Research Article
Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design
Table 1
Combination of experimental parameters.
| PSO | Population size | 10 | 10 | 10 | 10 | 20 | 20 | 20 | 20 | Generations | 500 | 500 | 1000 | 1000 | 500 | 500 | 1000 | 1000 | Max velocity | 0.95 | 1.25 | 0.95 | 1.25 | 0.95 | 1.25 | 0.95 | 1.25 | Initial weight | 1.25 | 2.15 | 1.25 | 2.15 | 1.25 | 2.15 | 1.25 | 2.15 | , | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | Avg. fitness | 24462289 | 24461153 | 24460103 | 24458116 | 24459087 | 24460841 | 24458923 | 24457531 | Avg. execution time (sec.) | 6.731 | 6.896 | 12.468 | 12.391 | 12.842 | 12.546 | 21.391 | 21.016 | Avg. convergence time (sec.) | 4.766 | 4.275 | 8.791 | 8.437 | 7.986 | 8.311 | 17.694 | 16.972 |
| GA | Population size | 5 | 5 | 5 | 5 | 10 | 10 | 10 | 10 | Generations | 500 | 500 | 1000 | 1000 | 500 | 500 | 1000 | 1000 | Crossover rate | 0.75 | 0.8 | 0.75 | 0.8 | 0.75 | 0.8 | 0.75 | 0.8 | Mutation rate | 0.08 | 0.07 | 0.08 | 0.07 | 0.08 | 0.07 | 0.08 | 0.07 | Avg. fitness | 24465387 | 24464085 | 24462752 | 24461924 | 24463297 | 24463159 | 244603191 | 24459450 | Avg. execution time (sec.) | 19.373 | 18.859 | 67.693 | 68.047 | 47.375 | 47.734 | 225.836 | 217.512 | Avg. convergence time (sec.) | 17.716 | 16.827 | 62.549 | 62.764 | 44.507 | 44.642 | 219.675 | 211.741 |
| | Population size | 10 | 10 | 10 | 10 | 20 | 20 | 20 | 20 | Generations | 500 | 500 | 1000 | 1000 | 500 | 500 | 1000 | 1000 | Max velocity | 0.95 | 1.25 | 0.95 | 1.25 | 0.95 | 1.25 | 0.95 | 1.25 | Initial weight | 1.25 | 2.15 | 1.25 | 2.15 | 1.25 | 2.15 | 1.25 | 2.15 | , | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | Mutation rate | 0.08 | 0.07 | 0.08 | 0.07 | 0.08 | 0.07 | 0.08 | 0.07 | Avg. fitness | 24790482 | 24692938 | 24662117 | 24579829 | 24507556 | 24453060 | 24697893 | 24662084 | Avg. execution time (sec.) |
6.758 | 6.579 | 11.864 | 12.714 | 12.898 | 12.685 | 23.257 | 22.934 | Avg. convergence time (sec.) | 3.147 | 3.038 | 5.342 | 6.182 | 6.379 | 5.824 | 12.681 | 12.507 |
| GA-SA | Population size | 5 | 5 | 5 | 5 | 10 | 10 | 10 | 10 | Generations | 500 | 500 | 1000 | 1000 | 500 | 500 | 1000 | 1000 | Crossover rate | 0.75 | 0.8 | 0.75 | 0.8 | 0.75 | 0.8 | 0.75 | 0.8 | Mutation rate | 0.08 | 0.07 | 0.08 | 0.07 | 0.08 | 0.07 | 0.08 | 0.07 | Initial temperature | 300 | 300 | 400 | 400 | 300 | 300 | 400 | 400 | Markov Chain Length | 50 | 50 | 100 | 100 | 50 | 50 | 100 | 100 | Cooling rate | 0.9 | 0.9 | 0.99 | 0.99 | 0.9 | 0.9 | 0.99 | 0.99 | Final temperature | 1 | 1 | 5 | 5 | 1 | 1 | 5 | 5 | Avg. fitness | 24459076 | 24458365 | 24458351 | 24458067 | 24457247 | 24456391 | 24457425 | 24457544 | Avg. execution time (sec.) | 27.477 | 26.041 | 81.462 | 79.039 | 68.671 | 66.993 | 279.145 | 274.338 | Avg. convergence time (sec.) | 22.374 | 20.768 | 74.744 | 73.412 | 63.467 | 62.522 | 265.074 | 263.862 |
| | Population size | 10 | 10 | 10 | 10 | 20 | 20 | 20 | 20 | Generations | 500 | 500 | 1000 | 1000 | 500 | 500 | 1000 | 1000 | Max velocity | 0.95 | 1.25 | 0.95 | 1.25 | 0.95 | 1.25 | 0.95 | 1.25 | Initial weight | 1.25 | 2.15 | 1.25 | 2.15 | 1.25 | 2.15 | 1.25 | 2.15 | , | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | 2.05 | Initial temperature | 300 | 300 | 400 | 400 | 300 | 300 | 400 | 400 | Markov Chain Length | 50 | 50 | 100 | 100 | 50 | 50 | 100 | 100 | Cooling rate | 0.9 | 0.9 | 0.99 | 0.99 | 0.9 | 0.9 | 0.99 | 0.99 | Final temperature | 1 | 1 | 5 | 5 | 1 | 1 | 5 | 5 | Avg. fitness | 24457588 | 24457887 | 24455842 | 24455147 | 24455895 | 24456068 | 24456457 | 24456778 | Avg. execution time (sec.) | 14.073 | 13.422 | 26.087 | 25.971 | 26.479 | 25.706 | 38.926 | 37.433 | Avg. convergence time (sec.) | 6.102 | 5.674 | 11.479 | 10.347 | 11.887 | 12.624 | 19.211 | 16.708 |
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