Fuzzy C-Means Clustering For The Optimal Portfolio Of Machinery by cuiliqing

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									           Fuzzy C-Means Clustering to
         Explore the Strategy Combination
               of Fuel Cell Industry

                   Hua-Kai Chiou, Gwo-Hshiung Tzeng
                  Benjamin J.C. Yuan & Chien-Pin Wang

                  National Chiao Tung University, Taiwan


IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA            1
 Agenda
     Introduction
     Multiple Criteria Decision Making Process
     Fuzzy C-Means Clustering
     Empirical Study
     Conclusions




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA   2
 Introduction

     According to the Protocol of United Nation Climatic
      Change Summit, there would be a 5% decrease of
      total emissions by 2012 against that of 1990, thus
      slowing the global warming process (Kyoto Protocol).

     Fuel cell is one type of electrical power device, but is
      unlike regular battery that is either thrown away after
      use or needing recharge.




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA              3
 …Introduction

     Fuel cell can be divided into Alkaline fuel cell (AFC),
      Phosphoric acid fuel cell (PAFC), Molten carbon fuel
      cell (MCFC), Solid oxide fuel cell (SOFC), Proton
      exchange membrane fuel cell (PEMFC), and Direct
      methanol fuel cell (DMFC).

     Fuel cell needs a type of fuel, hydrogen, to maintain
      its electrical power.




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA             4
 …Introduction

     Fuzzy AHP with multiple criteria decision
      analysis (MCDA) approach to find the preferred
      order of these development strategies.

     We further introduce fuzzy c-means to determine
      the optimal combination of these strategies.




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA        5
Table 1. Fuel Cell Technology Status, Research Institutes and Application Market
Fuel Cell        Electrolytes   Cond ucting Working      Energy      Oxide    Technology         Size        Main R esearch Lab .              Main Applied M arkets

Type                            Ion        Temp(o   C)               Agent    Status

Alkaline fuel                              Room          Pure        Pure                        1~10 0kW    1.Astris Ene rgy (Ca nada )        Traffic Signal
                  KOH           OH
cell (AFC)                                 Temp          Hydro gen   Ox ygen Successfully                    2.Zetek Pow er (UK)                Remo te Are as and
                  NaOH                     ~200                                                                                                  Islands Pow er
                                                                              applied to t he
                                                                                                                                                Automo bile Pow er syste m
                                                                                                                                                Aerosp ace an d De fense,
                                                                              aerosp ace
                                                                                                                                                 Ship
Proto n          FluroSulfoni   H         Room          Pure       Ox ygen   Fuel Cell Ca r     1~30 0kW    1. Ballard pow er (Cana da)            Residential Pow er
ex change        Acid                      Temp          Hydro gen Air        Sample                         2. H p ow er (US )                     Small Comm ercial
memb ran e       Memb ran e                ~100          Purified             Available, but                                                       Business
fuel cell                                                                                                    3. Hyd roge nics (C anad a)
                                                         Refo rme d           needs cost                                                          Traffic Signals
(PEMFC )                                                                                                     4. Unite d Technolo gy (US)
                                                         Gas                  dow n                                                               Porta ble products
                                                                                                             5. Ida Tech (US)
                                                                                                             6. Arthu r D.Little (US )            Remo te Are as and
                                                                                                                                                   Islands
                                                                                                             7. plug p ow er (US )
                                                                                                                                                  Automo bile Pow er
                                                                                                             8. Xellsis (Germ any)
                                                                                                                                                   System
                                                                                                             9. Du pont (U S)
                                                                                                                                                  Ship
                                                                                                             10. Arista L abs (US )

