Transcript A Hyper-Heuristic Multi-Criteria Decision Support System

A Hyper-Heuristic Multi-Criteria
Decision Support System for Ecoefficient Product life Cycle.
John R. Woodward (Computer Science)
and Nabil Gindy (Engineering)
The University of Nottingham Ningbo
China (UNNC)
OUTLINE
• Problem of eco-friendly design.
– Complex interacting systems
– Missing data?
– Ill defined problem – no overall measure.
Decision Support – analytical hierarchical process.
Machine Learning
– Heuristics
– Genetic Programming
Key Points
• To use decision support system to steer the
designer away from eco-unfriendly designs at
all stages in the design and manufacturing
process, saving the designer time.
Some problems
1. No agreed measure of environmental impact.
2. Concrete data may be unavailable.
3. Multi-dimensional Pareto design front.
Ill defined problem by its very nature.
Problems
• An in-depth understanding of design.
• And in-depth understanding of food-webs and
ecological niches etc.
• This is a complex system of interactions.
• How do we manage unforeseen
consequences.
• Analytical hieratical processes is one way of
combining different experts from the same
field and different fields.
The Product Life Cycle
Product Life Cycle
Material
Extraction
Material
Processing
Product
manufacture
&
Assembly
Product
Remanufacture
Product
Recycle
Product
Distributio
n
Product
Deployment
Product
Support/
Maintenanc
e
Product
Disassembly
Product
Final
Disposal
Sample influence diagram, showing
issues affected by the choice of a
certain material
A food web.
Problem of choosing Nappies
• Do I use reusable nappies or disposable?
• The cost of each is easy to calculate – a simple
linear programming decision making process.
• But what is the environmental cost of each
choice?
• Financial cost is immediate and transparent.
• Environmental cost is delayed and opaque.
No agreed measures
(energy? NO NO NO).
• Carbon emissions, Oil points scale, energy (?).
• These are quantifiable.
• How do we quantify the “loss of biodiversity”
against the clean-up cost of a river.
• Are we trying to compare apples and pairs.
• We do not have a universal measure of
environmental impact – and we never will!
(too complicated and multi-dimensional).
Concrete data may be unavailable.
• Even if we did have an agreed measure –
sometimes we could not calculate it and data
is not available.
• Data may be unavailable so need to combine
the opinions of experts.
• Statistical methods of ranking ect could be
employed to deal with these decisions.
Decision Support 1
•
•
•
•
•
•
•
An algorithm or software.
Does not make decisions for us.
Supports human decision making.
Not trying to model cognitive processes.
Decision support is already used in many areas.
Method – analytical hierarchical process.
Example – university careers advisor – marriage
guidance (?).
Decision Support 2
• The knowledge does not exist in the mind of a
single expert, but in the minds of a society of
experts who may be distributed
geographically and culturally and racially.
• A decision support system is a way to
overcome this, bringing together the collective
knowledge.
Machine Learning
• Machine Learning offers a number of
techniques to assist decision support.
• E.g. fuzzy data, missing values, unknown
values.
• statistical underpinning.
• GP as HH can construct and decompose
human designed heuristics.
Heuristics and Hyper-Heristics
• Heuristic is a rule of thumb, e.g. don’t buy the
cheapest wine on a restaurant menu.
• Heuristic has a strict computational definition.
• A heuristic can be thought of as an
approximation.
• Hyper-heuristics is a machine learning
methodology of combining atomic heuristics.
• Collections of heuristics can outperform single
heuristics. (an AHP is just a heuristic!!!)
Genetic Programming
• Genetic Programming generates programs at
random and uses “survival of the fittest” to
produce programs for a purpose.
• Like selective breeding of dogs, cattle etc.
• The representation is understandable by
humans (so some extent), unlike say artificial
neural nets.
• Human competitive solutions are now being
produced.
• We can evolve heuristics 
The Hyper-heuristic decision support
system.
Issues
• Pareto front of design solutions.
• Weighting criteria leads to ‘holes’ in the
design front.
• Clustering algorithms may decide what are
important exemplars at the Pareto front.
• Dealing with different levels of measurement
(Discrete, Categorical, Nominal, and Ordinal)
• Validation of the model?
• On-line learning and unsupervised learning.
Summary
• Eco-friendly design is difficult and requires
some sort of decision support.
• AHP is just one decision heuristic.
• Genetic Programming can supplement human
designed heuristics – an already established
framework.
• There are interesting issues machine learning
can assist the design process.