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SolveITSoftware

Adaptive Business Intelligence

Zbigniew Michalewicz


SolveITSoftware Business Intelligence

What is Business Intelligence?

Business Intelligence is a collection of tools, methods, technologies, and processes needed to transform data into knowledge.

What should I do?


SolveITSoftware

Although Business Intelligence can be used to:

• Increase profitability,

• Decrease costs,

• Improve customer relationship management,

• Decrease risk,

Business Intelligence


SolveITSoftware

… most companies use it to answer basic queries:

• How many customers do I have?

• During the past 12 months, how many products were sold in each region?

• Who are my 20 best customers?

Business Intelligence


SolveITSoftware The famous pyramid

DATA

INFORMATION

KNOWLEDGE


SolveITSoftware

Data – a collection of raw value elements or facts used for calculating, reasoning, measuring, etc.

Information – the result of collecting and organizing data that establishes relationship between data items.

Knowledge – the concept of understanding information based on recognized patterns.

Data, information, knowledge

Knowledge is power!


SolveITSoftware Observation

Discovered knowledge is of little value if there is no value producing action that can be taken as a consequence of gaining that knowledge.

Example: 37% of our customers live on the East Coast.

So what?


SolveITSoftware What do others think?

PricewaterhouseCoopers Global DataManagement Survey of 2001:

“Companies that manage their data as a strategic resource and invest in its quality are already pulling ahead in terms of reputation and profitability.”

Data should be treated as strategic resource.



Pacific Crest Equities, 2006:

“Increasingly you are seeing applications being developed that will result in some sort of action. It is a relatively small part now, but it is clearly where the future [of business intelligence] is.”



Jim Goodnight, CEO, SAS, 2007:

“Until recently, business intelligence was limited to basic query and reporting, and it never really provided that much intelligence…”



Jim Davis, VP Marketing, SAS, 2007:

“In the next three to five years, we’ll reach a tipping point where more organizations will be using BI to focus on how to optimize processes and influence the bottom line…”


SolveITSoftware What is “intelligence”?

Three major ingredients:

• Ability to predict

• Ability to optimise

• Ability to adapt


SolveITSoftware Prediction

• Evolutionary Programming aimed at achieving intelligence (L. Fogel 1966)

• Intelligence was viewed as adaptive behaviour

• Prediction of the environment was considered a prerequisite to adaptive behaviour

• Thus: capability to predict is key to intelligence (L. Fogel 1966)


SolveITSoftware Smart decisions

• Expert systems

• Games

• Search techniques

etc.


SolveITSoftware Adaptive products

Adaptive products are the way of the future:

Car transmissions

TV

Shoes

AI, in general…


SolveITSoftware

Businesses and government agencies are interested in two fundamental things:

• Knowing what will happen next (prediction); and

• Making the best decision under risk and uncertainty (optimisation).

The goal is to provide AI-based solutions for modelling, simulation, and optimisation to address these two fundamental needs.

Basic observation


SolveITSoftware Business Intelligence

KNOWLEDGE

DECISION

DATA

OPTIMISATION

PREDICTION

OUTCOME

Adaptive

INFORMATION


SolveITSoftware Technology Platforms

SolveIT Optimisation Platform

SolveIT Prediction Platform

Classic OR methods, and:• Evolutionary Algorithms• Swarm Intelligence• Simulated Annealing• Tabu Search • Co-Evolutionary Systems• Ant Systems

Classic forecasting methods, and:• Neural Networks• Fuzzy Systems• Genetic Programming• Agent-Based Systems• Data Mining Techniques• Rough Sets


SolveITSoftware

A major U.S. automaker sells 1.2 million off-lease cars each year on various auction sites.

Each day, a remarketing team uses business intelligence tools and reports to decide where to ship 4,000 – 7,000 off lease cars.

The problem is impacted by demand, depreciation, transportation schedules, cost of capital, risk, changes in market conditions, and the volume effect.

ABI – Example #1


SolveITSoftware Car Distribution System


SolveITSoftware Planning & Scheduling Optimisation

• Manufacturing production:


SolveITSoftware

• Media Allocation(multiobjective):

Planning & Scheduling/ Predictive Modelling


SolveITSoftware

Some research issues


SolveITSoftware

• Precise models of a problem

• Robustness of solutions

• Return of several solutions

• Time changing environments

• Handling constraints

• Large (and complex) search spaces

Issues


SolveITSoftware Models of a problem

Problem => Model => Solution

Problem-solving is a two-step process:(1) Building a model of a problem, and

(2) Solving the model


SolveITSoftware Cost functions

cost

amount


SolveITSoftware Robustness of solutions

It is important to minimize undesirable changes required by unforeseen events.

A B

solution

quality


SolveITSoftware Return of several solutions

Evolutionary algorithms can be structured to (1) give diverse near-optimal solutions and

(2) deal with tradeoffs present in multi-objective problems.

cost

time


SolveITSoftware Size of search space

Assume we deal with the following problem:

optimize f (x1, x2,..., x100)

where f is very complex and xi is 0 or 1.

The size of the search space is 2100~ 1030.

The exhaustive search is out of question!

2

i


SolveITSoftware Optimisation problem

Optimize:

f(x, y) = 100(x - y)2 + (1 – x)2

where -2.048 <= x, y <= 2.048(Rosenbrock’s function, F2)

2


SolveITSoftware Optimisation problem

What would happen, if we have additional constraints?

E.g., x <= log(y + 3)sin(x) <= 3y2 + 1


SolveITSoftware Search space


SolveITSoftware Main question

• YES: easy implementation, low efficiency

• NO: many issues to consider; usually much better results!

Should we consider infeasible individuals harmful and eliminate them from the population?