                  H3 PO4        H         100           Refo rme d Air       Available at       1~20 00kW 1.Inte rnatio nal Fuel Cell (US )    Residential Pow e r
Phospho ric                                ~200          Gas                  Dispersed                      2.Toshiba (J apan )                Small Com me rcial Pow er
                                                                              Pow er
                                                                                                                                                Remo te Are as   and
acid fuel cell                                                                Generator, High
                                                                                                                                                 Islands
                                                                              Cost an d low
(PAFC )                                                                       value for the
                                                                              remainin g he at
Molten           (Li-K )                   600           Pure       Air       Pilot Testing on   250~ 2000    Fuel Cell Energy (U S)            Cent ral Pow e r
                                CO3
                                  2
carbon fuel                                ~700          Refo rme d           250~ 2000kW        kW                                              Generation)
                  CO3                                    Gas /
cell (MCFC )                                                                  Needs Long er                                                     Remo te a reas a nd Islan ds
                                                         Natu ral             Product Life                                                      Automo bile pow er syste m
                                                         Gas
Solid ox ide     yttrium        O2        800           Pure        Air      Battery            1~10 0kW    1. Acument ric (US)                Cent ral Pow e r Gene ratio n
fuel cell        ox ide                    ~100 0        natural              Structu re/                    2. Ce ramic Fuel Cell (US)         Residential Pow e r
(SOFC)           and                                     gas                  Select low cost
                                                                                                             3. Delp hi Automotive syste m      Small Com me rcial Pow er
                                                                              technology
                 ZrO2                                                                                             (US )
                                                                                                             4. Fuel cell technology (U S)
                                                                                                             5. Global Thermoelect ric
                                                                                                                  (Ca nada )
                                                                                                             6.Seme n West House
                                                                                                                  (Germany )
Direct           Fluro          H         40~8 0O       Metha nol   Ox ygen Overcome            1~30 0kW    1. D CH Tech nology (US )          Low Pow er type po rtable
metha nol fuel   Sulfonic                                            / Air   Polymer                        2. Mot orola ( US)                  electronics
cell (DMFC )     acid                                                        Memb ran e                      3. Samsu ng (Korea )
                 Memb ran e                                                   Can be                        4. NE C、So ny (Jap an )
                                                                               Com merciali
                                                                               zed if CO                     5. Man hatta n Scientifics (US)
                                                                               Poison
                                                                               proble m
                                                                               resolved



IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                                                                                                           6
Table 2. Alternative Energy Development Policy from Major Countries (ITRI, 2001)

 Country      Projects/Actions              Alternative Energy/New Energy Policy

 Austria      Recycle energy power          50% of the financial aid for the equipment used on recycle energy in some
              generation project            areas.

 US           Technology development        US$2.4 billion to the original global warming foundation, increasing 40%
              foundation                    budget for developing the clean energy.

 Japan        Global warming action plan    Enhance technology development on fuel cell, solar energy and bio energy.
                                            Japan’s fuel cell development goal, 2.2 million watts by 2010.

 Germany      Revision on Power Parallel    Revision Act to target 10% of the recycle energy by 2010 and energy ratio
              Connection Bill               of 4%.

              Ecology tax act               Higher tax on petrochemical energy to reduce the total consumption of the
                                            petro fuel.

 Korea        1997 - 2006 energy            Plan to invest Korea Won 2,047 billion to develop energy protection
              technology development        technology, alternative energy, and clean energy. The alternative energy
              program                       includes fuel cell, solar energy, solar optic electronics and IGCC.

 Korea        New and recycle energy        Mainly to develop 12 alternative/ clean energy technologies, including fuel
              development act               cell. The incentives granted include 5% of the investment is tax free and up
                                            to 80% of the investment can enjoy the 5% low interest loan.


IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                                                         7
Table 3. 2000-2010 Market Forecasting on Global Fuel Cell Applications (Hester,2001)


                                     2005            2010              2000~2010
           Applications
                                  (US$ billion)   (US$ billion)   Compounded Growth Rate

        Power Generation               3.8            10.5               26.64%


          Vehicle Power                1.8            3.9                20.61%


         Portable Power                1.2            4.5                35.6%


             Others                    1.7            4.4                21.78%


              Total                    8.5            23.3




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                            8
 Multiple Criteria Decision Making Process

     Identifying the nature of problems;
     Defining the decision variables;
     Determining the appropriate evaluation method;
     Analyzing and evaluating step by step;
     Proposing results and conclusion for decision
      making.




