valuasi dan komersialisasi teknologi (2009) yandra arkeman lien herlina aji hermawan 1
TRANSCRIPT
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Valuasi dan Komersialisasi Teknologi (2009)
Yandra Arkeman
Lien Herlina
Aji Hermawan
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Perkuliahan:
1 jam kuliah (Jum’at) 3 jam responsi (Rabu)
Asisten : Nisa Zahra, Banun
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Topik yang akan dipelajari :
Valuasi teknologi (hasil inovasi) Komersialisasi teknologi
Termasuk di dalamnya: Inovasi Teknologi Technopreneurship HAKI (Hak Atas Kekayaan Intelektual)
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INNOVATION CULTURE IN JAPAN IN THE AREA OF INFORMATION AND COMPUTER TECHNOLOGY
A special gift from JAPAN for students of TIN from
YANDRA
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INTRODUCTION
The key of success of Japan and other developed countries INNOVATION
Everyday scientists and engineers in Japan think about new products, new process, new management techniques and new methods for improving their quality of life
This innovation culture should also be adopted by students of Department of Agroindustrial Technology
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Example of Recent Innovation in Japan High speed train (Shinkansen) :
250 km/hr 500 km/hr Maglev (Magnetic levitation systems)
Aeroplane jet engine Roll Royce USA Honda Japan (not only motor bike and car)
Robotics and Aerospace technology, etc IMTS (Intelligent Mode Transportation
Systems) driverless car
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Some Photographs in Japan
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INNOVATION IN AGROINDUSTRY Product Innovation
New products, new materials Examples: bioplastics, bio-diesel, bio-lubricants, leather
products, etc Process Innovation
New process, new technology for producing products Examples: Using new technology for producing bioplastics
from sweet-potato, etc System Innovation
New management techniques Examples: Supply chain management, lean and agile
agroindustrial systems, zero-waste management
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Methods Innovation New methodology, new algorithms, new
information technology techniques Examples: artificial intelligence, meta-heuristics,
genetic algorithms, multiobjective optimization, etc
FOCUS OF MY POSTDOCTORAL RESEARCH IN JAPAN
(Details of my research results will be discussed in the following slides)
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Research Topic : Innovation in Computer and Information
Technology (Genetic Algorithms) for Agroindustry Researchers :
Dr. Yandra (TIN-IPB, Indonesia) Prof. Hiroyuki Tamura (Kansai University, Japan)
Host Institution : Department of Electrical Engineering and
Computer Science, Kansai University
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RESEARCH BACKGROUND
Many real-world problems in engineering design, agroindustry, decision making and information technology involve simultaneous optimization of multiple objectives
The principle of multi-objective optimization is different from that in a single objective optimization
Single objective optimization the goal is to find the best solution ( MAX or MIN)
Multi-objective optimization: No single optimal solution (due to conflicting objectives) Compromise solutions or Pareto-optimal solutions Then, choosing the most preferred solution for implementation
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SINGLE OBJECTIVE OPTIMIZATION
x
Max
F (x).
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Objective 1
Ob
ject
ive
2nadir solution
utopian solution
feasibleregion
infeasibleregion
Pareto-optimal solutions
MULTIOBJECTIVE OPTIMIZATION(Minimizing Obj.1 and Minimizing Obj.2)
Dominated solutions
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The aim of this research is: to develop a new computer-based methodology for multi-
objective optimization to apply this new methodology in agroindustry and other areas
The development of this methodology consists of two steps: Developing a new multi-objective genetic algorithm for finding a
well diverse Pareto-optimal solutions Developing an expert system for selecting the most preferred
solution based on higher-level information
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Genetic Algorithm
Expert System
Pareto-optimum solutions
Final solution
Intelligent DSS
Multi-objectiveproblems
Z1
Z1
Z2
Z2
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Main Program
Initialize Generation ReportInit-Data
Init-Pop
Init-Report
Mutation
Crossover
Selection
Old-PopNew-Pop
New-Pop2
New-Pop3
NDS+CD
ADM
New-Pop3
Pareto-optimum solutions
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One of the methods to process higher level information for selecting the most preferred solution is an expert system (ES)
The basic ideas of an ES: Expertise is transferred from a human into a computer
stored in a knowledge-base The computer can make inferences and arrive at
specific conclusion Then computer gives advices and explains, if
necessary, the logic behind the advice
EXPERT SYSTEMS
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APPLICATION FOR AGROINDUSTRY
Agroindustrial Supply Chain Management (Agro-SCM) :
The management of the entire set of production, transformation/processing, distribution and marketing activities
in agroindustry by which a consumer is supplied with a
desired product Agro-SCM is more complicated than manufacturing SCM
agricultural products are perishable
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..….
