognitive capabilities
* Is bound by the law of physic
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Three rules
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Collision avoidance (avoid collisions with
neighboring boids)
 Velocity Matching (match the velocity of
neighbouring boids)
 Flock centering (stay near neighnoring
boids)
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Bad news, good news
Bad news
• Difficult to predict collective behavior from individual rules.
• Interrogate one of the participants, it won’t tell you anything
about the function of the group.
• Small changes in rules lead to different group-level behavior.
• Individual behavior looks like noise: how do you detect
threats?
Good news
• Possible to efficiently control organization or manipulate
groups using simple rules.
• Possible to predict group-level outcome using bottom
simulation.
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Simple rules modeling at Southwest Airlines
Problem
 Optimize cargo routing
 Use simple rules
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Results
 71% improvement
 At least $10m/yr
2015/7/17
Challenges in simple rules modeling
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Important research thrust: If we want the swarm to
exhibit a certain behavior (accomplish an
objective), what simple rules should the agents
follow?
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Have to know relationship between rules and
behavior
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2015/7/17
5. Raiding new markets
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Laying pheromone is a form of “mass
recruitment”;
In some species, though, an ant that finds a food
source returns to the nest and vibrates its antennae
to convince one other nest mate to return to the
site. That’s “tandem recruitment”;
In other cases, an ant vibrates its antennae to get a
number of nest mates to follow. That’s “group
recruitment”.
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Raiding new markets (cont.)
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Mass recruitment is most often associated with
large colonies
Tandem recruitment with small colonies
Group recruitment with medium-sized ones
Example: Louise Kitchen, 1999, Thank s to
Group recruitment, that new “food source”
handles approximately $1 billion of transactions
daily and has added a few billion dollars to
Enron’s market capitalization.
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The right nurturing environment for
group recruitment
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Maintain their ability to explore new opportunities
while exploiting existing ones;
Enable a person with an idea to recruit others;
Allow, but not force, people to be recruited, even
when they are working in a core business;
Let the system self-select the best ideas; and
Support the winning ideas with sufficient
resources.
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6. A swarm of possibilities
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The possible applications of swarm intelligence
may be limited only by the imagination.
 The way insects cluster their colony’s dead and
sort their larvae has led to a novel approach for
banks to use to analyze their data for interesting
commonalities among customers.
 Reconfigurable robots swarms can assemble
themselves into vacuum cleaners and other home
appliances or move an object collaboratively.
 More and more.
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2015/7/17
Some future studies
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When a honeybee colony becomes too large - that is ,when it
reaches a point of diminishing returns - the nest splits into two;
exactly what rules bees follow to do this remains a mystery.
Such knowledge maybe inspired: When should a large corporations
determine to spin off some of their operations?
A queen wasp, fearing that the departure of some of her
subordinates could cripple the colony, induces them to stay by
granting them the right to lay eggs. The amount of this “staying
incentive” depends on ecological conditions. If, say, the weather is
mild and food abundant, the queen must offer greater inducements.
The parallel with managers trying to retain top talent in a booming
economy is striking.
The parallel between social insects and people are more than just
conceptual: they can have practical and useful significance, as
recent research has shown.
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2015/7/17
7. Conclusion
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SW is becoming a valuable tool for optimizing the operations of
various businesses.
Through SI provides a fresh new framework for solving such
problems, and it still questions the wisdom of certain assumptions
regarding the need for employee supervision through commandand control management.
In the future, some companies could build their entire businesses
from the ground up using the principles of swarm intelligence,
integrating the approach throughout their operations,
organization, and strategy.
The result: the ultimate self-organizing enterprise that could adapt
quickly and instinctively to fast changing markets.
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Thanks for Your Attention!
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2015/7/17