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA        9
 Identifying the nature of problems
     MCDA problems:
          What are the aspects, considered criteria, sub-criteria …?
          Qualitative or quantitative?
          Crisp, fuzzy, grey or rough set data?
          Does it exists threshold value of criteria?
          Maximum, minimum or anticipated value seeking for criteria?

     MOP problems:
          What are the goal, constraints and right-hand sight values?
          Qualitative or quantitative?
          Crisp, fuzzy, grey or rough set data?
          Single-objective, bi-objectives or multi-objectives problem?
          Single-level, multi-level, multi-stage programming?

IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                       10
 Determining the evaluation method

    MCDA approaches:
         AHP;
         SAW;
         Grey Relation Analysis;
         TOPSIS;
         VIKOR;
         ELECTRE;
         PROMETHEE;
         Statistics Inferring

IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA   11
 …Determining the evaluation method

     MOP techniques:
          Goal Programming;
          DEA;
          De Novo;
          TOPSIS;
          Compromise Solution;
          εconstraints;
          Game Theory.



IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA   12
 Fuzzy C-Means Clustering

     It is a branch in multivariate analysis and an
      unsupervised learning in pattern recognition.
     Hard C-Mean Clustering (HCM, K-Means)
     The objective function of HCM can be defined as
      following:

                        c
       J HCM   x j  zi
                                                  2
                                                      where 2  C  n  1   (1)
                       i 1 jI i




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                             13
 …Fuzzy C-Means Clustering

     The objective function of Fuzzy C-Means
      clustering can be defined as following:

       J FCM    ij 
                       C      n
                                                m           2
                                                    x j  zi , 1  m               (2)
                       i 1 j 1

       where (1) 0  uij  1,                           j  1,..., n; i  1,..., C
                                  C
                       (2)  uij  1,                  j  1,..., n;
                                  i 1
                                         n
                       (3) 0   uij  n, i  1,..., C
                                         j 1
IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                      14
 …Fuzzy C-Means Clustering
       The FCM algorithm can be summarized as
        following steps:
     1). Choose an initial partition membership matrix U0;
     2). Set the stop condition as follows:
               C

              j 1
                       v jt  v jt 1                                (3)

     3). While not stop at condition (3) do
          3-1). Compute reference vectors for each part family using
                Eq.(4)

               v   i 1          
                                   * m
                                                      * m
                   *        P                      P
                   j               ij    ai        i 1   ij           (4)

IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                        15
J m (U ,V )



              …Fuzzy C-Means Clustering

                   3-2). Update the part membership matrix according to (5)
                                                                          2 ( m 1)
                                   1 ai  v              *           
                         ij 
                          *                              j
                                                                      
                                   j ' 1      
                                 C 1 ai  v j*'
                                                                    
                                                                      
                                                                                      (5)

                                            J m (U,V )
                   3-3). Evaluate Jm(U,V) using (6).
                                                                                2

                          J m (U ,V )    ij 
                                              P    C
                                                              m
                                                                  ai  v j            (6)
                                             i 1 j 1




        IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                               16
 Empirical Study

     This research is to develop and evaluate the
      proposed strategies of Fuel Cell Industry utilizing
      Fuzzy AHP with MCDM approach.

     Fuzzy C-Means Clustering to find the optimal
      combination of strategies for resources limitation.




