interaction theory - new paradigm in solving the traveling salesman problem (tsp)
DESCRIPTION
Interaction Theory has been developed by Professor Anang Zaini Gani from Institut Teknologi Bandung, Indonesia since 1966. This theory seeks to revolutionize the way Traveling Salesman Problem (TSP) is solvedTRANSCRIPT
![Page 1: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/1.jpg)
INTERACTION THEORY
NEW PARADIGM FOR SOLVING THE
TRAVELING SALESMAN PROBLEM
(TSP)
Department of Industrial Engineering
Institut Teknologi Bandung
Bandung, Indonesia [email protected]
Anang Z.Gani
![Page 2: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/2.jpg)
2
![Page 3: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/3.jpg)
3
![Page 4: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/4.jpg)
4
INTRODUCTION
OBJECTIVE
BACKGROUND
INTERACTION THEORY
COMPUTATIONAL
EXPERIENCES AND
EXAMPLE
CONCLUSION
AZG2012
(Keywords: Graph; P vs NP; Combinatorial Optimization;
Traveling Salesman Problem; Complexity Theory; Interaction
Theory; Linear Programming; Integer Programming ;
Network).
![Page 5: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/5.jpg)
5
The area of Applications :
Robot control
Road Trips
Mapping Genomes
Customized Computer Chip
Constructing Universal DNA Linkers
Aiming Telescopes, X-rays and lasers
Guiding Industrial Machines
Organizing Data
X-ray crytallography
Tests for Microprocessors Scheduling Jobs
Planning hiking path in a nature park
Gathering geophysical seismic data
Vehicle routing
Crystallography
Drilling of printed circuit boards
Chronological sequencing
AZG2012
![Page 6: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/6.jpg)
The problem of TSP is to find the shortest
possible route to visit N cities exactly once and
returns to the origin city.
The TSP very simple and easily stated but it is
very difficult to solve.
The TSP - combinatorial problem
the alternative routes exponentially increases
by the number of cities.
1/2 (N-1)!
4 cities = 3 possible routes
4 times to 16 cities = to 653,837,184,000.
10 times to 40 cities =1,009 x1046
IF 100,000 CITIES...... (possible routes?)
AZG2012
6
![Page 7: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/7.jpg)
SOAL 33 KOTA
ALTERNATIVE RUTE 32!/2 =
131.565.418.466.846.756.083.609.606.080.000.000
KOMPUTER PALING TOP $ 133.000.000 ROADRUNNER CLUSTER DARI UNITED STATES DEPARTMENT OF ENERGY DIMANA 129.6600 CORE MACHINE TOPPED THE 2009 RANKING OF THE 500 WORLD’S FASTES SUPER COMPUTERS, DELIVERING UP TO 1.547 TRILION ARITHMETIC OPERATIONS PER SECOND.
DIPERLUKAN WAKTUN 28 TRILIUN TAHUN
SEDANGKAN UMUR UNIVERS HANYA 14
MILIAR TAHUN
INI MEMANG GILA
AZG
2012
![Page 8: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/8.jpg)
7 (tujuh) problem
matematikapada
millenium ini
1. The Birch and Swinnerton-
Dyer Conjecture
2. The Poincare Conjecture
3. Navier-Stokes Equations
4. P versus NP Problem
5. Riemann Hypothesis
6. The Hodge Conjecture
7. Yang-Mills Theory and The
Mass Gap Hypothesis.
AZG
2012
![Page 9: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/9.jpg)
9
. "The P versus NP
Problem" is considered one
of the seven greatest
unsolved mathematical
problems
AZG2012
![Page 10: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/10.jpg)
10
One important statement about the NP-
complete problem (Papadimitriou & Steiglitz) :
a. No NP-complete problem can be solved by
any known polynomial algorithm (and this
is the resistance despite efforts by many
brilliant researchers for many decades).
b. If there is a polynomial algorithm for any
NP-complete problem, then there are
polynomial algorithms for all NP-complete
problems.
THIS IS CHALLENGE TO PROVE
P= NP MUST BE PURSUED!
AZG2012
![Page 11: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/11.jpg)
TSP dealing with the resources :
1. Time (how many iteration it takes to
solve a problem)
2. space (how much memory it takes to
solve a problem).
THE MAIN PROBLEM :
1. THE NUMBER OF STEPS (TIME) INCREASES
EXPONENTIALLY ALONG WITH THE INCREASE IN
THE SIZE OF THE PROBLEM.