..…
…..
Agroindustry 1
Agroindustry j
Farming 1
Farming i
Customer 1
Customer k
Description of Agro-SCM Model
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ICIFICIPXCAYCB i
I
iij
J
jjij
I
i
J
jijjk
J
j
K
kjk
TSCCMin
111 11 1
:
kk
J
jjk DY
1
jj
K
kjk CapY
1
ii
J
jij SX
1
jj
K
kjk
I
iij IYX
11
Subject to:
Mathematical Model for Agroindustry SCM
IPIFIPIPXPAYPB i
I
iij
J
jjij
I
i
J
jijjk
J
j
K
kjk
ENDPMin
111 11 1
:
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Optimization objectives : First Objective:
Minimizing Total Supply Chain Cost (TSCC) that consists of transportation and inventory costs
Second Objective : Minimizing Expected Number of Deteriorated Product
(ENDP) Maximizing quality Essential for agroindustry
As both objectives are conflicting : No single optimum solution Pareto-optimum or non-dominated solutions or trade-off
solutions
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Optimization variables : The amount of raw materials to be transported from
farming to agroindustry (Xij) The amount of products to be transported from
agroindustry to consumer (Yjk)
Inventories at agroindustry (Ij) and farming (Ii) Subject to some constraints, such as:
Demand constraint Supply constraint Inventory constraint Production capacity constraint
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A case study 2-farming, 2-factory and 2-customer (2 x 2 x 2 SCM-problem)
Compare the performance of h-NSGA-II and original NSGA-II
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12 9 4 23
YA1YA2 XPA XPB
Chromosome Structure for 2x2x2 Agro-SCM
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12 9 4 23
8 17 41 15
12 9 41 15
8 17 4 23
P1
P2
C1
C2
Crossover
crossover point
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12 9 41 15 C1
12 11 41 15 C1’
*
mutation point
Mutation
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Multi-objective genetic algorithms were then executed with the following parameters: Crossover Probability (Pc) 0.9, 0.8, 0.7
Mutation Probability (Pm) 0.05, 0.01, 0 Population Size = 20 Number of replication = 5
Output of h-NSGA-II and original NSGA-II are presented in the following figures
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14
14.5
15
15.5
16
16.5
800 850 900 950 1000 1050 1100 1150
TSCC
EN
DP
Pareto-optimum solutions of heterogeneous NSGA-II
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14.2
14.4
14.6
14.8
15
15.2
15.4
15.6
15.8
16
800 850 900 950 1000 1050 1100 1150
TSCC
EN
DP
Pareto-optimum solutions of original NSGA-II
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The above figures show that: Pareto-optimum solutions could be found by h-NSGA-II in a
reasonable number of generation (i.e. 50) The solutions’ diversity of heterogeneous GA is better than its
original version There are 20 solutions produced by h-NSGA-II compare to 14
solutions produced by NSGA-II
These Pareto-optimum solutions were then fed to an expert system for selecting the most preferred solution
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RULES
Recommendations(A, B, …,T)
QS (H,M,L)
FB (H,M,L)
CI (E,G,F,P)
TP (A,B,C)
Dependence diagram of the Expert System
QS = Customer and social concern about product quality and safety
CI = Company’s cash inflow
TP = Type of product being handled
FB = Possibility to get additional funding or loans from Bank
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The input for expert system are: QS High CI Good TP type C FB Medium
Rule 064 was fired Choose SCM D-20: YA1 = 20
YA2 = 30
XPA = 38
XPB = 50 TSCC = 1048 m.u and ENDP = 14.1 units
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APPLICATION OPPORTUNITIES IN OTHER AREAS At this stage we have developed an intelligent and
integrated methodology for solving multi-objective problems
One application example Agro-SCM In principle this new methodology can be used in
many areas of research with minor adjustments This section :
Review recent developments of multi-objective optimization in the areas of engineering design and information technology
Discuss the application prospects of the methodology developed in this research in those particular areas
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An example Design of Turbojet Engines (2005) Used modified version of NSGA-II for multi-objective
optimization of thermodynamic cycle of ideal turbojet engines
The multiple and conflicting thermodynamic objectives used in this research are: Specific thrust Thrust specific fuel consumption Propulsive efficiency Thermal efficiency
ENGINEERING DESIGN
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This research can be enhanced by the introduction of an expert system for choosing the most preferred solution