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA        17
     Goal                              Aspects          Criteria                                                  Strategic Actions
                                                       R&D investment (C11)                                   S1. Increase R&D budget and transfer
                                       A1: R&D /       Technology enhancement (C12)                               technology from overseas
                                       Technology      R&D alliance (C13)                                     S2. Carefully select the niche products
                                                       Technology transfer (C14)                                  and its applications
                                                                                                              S3. Financial aid to new users
                                                       R&D manpower (C21)
      Fuel Cell Future Development




                                       A2: Human       Professionals incubation (C22)                         S4. Sponsor to build infrastructure and tax
                                                       Mass production professionals (C23)                        aid
                                       Resources
                                                                                                              S5. Tax incentives and other incentive
                                                                                                                  measures
                                                       Alternative products development (C31)                 S6. Low interest loan
                                       A3: Market      Reducing production cost (C32)
                                                                                                              S7. Make the product standard and
                                                       Information integration and communication (C33)            establish certified testing center
                                                       Enlarge market size (C34)                              S8. Establish national program and
                                                                                                                  enhance coordination and promotion
                                                       Explicit government policy (C41)                           mechanism
                                     A4: Government    Sufficient knowledge for educational promotion (C42)   S9. Policy or regulations for clean energy
                                          Policy       Provide tax incentives and other incentive (C43)       S10. Re-enforce education training
                                                       Provide low interest loan (C44)                              promotion and demo
                                                                                                              S11. Train marketing & planning
                                                       Improve the volume and flip chip system (C51)                professionals
                                           A5:         Make universal standard for the fuel types (C52)       S12. Efficiently manage business
                                      Infrastructure   Improve the infrastructure (C53)                             strategies
                                                       Resolve the hydrogen supply source (C54)               S13. Develop exemplified zone or system
                                                                                                                    with financial support



                                             Fig. 1 Hierarchical frame of evaluation model
IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                                                                                             18
          Table 1. Defuzzified Fuzzy Weights of Evaluated Criteria
                      Group 1                       Group 2                       Group 3                       Group 4              Aggregated Judgment
            weights             ranking   weights             ranking   weights             ranking   weights             ranking   weights       ranking
    C11      0.052                10       0.067                5        0.157                1        0.098                3       0.0935           2
    C12      0.099                1        0.118                1        0.129                2        0.099                2       0.1111           1
    C13      0.056                8        0.045                9        0.052                7        0.031                16      0.0462          9
    C14      0.061                6        0.033                15       0.062                5        0.029                17      0.0462          10
    C21      0.043                11       0.096                3        0.093                3        0.044                12      0.0692           5
    C22      0.034                14       0.055                7        0.066                4        0.033                15      0.0471           8
    C23      0.037                13       0.092                4        0.032                14       0.020                18      0.0453          11
    C31      0.069                4        0.040                12       0.038                12       0.053                9       0.0500           6
    C32      0.098                2        0.045                10       0.054                6        0.107                1       0.0759           4
    C33      0.060                7        0.013                19       0.021                16       0.036                14      0.0323          16
    C34      0.033                16       0.017                18       0.015                19       0.053                8       0.0294          19
    C41      0.092                3        0.101                2        0.051                8        0.060                5       0.0760           3
    C42      0.040                12       0.067                6        0.018                17       0.046                11      0.0427          14
    C43      0.063                5        0.039                13       0.050                 9       0.044                13      0.0489          7
    C44      0.055                9        0.034                14       0.026                15       0.010                19      0.0314          18
    C51      0.034                15       0.026                16       0.044                11       0.073                4       0.0440          12
    C52      0.023                18       0.052                8        0.016                18       0.049                10      0.0349          15
    C53      0.033                17       0.040                11       0.044                10       0.058                6       0.0440          13
    C54      0.017                19       0.020                17       0.033                13       0.057                7       0.0319          17




