Selecting Design Alternatives

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Selecting Design Alternatives

• TMR4115
• Friday, September 6 2003
• Prof. Stein Ove Erikstad

Reality
Decision Models
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Selection problemsExample

• You work as a design engineer, and you are given
  the task of designing a high speed vessel for a
  particular route. Several alternative vessel
  concepts are relevant, catamaran, SES, SWATH,
  foil-cat. How can you decide which one is the
  best conceptual solution?

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?
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Selection problemsExample 2

• You work in the transport division of Statoil.
  You have just received more than 100 offers for
  shuttle tankers for 10 years contracts between
  Statfjord and Mongstad.
• The offers contain vessels with different cargo
  capacity, service speed, quality, age and price.
  How can you rationally and efficiently select
  the best offer?

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Related to the design process
Problem statement
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Classification of selection problems
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3
1-dimensional
1-dimensional
No uncertainty
Uncertainty
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2
Multi- dimensional
Multi- dimensional
No uncertainty
Uncertainty
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Examples

• Your task is to select the main machinery
  configuration for the vessel , and you have five
  different alternatives before you.
• Your objective is lowest weight. Only one
  criteria that can be determined with no
  uncertainty
• Your objective is the best combination of cost,
  weight and specific fuel consumption i.e.
  multiple criteria which can be determined with (a
  reasonable degree of) certainty

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Examples, continued...

• 3. You want the solution that will give you the
  lowest lifecycle maintenance cost, i.e. one
  criteria with an uncertain value
• 4. You want the solution with is preferable
  taking price, weight, maintenance cost and
  lifetime into consideration i.e.multiple
  criterie with (at least some) uncertain values

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Core concepts and notation

• Decision problem
• We must select a conceptual solution for a high
  speed passenger vessel
• Decision alternatives
• A - SES
• B - Catamaran
• C - FoilCat
• Attributes
• X1 - Max. service speed
• X2 - Motion behaviour
• X3 - Total annual cost
• Attribute values
• xA - (42, good, 8) (knots,
  , mEUR)
• xB - (34, medium, 5)
• xC - (48, excellent, 10)

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Objective and objective hierarchy

• Preferable features
• 1. Complete cover all relevant aspects
• 2. Operational measurable and relevant criteria
• 3. Decomposable splitting/grouping possible
• 4. Non-redundant avoid counting some features
  twice
• 5. As small as possible

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Objective hierarchy - example
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Objective hierarchyHigh speed vessel example
Max lifecycle profit
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Pareto
Dominans B is dominated by A D is dominated by
C G is not dominated

• Pareto-optimal set
• The set of non-dominated solution form a
  Pareto-optimal set

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The form of the value function
vi(xi)
vi(xi)
xi
xi
vi(xi)
vi(xi)
xi
xi
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One-dimensional value function v i(x i)

• 1. Determine scale
• 2. Normalise
• 3. Determine form of function

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Example
1
.

Determine scale
3. Determine form of function
min
Lower limit


x

27

kn
v
max
Upper limit

x

33

kn
1.0
v
2.
Normalize value function
v
(
27
)

0
.
0
v
v
(
33
)

1
.
0
0.0
v
33
27
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The Assumption of an additiv value function
Examples of dependencies between the attributes
where
X1 max speed X2 motion behaviour X3 - total
annual cost
i.e.
we are willing to accept high annual cost for a
vessel with a high service speed IF the motion
characteristics are good. If, on the contrary,
the motion characteristics are bad, we are not
able to exploit the speed potential, and thus not
willing to pay much for a high max service speed.
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Practical approaches to selection problems

• 1) Determine an objective hierarchy with
  corresponding attributes
• 2) Determine weights for each attribute, such
  that S?i 1
• 3) Determine upper and lower limits for each
  attribute, and give these the values 1.0 and 0.0,
  respectively. Choose the form of the value
  function default is linear.
• 4) For each attribute Evaluate the different
  alternatives, use the value function to determine
  the value
• 5) Calculate for each
  alternative. Choose the alternative with the
  highest value.

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Decisions under uncertainty

• Types of uncertainty None, known, full.
• Risk concepts (Risk Probability
  Consequence). Risk aversion/preference.
• Rationality We ACT lite rasjonelt (eks. St.
  Petersburg paradox, Lotto, etc.), or not very
  CONSEQUENT (e.g. Lotto vs. bonds, stocks). Risk
  premium.
• Utility structures under uncertainty Minmax -
  expected value
• Problems by such calculations Moral model
  related

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Warning

• The main purpose here has been to discuss a
  RATIONAL PLATFORM for decision-making NOT to
  give a recipe for how such decision SHOULD be
  made
• The most important result from this process is
  not a definite answer but as a documentation
  of the process leading to the decision