group technology rationale to assign roommates in student dormitories

trainerhungarianΤεχνίτη Νοημοσύνη και Ρομποτική

20 Οκτ 2013 (πριν από 3 χρόνια και 1 μήνα)

66 εμφανίσεις

Assessing the performance of bio
-
inspired heuristics for single row
layout problems

Berna ULUTAŞ
B.Burak ULUTA
Ş


Eskisehir Osmangazi University

Department of Industrial Engineering

30 June
-
2 July 2010 Oprerations Research and Industrial Engineering Conference

Sabanci University , Istanbul Turkey

Outline


Single

row

facility

layout

problem

(SRFLP)


Literature

review


The

algorithms

to

solve

SRFLP


Experiments

for

the

test

problems



Results

and

discussion

The
Single Row
Facility Layout Problem


Arrangement of
n

machines/departments on a straight
line in a given direction



Named as


One dimensional layout


Linear Ordering Problem (LOP) where all machines have unit
length


Objective

is

to

minimize

the

total

material

handling

costs

and

to

find

the

optimum

layout

for

machines

in

one

dimension


SRLP examples

SRLP examples
(cont’)

Literature review for
SRFLP


Karp

and

Held

(
1967
)

Dynamic

programming


Simmons

(
1969
,

1971
)

Branch

and

bound

algorithm


Love

and

Wong

(
1976
)

Linear

mixed

integer

program


Picard

and

Queyranne

(
1981
)

Dynamic

programming


Heragu

and

Kusiak

(
1988
)

T
wo

heuristic

constructive

algorithms



Heragu

and

Kusiak

(
1991
)

Nonlinear

model


Braglia

(
1997
)

Heuristics

derived

from

scheduling

problem



Djellab

and

Gourgand

(
2001
)

A
n

iterative

construction

procedure



Ponnambalam

and

Ramkumar

(
2001
),

Chen

et

al
.

(
2001
),

Ficko

et

al
.

(
2004
)

GA





Literature review for
SRFLP
(cont’)


Ponnambalam and Ramkumar (2001)
GA+SA


Ponnambalam et al. (2005)
Hybrid search heuristics
(Flow Line Analysis (FLA)+SA, FLA +GA, FLA+GA+SA)


Anjos

et

al
.

(
2005
)

Semidefinite

programming

relaxation


Solimanpur

et

al
.

(
2005
)

A
CO


Amaral

et

al
.

(
2005
)

S
A


Amaral

(
2006
)

Linear

mixed

integer

program


Teo

and

Ponnambalam

(
2008
)

ACO+PSO


Amaral

(
2009
)

Linear

programming



Samarghandi

and

Eshghi

(
2010
)

TS


Samarghandi

et

al
.

(
2010
)

PSO



The dimensions of the machines either are not considered or
are assumed to be equal (Braglia, 1996)


The locations of facilities are predetermined (Kumar et al.,
1995; Braglia, 1996)


The size of the machines is only considered in the physical
layout of the machines (Heragu and Kusiak, 1988)




The method requires too much time to construct a layout,
especially when applied to large instances of the SRFLP (Anjos
et al., 2005).

The problem in concern


Large size departments


Unequal dimensions (length)


No clearence


No backtracking



Biologically inspired computing


genetic algorithms


evolution


neural networks


the brain


artificial immune systems


immune system


emergent systems


ants, bees


rendering (computer graphics)


patterning and
rendering of animal skins, bird feathers, mollusk shells
and bacterial colonies


cellular automata


life



Clonal

selection



Bacterial foraging

CLONAL SELECTION ALGORITHM

AIS
algorithms

Population
based

Network
based

Clonal
selection

Bone
marrow

Negative
selection

Continuous
models

Discrete
models




m
5

m
3

m
2

m
1

m
4

m
6

Enc
oding


[m
5

m
2

m
1

m
6

m
4

m
3
] = [5 2 1 6 4 3]