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                                                                                          19
              Table 2. Defuzzified Performance Score of Evaluated Data
               S1       S2       S3       S4       S5       S6       S7       S8       S9      S10      S11      S12      S13
      C11     81.549   74.237   62.632   57.234   66.000   38.607   55.534   75.429   76.680   47.001   32.786   46.499   58.776
      C12     68.761   70.294   41.742   42.475   49.842   31.475   52.496   74.435   69.505   41.860   25.739   46.811   53.701
      C13     52.527   56.783   37.480   44.236   50.863   28.288   54.358   64.663   63.839   42.391   30.292   52.551   47.379
      C14     68.656   54.352   43.220   37.138   51.819   30.985   54.226   65.618   63.818   43.347   29.838   54.007   42.641
      C21     78.166   64.435   50.124   49.470   48.103   31.301   60.028   75.186   69.633   53.184   35.781   47.501   53.956
      C22     69.975   59.763   39.756   40.562   47.656   28.099   56.177   73.713   64.402   66.725   50.090   50.441   52.428
      C23     64.190   61.416   48.469   45.201   50.964   33.951   54.365   58.701   64.039   53.200   36.944   56.550   55.829
      C31     68.402   67.244   64.609   58.827   57.897   39.810   49.308   60.378   69.104   48.371   36.459   52.415   63.880
      C32     68.616   66.475   69.157   63.836   66.692   44.495   47.629   53.935   71.344   41.665   35.842   53.493   59.573
      C33     60.216   50.532   46.972   43.984   44.010   36.440   46.291   65.127   56.665   54.128   36.718   44.620   51.399
      C34     51.013   56.861   61.581   62.048   58.247   45.646   39.971   48.116   64.158   44.006   37.410   48.726   55.416
      C41     78.563   64.684   64.221   63.403   57.550   40.547   58.508   79.249   78.463   58.260   34.247   52.925   69.614
      C42     59.086   55.732   60.049   59.957   54.081   40.218   45.138   65.679   73.453   80.916   52.363   52.660   71.118
      C43     49.897   46.710   54.694   63.271   71.404   52.918   38.643   39.631   59.920   38.080   33.010   41.710   50.796
      C44     46.249   44.077   45.417   44.368   42.986   60.640   34.700   36.105   53.038   30.815   27.929   36.215   43.218
      C51     76.025   65.734   38.000   42.780   38.968   27.420   45.662   68.095   59.375   37.176   23.520   41.096   45.387
      C52     64.360   65.029   43.156   50.610   41.786   27.522   60.445   65.375   71.624   50.051   22.721   45.063   53.880
      C53     63.438   61.857   44.230   66.952   49.889   37.825   57.294   59.147   71.455   39.933   29.237   46.102   58.950
     C54      56.500   58.306   42.765   59.318   42.992   30.237   45.424   51.767   59.256   34.298   24.157   40.795   46.038
     Mean     64.536   60.238   50.435   52.404   52.197   37.180   50.326   62.124   66.304   47.653   33.425   47.904   54.420
    Ranking     2        4        8        6        7       12        9        3        1       11       13       10        5



IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                                                                    20
        Table 3. Defuzzified Synthetic Values of Development Strategies
               S1       S2       S3       S4       S5       S6       S7       S8       S9      S10      S11      S12      S13
     C11      9.302    8.854    8.234    6.865    8.036    4.954    7.331    9.093    8.853    6.398    4.775    6.186    7.250
     C12      9.423    9.908    6.639    6.598    7.333    4.902    7.787    10.322   9.840    6.587    4.062    6.983    7.628
     C13      3.282    3.594    2.348    2.807    3.243    1.922    3.339     3.753   3.987    2.720    1.951    3.222    2.913
     C14      3.988    3.340    2.869    2.580    3.079    1.983    3.316     3.872   3.843    2.718    1.919    3.377    2.754
     C21      6.750    5.568    4.831    4.665    4.591    3.049    5.597     6.612   6.236    5.114    3.559    4.513    4.833
     C22      4.141    3.715    2.548    2.723    3.157    1.909    3.755    4.306    4.008    3.942    3.099    3.263    3.181
     C23      3.906    3.811    3.069    2.823    3.040    2.192    3.654    3.650    4.005    3.310    2.493    3.638    3.368
     C31      4.452    4.269    4.339    4.006    3.745    2.812    3.676    4.123    4.512    3.390    2.585    3.481    3.917
     C32      6.505    6.639    7.022    6.586    6.523    4.725    4.982    5.628    7.213    4.647    3.969    5.261    6.092
     C33      2.514    2.280    2.132    2.068    2.073    1.813    2.102    2.783    2.569    2.467    1.807    2.064    2.189
     C34      2.224    2.377    2.483    2.550    2.404    1.948    1.757    2.193    2.647    1.847    1.665    1.995    2.231
     C41      7.429    6.554    6.456    6.330    5.764    4.092    6.222    7.371    7.544    5.892    3.729    5.558    6.900
     C42      3.442    3.079    3.350    3.603    3.076    2.544    2.848    3.716    4.212    4.256    3.079    3.146    3.910
     C43      3.479    3.162    3.674    4.149    4.371    3.656    2.820    2.854    3.859    2.751    2.316    2.907    3.201
     C44      2.032    2.024    2.020    1.970    1.855    2.739    1.722    1.653    2.218    1.379    1.239    1.659    1.940
     C51      4.270    3.730    2.413    2.554    2.448    1.787    2.897    4.045    3.535    2.298    1.455    2.626    2.778
     C52      2.954    2.911    2.054    2.417    2.051    1.395    2.865    3.100    3.279    2.450    1.111    2.191    2.396
     C53      3.582    3.773    2.815    3.933    3.201    2.352    3.690    3.790    4.064    2.474    1.806    2.903    3.351
      C54      2.449    2.548    2.011    2.466    1.847    1.410    2.151    2.357    2.640    1.650    1.049    1.839    2.058
     Grand    86.122   82.132   71.307   71.692   71.837   52.185   72.513   85.221   89.066   66.292   47.666   66.809   72.888
    Ranking      2        4        9        8        7        12       6        3        1        11       13       10       5



IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                                                                    21
     From Table 3, we can conclude the preferred
      order of proposed development strategies of fuel
      cell industry as follows:
        S9 S1 S8 S 2 S13 S7 S5 S 4 S3 S12 S10 S6 S11




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA      22
      The first five development strategies to be
       implemented suggested by experts who
       participated this research are
          1. Policy or Regulations for Clean Energy;
          2. Increase R&D Budget and Transfer Technology
             from Overseas;
          3. Establish National Program and enhance
             coordination and promotion mechanism;
          4. Carefully Select the Niche Products and its
             applications; and
          5. Develop Demonstrated Zone or System with
             Financial Support.

IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA            23
  Table 4. Grade of Membership of Strategies for Fuel Cell Industry (three-cluster case)

                S1        S2        S3        S4        S5        S6        S7        S8        S9        S10       S11       S12      S13
  Cluster 1    0.0039    0.0123    0.0753    0.0564    0.0373    0.9249    0.0815    0.0126    0.0131    0.1615    0.9465    0.0470   0.0271
  Cluster 2    0.9769    0.9183    0.0635    0.0683    0.0927    0.0164    0.2332    0.9371    0.9314    0.0900    0.0142    0.0512   0.0928
  Cluster 3    0.0192    0.0694    0.8612    0.8753    0.8700    0.0587    0.6852    0.0503    0.0555    0.7485    0.0393    0.9018   0.8801




  Table 5. Grade of Membership of Strategies for Fuel Cell Industry (four-cluster case)