Without this ES the task of selecting final solution for implementation becomes very difficult and complicated
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Also known as grid computing Seeks to unify geographically dispersed systems to
create one large and powerful system An example Application of GA for solving Task
Assignment Problem (TSAP) in grid computing (2006) Used HNNGA to solve tasks assignment of programs to
a number of processors to minimize a cost function This research only considers one objective
DISTRIBUTED COMPUTING SYSTEMS
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In fact, the objective of TSAP can be many, such as: Minimization of completion time of the entire programs (make-
span) Minimization of communication time among tasks Minimization of processor’s load, etc
These multiple (and conflicting) objectives can be solved using the intelligent and integrated methodology proposed in our research
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One important area in today’s information technology is intelligent data mining systems
An example Application of multi-objective genetic algorithms for data mining (2004)
Used NSGA-II for optimizing rule extraction process Multiple objectives:
Maximizing confidence of the rules Maximizing coverage of the rules
In the future this research can be improved by using new techniques such as h-NSGA-II as well as expert systems
DATA MINING
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RESEARCH FOR THE FUTURE
We have presented the development of h-NSGA-II and expert system for solving multiobjective problems
The usefulness of this methodology has been shown using an Agro-SCM case study
We have published 6 conference papers and submitted 1 journal paper. Another journal paper is in preparation now. Our computer programs are being copyrighted.
This research concludes that this new methodology is robust and reliable it can be used in many areas with some modifications
Further research we have formulized “Research Framework” for the future as presented in the next slide
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Strategic Research Framework
Single ObjectiveGenetic Algorithms
Multi ObjectiveGenetic Algorithms
Development ofNew Methods
Real-worldApplications
General purpose GA software
Hybrid with the other AI tools
Chromosome representation
Genetic operators
Initial population formation
Population diversity, convergence
Supply chain management
Scheduling, System Design
Grid computing, Data mining
Other areas in Mfg, Agroindustry, IT
GA-brain ©TM
Neural Networks
Fuzzy Logic
Expert Systems
Simulation
Bioinformatics
Parallel Meta-heuristics
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Institutions and Counterparts
Institutions: TIN-Fateta-IPB, Bogor, Indonesia Kansai University, Osaka, Japan
Prospective Counterparts: Centre for Advanced Manufacturing Research
(CAMR), University of South Australia Prof. Lee Luong, Dr. Sev Nagaligam
Institute of Intelligent Information and Communication Technology (IICT), Konan University, Japan Prof. Wuyi Yue
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National Engineering Research Centre for Information Technology in Agriculture (NERCITA), Beijing Dr.Xuzhang Xue
University Putra Malaysia (UPM) Prof. Amin Mohd. Soom
Nanyang Technological University (NTU), Singapore Prof. Khoo Li Peng
Funding : JSPS Japan Society for Promotion of Science Other competitive sources (National and
International)
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The ultimate goals (in the long term) : Truly intelligent machines Computers with their own
minds True electronic brains
for improving the qualityof human-life
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Research Topics for Students (S1,S2,S3) of TIN/TIP Innovation Culture Application of Single and Multiple Objective Genetic
Algorithms for Agroindustrial Systems Design (S1,S2,S3) Scheduling Project Management Plant Lay-out and Design Production Planning, etc
Optimization of Agroindustrial Supply Chain Networks Using Intelligent Systems (S1,S2,S3) All commodities of agroindustry (sugar, cacao, rice, fruit,
vegetable, oil-palm, sweet-potato, meat, fish, etc) New Genetic Algorithms for Solving Multiobjective
Problems (S3) Methodology development (!)
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Parallel Genetic Algorithms (S2, S3) Parallel and distributed computing New meta-heuristic techniques Super computer (multiple processor)
Bioinformatics (S2,S3) Application of information technology in molecular
biology, nuclear physics, chemical reaction, etc Computer Security (S1, S2, S3)
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