Experimental data - A Co-evolutionary Genetic Algorithm for the Two-machine Flow Shop Group Scheduling Problem with Job-related Blocking and Transportation Times
Description
The experimental data (File format: txt) used to support the findings of this study are included within the supplementary information file(s).
Files
Steps to reproduce
The data are divided three sections: a) Effectiveness of the strategies in CDDEA (corresponding to Section 6.2) b) The optimality test (corresponding to Section 6.3) c) Comparison with the meta-heuristics (corresponding to Section 6.4) Format of the test data: Each problem instance is titled by the following free format: a) Number of groups (Groups*) b) Number of jobs within each group (Jobs*) such as Groups4-Jobs4.txt (total 4 groups, and 4 jobs within each group) IN each instance : P=[...] represents the processing time for each job within each group on each machine (M1,M2) : such as: P=[4 12 12 14 10 12 13 14 10 ; 4 18 19 18 12 18 13 12 15 ; 4 19 12 13 17 11 19 19 20 ; 4 15 17 13 15 19 10 14 16 ]; /* each row: each group first column: number of jobs within this group second-third column : the processing time of the first job on M1 and M2,respectively fourth-fifth column : the processing time of the second job on M1 and M2,respectively ………… */ T1=[…………] represents the the transportation time from M1 to M2 for each job within each group such as: T1=[4 14 14 7 ; 1 14 9 3 ; 10 8 2 15 ; 14 4 10 4 ]; /* each row: each group first column: the transportation time of the first job from M1 to M2 second column: the transportation time of the second job from M1 to M2 ………… */ T2=[…………] represents the the return time from M2 to M1 for each job within each group Y =[…………] represents the block feature of the job within each group 0 Non-blocking job 1 blocking job The Last line is the value of the optimal solution generated by the proposed CGA