                  S1        S2        S3        S4       S5        S6        S7        S8        S9        S10       S11      S12      S13
   Cluster 1    0.0039    0.0125    0.0368    0.0276    0.0229    0.8573    0.0340    0.0113    0.0113    0.0831   0.9237    0.0079   0.0168
   Cluster 2    0.9586    0.8496    0.0317    0.0339    0.0572    0.0176    0.0964    0.8980    0.8950    0.0475    0.0114   0.0087   0.0572
   Cluster 3    0.0190    0.0718    0.7607    0.7699    0.7001    0.0602    0.1870    0.0438    0.0511    0.2639   0.0291    0.0656   0.7289
   Cluster 4    0.0185    0.0661    0.1709    0.1686    0.2199    0.0649    0.6827    0.0469    0.0426    0.6055   0.0358    0.9178   0.1972


IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                                                                                              24
     Table 4 shows the three-cluster combination:
         The first cluster includes two strategic combinations,
          that is, Low interest loan (S6) and Train marketing &
          planning professionals (S11).
         The second cluster includes four strategic
          combinations, that is, Increase R&D budget and
          transfer technology from overseas (S1), Carefully
          select the niche products and its applications (S2),
          Establish national program and enhance coordination
          and promotion mechanism (S8), and Policy or
          regulations for clean energy (S9).


IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                    25
         The third cluster includes seven strategic
          combinations, that is, Financial aid to new users (S3),
          Sponsor to build infrastructure and tax aid (S4), Tax
          incentives and other incentive measures (S5), Make
          the product standard and establish certified testing
          center (S7), Re-enforce education training promotion
          and demo (S10), Efficiently manage business
          strategies (S12), and Develop exemplified zone or
          system with financial support (S13).




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                 26
     Table 5 presents the strategy combination of
      four-cluster cases;
         The strategic combinations of the first two clusters
          have the same contents. It only split the third cluster to
          two clusters;
         The third cluster includes seven strategic
          combinations, that is, Financial aid to new users (S3),
          Sponsor to build infrastructure and tax aid (S4), Tax
          incentives and other incentive measures (S5), and
          Develop exemplified zone or system with financial
          support (S13).

IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                   27
         The fourth new cluster includes three strategic
          combinations, that is, Make the product standard and
          establish certified testing center (S7), Re-enforce
          education training promotion and demo (S10), and
          Efficiently manage business strategies (S12).




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                  28
     We further explore the results of different fuzzy
      classification with strategic preferred order
      based on individual synthetic value, we can
      easily find that,
         Both strategies in the first cluster (S6 and S11) have
          the lowest preference.
         All of the strategies in the second cluster (S1, S2, S8
          and S9) have the higher synthetic values.
         In addition, the strategies in third or fourth cluster have
          the medium synthetic values.


IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                    29
 Conclusions

     Facing the dynamic change of global business
      environment, although the evaluators give
      different weights on criteria, from group decision
      evidence, the first five important criteria are
      technology enhancement (0.1111), R&D
      investment (0.0935), explicit government policy
      (0.0760), reducing production cost (0.0759), and
      R&D manpower (0.0692).



IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA        30
 …Conclusions

     It indicates that technology enhancement and
      R&D investment will definitely influence the
      fulfillment in such emerging industry. For the
      role of government, how to make an explicit
      policy is very important especially in such
      emerging technology and industry. In addition,
      most of the participated experts agree that how
      to reduce production cost and how to recruit and
      manage the R&D manpower also are the critical
      factors for getting into such new applied field.
IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA      31
 …Conclusions

     This research has successfully demonstrated
      the appropriateness of fuzzy c-means clustering
      for solving the optimal strategic combinations for
      fuel cell industry in Taiwan. The widely used
      technique, AHP, fuzzy c-means clustering can
      provide some useful information for coping with
      real MCDA problems.




IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA        32
                              THE END

          Thanks for your patience
          and attention to listening.

                   E-mail: ghtzeng@cc.nctu.edu.tw (G.H. Tzeng)
                          hkchiou@ebtnet.net (H.K. Chiou)

IFORS. July 11 - 15, 2005. Honolulu Hawaii, USA                  33

								
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