Single Machine Scheduling Problem with Sequence Dependent Family Setup Times (SMSP-SDFST)

• Papers:

  We have used these instances in the following papers:
  
  • Rego, M.F. and Souza, M.J.F. and Arroyo, J.E.C. Multi-objective algorithms for the single machine scheduling problems with setup time dependent on the sequence and the job family. XXXVIII Conferencia Latinoamericana En Informatica (CLEI 2012), 1-8.
  • Rego, M.F. and Souza, M.J.F. and Machado, I. and Arroyo, J.E.C. Multi-objective Algorithms for the Single Machine Scheduling Problem with Sequence-dependent Family Setups. 17th Online World Conference on Soft Computing in Industrial Applications (2012).

• Instances:

  The instances were generated in a random way and with uniform distribution. This compressed file contains the instances:
  Instances
  
  The instance files are simple tab-delimited text files. The names of these files are in the format N-F-H-C.txt, where:
  
  • N: is the number of jobs, N ∈ {60, 80, 100}.
  • F: is the number of families, F ∈ {2, 3, 4, 5}.
    
  • H: is a parameter to define the due date, which was generated in the interval [0, H.∑p_j], with H ∈ {0.5 ; 1.5 ; 2.5 ; 3.5}.
  • C: is used setup time class:
  • Class S: [10, 20]
  • Class M: [51, 100]
  • Class L: [101, 200]
  The class S setup time is relatively smaller than the average processing time. The class M setup time is close to the average processing time while the class L setup time is relatively bigger than the average processing time.
  By combining the parameters of the number of jobs, the number of families, the number of intervals to the due dates and the number of classes of intervals to the setup time, an amount of 144 different instances were generated to the tests completion. Note that, to each n, 48 instances were generated.



• Results:

  This file contains the results:
  Results
  
  This file contains the result for each metric in a different spread sheet, where:
  
  • CardinalAverage: this plan provide results for the average of the cardinal metric.
  • AverageDistanceAverage: this plan provide results for the average of the average distance metric.
  • MaximumDistanceAverage: this plan provide results for the average of the maximum distance metric.
  • HypervolumeAverage: this plan provide results for the average of the hypervolume metric.
  • EpsilonAverage: this plan provide results for the average of the epsilon metric.
  • CardinalBest this plan provide results for the best of the cardinal metric.
  • AverageDistanceBest: this plan provide results for the average of the best distance metric.
  • MaximumDistanceBest: this plan provide results for the best of the maximum distance metric.
  • HypervolumeBest: this plan provide results for the best of the hypervolume metric.
  • EpsilonBest: this plan provide results for the best of the epsilon metric.
  All algorithms were coded on C++ language and the tests were done in a computer with Intel^® Core™ 2 Quad processor with 2.4 GHz of clock frequency and 6 GB RAM memory.
  The stopping criterion to each algorithm is the CPU time and it is related to the size of the instance. In the literature, it is common the usage of this criterion in optimization algorithms. It has been established 1,000 . n milliseconds as time limit of execution of each algorithm, in which n is the number of jobs of the instance. To each instance were made 30 executions, with 30 different seeds generated randomly.