2. HUGE AMOUNT COMPUTER RESOURCES ARE
REQUIRED
NEW PARADIGM (BREAKTHROUGH)
AZG2012
11
![Page 12: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/12.jpg)
PARADIGM
OLD NEW
1. LP & DERIVATIVES2. HEURISTIC (PROBABILISTIC)3. PROCEDURE IS
COMPLICATED4. NEEDS RESOURCES OF TIME
AND MEMORY UNLIMITED5. CHECKING ALL ELEMENTS
6. P = NP VS P ≠ NP ?7. KNOWLEDGE IS HIGH
8. LONG OPERATING TIME
1. INTERACTION THEORY2. DETERMINISTIC3. PROCEDURE IS SO SIMPLE4. RESOURCES NEED IS
LIMITED
5. CHECKING LIMITED ELEMENTS (PRIORITY)
6. P=NP7. SIMPLE ARITHMATIC
8. SHORT OPERATING TIME (EFFICIENT AND EFFECTIVE)
AZG2012
12
![Page 13: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/13.jpg)
SUMMARIZES THE MILESTONES OF SOLVING
TRAVELING SALESMAN PROBLEM.
Year Research Team Size of Instance
1954 G. Dantzig, R. Fulkerson, and S.
Johnson
49 cities
1971 M. Held and R.M. Karp 64 cities
1975 P.M. Camerini, L. Fratta, and F.
Maffioli
67 cities
1977 M. Grötschel 120 cities
1980 H. Crowder and M.W. Padberg 318 cities
1987 M. Padberg and G. Rinaldi 532 cities
(109,5 secon)
1987 M. Grötschel and O. Holland 666 cities
1987 M. Padberg and G. Rinaldi 2,392 cities
1994 D. Applegate, R. Bixby, V.
Chvátal, and W. Cook
7,397 cities
1998 D. Applegate, R. Bixby, V.
Chvátal, and W. Cook
13,509 cities
(4 Years)
AZG2012
13
![Page 14: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/14.jpg)
SUMMARIZES THE MILESTONES OF SOLVING
TRAVELING SALESMAN PROBLEM.
Year Research Team Size of Instance
2001 D. Applegate, R. Bixby, V. Chvátal,
and W. Cook
15,112 cities
(ca. 22 Years)
2004 D. Applegate, R. Bixby, V. Chvátal,
W. Cook and K. Helsgaun
24,978 cities
2006 D. Applegate, R. Bixby, V. Chvátal,
and W. Cook
85,900 cities
2009 D. Applegate, R. Bixby, V. Chvátal,
and W. Cook
1,904,711 cities
2009 Yuichi Nagata 100.000
Mona Lisa
AZG2012
14
![Page 15: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/15.jpg)
15
TECHNIQUE AND METHOD
FOR SOLVING TSP
• NEURAL NETWORK
• GENETIC ALGORITHM
• SIMULATED ANNEALING
• ARTIFICIAL INTELLEGENT
• EXPERT SYSTEM
• FRACTAL
• TABU SEARCH
• NEAREST NEIGBOR
• THRESHOLD ALGORITHM
• ANT COLONY OPTIMIZATION
• LINEAR PROGRAMMING
INTEGER PROGRAMMING
• CUTTING PLANE
• DYNAMIC PROGRAMMING
• THE MINIMUM SPANNING
TREE
• LAGRANGE RELAXATION
• ELLIPSOID ALGORITHM
• PROJECTIVE SCALING
ALGORITHM
• BRANCH AND BOUND
• ASAINMENT
HEURISTIC EXACT SOLUTION
AZG
2012
![Page 16: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/16.jpg)
16
OBJECTIVE FUNCTION
• d(i,j) = (direct) distance between
city i and city j.
z x(i, j)d(i, j)j1
n
i1
n
AZG
2012
![Page 17: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/17.jpg)
Constraints
• Each city must be “exited” exactly once
• Each city must be “entered” exactly once
x(i, j)j1
n
1 , i 1,2,...,n
x(i, j)i1
n
1 , j 1,2,...,n
Subtour elimination constraint
• S = subset of cities
• |S| = cardinality of S (# of elements in S)
• There are 2n
such sets !!!!!!!