SolveITSoftware Further questions:

• How to compare two feasible individuals?• How to compare two infeasible individuals?• How to compare an feasible individual with infeasible one?• Should we penalize infeasible individuals? • Should we “repair” infeasible individuals?• Should we use specialized operators which produce feasible

individuals only?• Should we use decoders?• Should we concentrate on the boundary between feasible and

infeasible areas of the search space?• How to find feasible solutions?

If we keep infeasible individuals in the population, we have to address several issues:


SolveITSoftware Penalties

• Should we keep W constant?• Should we increase W together with

generations?• Should we use some adaptive

mechanism which influences the value of W on the basis of the feedback from the search?

• Should we include the value of W as a part of individuals and trust the

General idea:Eval(x) = f(x) + W*penalty(x)


SolveITSoftware Repairs

• Should we repair for evaluation purpose only (so-called Baldwin effect)?

• Should we replace the original individual x by its repaired version x’ (so-called Lamarckian evolution)?

• Are there any other possibilities? ( D i ’ i 5% l )

General idea:Transform infeasible x into

feasible x’ by applying some problem-specific algorithm


SolveITSoftware Specialized operators

Genocop 3.0 – an experimental system to take:– Arbitrary objective function (continuous

variables)– Set of linear constraints

to produce the optimal solution.

System available from www.cs.adelaide.edu/~zbyszek


SolveITSoftware Decoders

General idea:

The original spaceEncoded space


SolveITSoftware Decoders

Genocop V: universal tool for nonlinear optimization problems with nonlinear constraints!

The system accepts an arbitrary function (continuous variables) and any number of nonlinear constraints.

System available from www.cs.adelaide.edu/~zbyszek


SolveITSoftware Andy Keane’s function

G2(x) = (Σ cos4(xi) – 2 Π cos2(xi))/sqrt(Σ i xi2),

where 0 ≤ xi ≤ 10 and

Π xi ≥ 0.75


SolveITSoftware Boundary operators

For some problems, it is possible to design boundary operators, which generate offspring as a new boundary point.

E.g., consider constraint: xy <= 5

For two boundary parents, (x1,y1) and (x2,y2), an offspring: (sqrt(x1*x2), sqrt(y1*y2))is also a boundary point.


SolveITSoftware Parameter tuning

Parameter tuning: the traditional way of testing andcomparing different values before the “real” run

Problems:• users mistakes in settings can be sources of errors

or sub-optimal performance• costs much time• parameters interact: exhaustive search is not

practicable• good values may become bad during the run


SolveITSoftware Parameter control

Parameter control: setting values on-line, during the actual run, e.g.,

• predetermined time-varying schedule p = p(t)• using feedback from the search process• encoding parameters in chromosomes and rely on

natural selection

Problems:• finding optimal p is hard, finding optimal p(t) is harder• still user-defined feedback mechanism, how to

“optimize”?• when would natural selection work for strategy

parameters?


SolveITSoftware Various Casesf(x)

f(x), c1(x), c2(x),…

f1(x), f2(x),…

f1(x), f2(x),… , c1(x), c2(x),…

f(x,t)

f(x,t), c1(x), c2(x),…

f(x), c1(x,t), c2(x,t),…

f(x,t), c1(x,t), c2(x,t),…

f1(x,t), f2(x,t), … , c1(x,t), c2(x,t),…


SolveITSoftware Heuristic vs. Problem

EvaluationFunctions

Heuristic Method Problem


SolveITSoftware Evaluation Functions

Some researchers acknowledged that a real world scenario might be a bit more complex:

• Noise

• Robustness

• Approximation

• Time-changing environments


SolveITSoftware Noise

Sometimes evaluation functions return results of randomised simulations. The common approach in such scenarios is to approximate a noisy evaluation function eval by an averaged sum of several evaluations:

eval(x) = 1/q Σi=1…q (f(x) + zi),

where x is a vector of design variables (i.e., variables controlled by a method), f(x) is the evaluation function, zi represents additive noise, and n is the sample size. Note that the only measurable (returned) values are f(x) + z.


SolveITSoftware Robustness

Sometimes slightly modified solutions should have quality evaluations (thus making the original solution robust). The common approach to such scenarios is to use evaluation function eval based on the probability distribution of possible disturbances δ, which is approximated by Monte Carlo integration:

eval(x) = 1/q Σi=1…q f(x + δi).

Note that eval(x) depends on the shape of f(x) at point x; in other words, the neighbourhood of x determines the value of eval(x).


SolveITSoftware Approximation

Sometimes it is too expensive to evaluate a candidate solution. In such scenarios, evaluation functions are often approximated based on experimental or simulation data (the approximated evaluation function is often called the meta-model). In such cases, evaluation function eval becomes:

eval(x) = f(x) + E(x),

where E(x) is the approximation error of the meta-model. Note that the approximation error is quite different than noise, as it is usually deterministic and systematic.


SolveITSoftware Dynamic environments

Sometimes evaluation functions depend on an additional variable: time. In such cases, evaluation function eval becomes:

eval(x) = f(x, t),

where t represents time variable. Clearly, the best solution may change its location over time. There are two main approaches for handling such scenarios: (1) to restart the method after a change, or (2) require that the method is capable of chasing the changing optimum.


SolveITSoftware The most “real” case

However, it seems the largest class of real world problems is not included in the above four categories. It is clear that in many real world problems the evaluation functions are based on predictions of the future values of some variables. In other words, evaluation function eval is expressed as:

eval(x) = f(x, P(x, y, t)),

where P(x, y, t) represents an outcome of some prediction for solution vector x and additional (environmental, beyond our control) variables y at time t.


SolveITSoftware More info…

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Adaptive Business Intelligence - IEEEewh.ieee.org/cmte/cis/mtsc/ieeecis/tutorial2007/CEC2007/... ·...

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