galog.txt

一个生产工序安排的算法（采用遗传算法

 Comment:  
   Machine: #0 to #3
   Works  : #0 to #7

 Star Simulation...

gener-    best       average   standard
tion      value      fitness   deviation
number
-----:  ---------,-----------,-----------;
    1:     10.000,     19.400,      4.380;
    2:     10.000,     18.840,      4.586;
    3:     10.000,     16.900,      3.824;
    4:     10.000,     17.980,      4.533;
    5:     10.000,     17.280,      4.199;
    6:     10.000,     17.780,      4.344;
    7:     10.000,     17.600,      3.580;
    8:      8.000,     17.940,      4.118;
    9:      8.000,     17.120,      3.751;
   10:      8.000,     15.380,      4.090;
   11:      8.000,     18.040,      4.544;
   12:      8.000,     18.040,      4.290;
   13:      8.000,     16.780,      4.306;
   14:      8.000,     16.820,      3.312;
   15:      8.000,     16.020,      3.904;
   16:      8.000,     15.740,      3.385;
   17:      8.000,     16.080,      4.589;
   18:      7.000,     16.560,      4.450;
   19:      7.000,     16.680,      5.192;
   20:      7.000,     17.160,      4.487;
   21:      7.000,     18.460,      4.648;
   22:      7.000,     17.160,      5.196;
   23:      7.000,     17.140,      3.747;
   24:      7.000,     15.720,      3.631;
   25:      7.000,     17.220,      3.996;
   26:      7.000,     17.000,      4.426;
   27:      7.000,     16.880,      4.336;
   28:      7.000,     17.340,      3.978;
   29:      7.000,     16.740,      3.779;
   30:      7.000,     16.060,      3.513;
   31:      7.000,     17.300,      4.778;
   32:      7.000,     15.960,      4.333;
   33:      7.000,     16.620,      4.252;
   34:      7.000,     17.700,      4.946;
   35:      7.000,     17.080,      3.434;
   36:      7.000,     18.400,      4.823;
   37:      7.000,     17.880,      3.842;
   38:      7.000,     16.100,      4.929;
   39:      7.000,     16.300,      3.965;
   40:      7.000,     17.580,      4.376;
   41:      7.000,     16.480,      4.166;
   42:      7.000,     18.900,      4.778;
   43:      7.000,     17.720,      5.849;
   44:      7.000,     16.960,      3.422;
   45:      7.000,     16.500,      3.991;
   46:      7.000,     17.240,      3.836;
   47:      7.000,     18.120,      4.312;
   48:      7.000,     15.840,      4.569;
   49:      7.000,     15.360,      4.289;
   50:      7.000,     16.860,      4.436;
   51:      7.000,     16.940,      4.644;
   52:      7.000,     16.560,      3.764;
   53:      7.000,     16.380,      4.242;
   54:      7.000,     18.000,      3.411;
   55:      7.000,     17.560,      4.011;
   56:      7.000,     17.440,      4.803;
   57:      7.000,     16.280,      3.270;
   58:      7.000,     17.080,      4.110;
   59:      7.000,     16.720,      4.616;
   60:      7.000,     16.980,      6.548;

 Simulation completed

 ==========================================
 Result report: 

 Best member: 

  Works would be arranged on which machine: 

    * Works#0 is done on Machine#1, consuming time = 2.000000s
    * Works#1 is done on Machine#2, consuming time = 3.000000s
    * Works#2 is done on Machine#0, consuming time = 4.000000s
    * Works#3 is done on Machine#2, consuming time = 4.000000s
    * Works#4 is done on Machine#3, consuming time = 3.000000s
    * Works#5 is done on Machine#0, consuming time = 3.000000s
    * Works#6 is done on Machine#1, consuming time = 4.000000s
    * Works#7 is done on Machine#3, consuming time = 3.000000s


  Works on Each Machine: 

    * Works on Machine#0: #2(4.000000s),  #5(3.000000s),   Total Time consumed = (7.000000s)
    * Works on Machine#1: #0(2.000000s),  #6(4.000000s),   Total Time consumed = (6.000000s)
    * Works on Machine#2: #1(3.000000s),  #3(4.000000s),   Total Time consumed = (7.000000s)
    * Works on Machine#3: #4(3.000000s),  #7(3.000000s),   Total Time consumed = (6.000000s)

 Best consuming time = 7.000000s

 End of report!