x(i, j) Si , jS
1, S {1, 2,...,n}
AZG
2012
![Page 18: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/18.jpg)
18
NUMBER OF LINIER INEQUALITIES
AS CONSTRAINS IN TSP
• If n=15 the number of countraints is 1.993.711.339.620
• If n=50 the number of countraints 1060
• If n=120 the number of countraints 2 x 10179
or to be exact :
26792549076063489375554618994821987399578869037768707804846519432957724703086273401563211708807593998691345929648364341894253344564803682882554188736242799920969079258554704177287
AZG2012
Grotschel
![Page 19: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/19.jpg)
19
INTERACTION THEORY
AZG2012
![Page 20: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/20.jpg)
INTERACTION THEORY
In 1965 Anang Z. Gani [28] did research on the Facilities Planning
problem as a special project (Georgia Tach in 1965)
Supervision James Apple
Later, J. M. Devis and K. M. Klein further continued the original
work of Anang Z. Gani
Then M. P. Deisenroth “ PLANET” direction of James Apple
(Georgia Tech in1971)
Since 1966, Anang Z. Gani has been continuing his research and
further developed a new concept which is called “The Interaction
Theory” (INSTITUT TEKNOLOGI BANDUNG)
The model is the From - To chart the which provides quantitative
information of the movement between departments
AZG2012
20
![Page 21: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/21.jpg)
The model is the From - To chart the which
provides quantitative information of the
movement between departments (common
mileage chart on the road map).
The absolute value or the number of a
element as an individual of a matrix can not
be used in priority setting
the TSP matrix has two values,
1. the initial absolute value (interaction
value)
2. the relative value (interaction coefficient)
DIM = The Delta Interaction Matrix
AZG2012
21
![Page 22: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/22.jpg)
AZG2012
22
Two parallel lines
![Page 23: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/23.jpg)
Two parallel lines distorted
(Hering illusion)
AZG2012
23
![Page 24: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/24.jpg)
1 2 3 4
1 0 700 10 20
2 2 0 800 15
3 4 3 0 10
4 10 2 30 0
RELATIVE VALUE
AZG2012
24
![Page 25: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/25.jpg)
The formula for the interaction
coefficient ( ci,j ) is:
ci,j = xi,j2/(Xi. .X.j).
Xi. =
m
j 1
xij (i = 1 ……. m )
X.j =
n
i 1
xij (j = 1 ……. n )
AZG2012
25
![Page 26: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/26.jpg)
TSP
INTERACTION THEORY
TSP P=NP
GENERAL
AZG
2012
![Page 27: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/27.jpg)
APPLICATION OF THEORY INTERACTION
• Traveling Salesman Problem (Symmetric and Asymmetric, minimum and maximum).
• Transportation Problem.• Logistic.• Assignment problem.• Network problem• Set Covering Problem.• Minimum Spanning Tree (MST)• Decision Making.• Layout Problem.• Location Problem• Financial Analysis.• Clustering.• Data Mining
AZG
2012
![Page 28: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/28.jpg)
28
TSP(Symmetric & Asymmetric
Transportation
ProblemsGraph
Network Problems
Scheduling
Decision
Making Clustering
Layout
Problems
Routing Data Mining
Financial
Analysis
Location
Problems
Assignment
Problems
AZG
2012
![Page 29: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/29.jpg)
29
Computer Science
Transportasi
Militer
Ekonomi
Strategi
Finansial
Distribusi / Logistik
Psikologi
Kimia
Fisika
Biologi
Operations Research
Telekomunikasi
Industri Sosial
AZG2012
![Page 30: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/30.jpg)
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40 50 60 70 80
38
86
16
6185
91
100
98
37
92
5993
9996
6
89
5218
83
60
584
17
45
8
46
47
36
49
64
6390
32
1062
11
19
48
82
788 31
70
30
20
66
71
65
35
34
7881
951
33
793
7776
50
1
69
27
10153
28
26
1280
68
29
24
54
55
254
39
6723
56
75
41
2274
7273
21
4058
1394
95
97
87
2
57
1543
42
14
44
Route for 101 cities ( 8 Optimal solutions)
AZG2012
30
![Page 31: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/31.jpg)
Portrait of Mona Lisa with Solution of a Traveling
Salesman Problem. Courtesy of Robert Bosch ©2012
( 7 Optimal solutions)
AZG2012
31
![Page 32: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/32.jpg)
• The conclusion is that the
Interaction Theory creates a new
paradigm to the new efficient and
effective algorithm for solving the
TSP easily (N=NP).
• Overall, the Interaction Theory
shows a new concept which has
potential for development in
mathematics, computer science
and Operations Research and their
applications
CONCLUSIONAZG2012
32
![Page 33: Interaction Theory - New Paradigm in Solving the Traveling Salesman Problem (TSP)](https://reader034.vdocuments.mx/reader034/viewer/2022051314/559b369f1a28ab49638b4891/html5/thumbnails/33.jpg)
33
THANK
YOU
AZG2012
SIMPLICITY IS
POWER