Representation

Step 1: Initialization


The antibodies are randomly generated based on the
predetermined population size


For each antibody in the population, the objective
function value is calculated

Step 2: Evaluation

Selection of antibodies are made based on their objective
values


For example, layouts that have lower cost values have the
largest share on the wheel

Step 3: Selection and cloning

Step 4: Hypermutation


For

each

antibody

in

the

population,

hypermutation

operator

is

applied




Then,

objective

value

of

the

mutated

antibody

is

calculated



If

there

is

an

improvement,

the

existing

antibody

is

replaced

with

the

mutated

one



Step 5: Receptor editing operator


A

percentage

of

the

antibodies

(worst

R
%

of

the

whole

population)

in

the

antibody

population

are

eliminated

and

randomly

created

antibodies

are

replaced

with

them
.



This

procedure

enables

the

algorithm

to

search

new

regions

in

the

solution

space
.



Step 6: Repeat Steps 2
-
5 until termination
criterion is met


The algorithm is terminated if the best feasible solution
has not improved after a predetermined number of
iterations (i.e., 250).

BACTERIA FORAGING ALGORITHM

BFA


is based on the foraging (i.e., searching food) strategy of
Escherichia coli
bacteria

Step 1.Initialization


The bacteria are randomly generated based on the
predetermined population size


Step 2. Chemotaxis


is a foraging strategy that implements a type of local
optimization


the bacteria try to climb up the nutrient concentration,
avoid noxious substances


search for ways out of neutral media


is similar with biased random walk model

Step 3. Swarming


the bacteria move out from their respective places in
ring of cells by moving up to the minimal value


bacteria usually tumble, followed by another tumble or
tumble followed by run or swim


if the cost at present is better than the cost at the
previous time or duration then the bacteria takes one
more step in that direction


1.
N
clones

copies of the solution are generated so that there
are (
N
clones
+1) identical solutions

2.
Inverse mutation is applied to each of the
N
clones

copies

3.
The solution surviving the mutation is the non
-
dominated solution among the mutated solutions

4.
All other solutions are discarded

5.
Repeat the procedure for all the solutions in the
population

Tavakkoli
-
Moghaddam et al. (2007) A hybrid multi objective immune algorithm for a flow shop scheduling problem with
bi
-
objectives: Weighted mean completion time and weighted mean tardiness

Step 4. Reproduction



the

bacteria

are

stored

in

ascending

order

based

on

their

fitness




percent

of

the

least

healthy

bacteria

dies

and

others

split

into

two

which

are

placed

in

the

same

location



the

population

of

bacteria

remains

constant



Step 5. Repeat Steps 2
-
4 until
termination criterion is met


The algorithm steps are repeated until the termination
criterion is succeeded


Algorithm parameters


Population size : 10


Receptor editing / Reproduction rate : 10%

Problem set 1

Problems with optimum solutions

Problem set 2

Larger size test problem results for CSA

Problem set 2

Larger size test problem results for BFA

Problem set 3

Larger size test problem results for CSA

Problem set 3

Larger size test problem results for BFA

Conclusions and Results


The performance metrics:


solution quality


speed of convergence


frequency of hitting the optimum




CSA and BFA outperformed best known results available in
the literature



CSA obtained better results than BFA




Best
known

Better

Worse

Problem
set 1

CSA

100

0

0

BFA

100

0

0

Problem
set 2

CSA

81.25

6.25

12.50

BFA

12.50

6.25

81.25

Problem
set 3

CSA

35.00

45.00

20.00

BFA

15.00

5.00

80.00

Further studies


D
esign

of

experiments

to

determine

the

optimum

parameters



Experiments

with

different

termination

criteria

and

larger

population

size



New

strategies

to

improve

BFA

search

capabilities


Considering

the

elimination

and

dispersal

events

that

are

based

on

population

level

long
-
distance

mobile

behavior



Real

life

application

of

SRLP

THANKS

QUESTIONS?