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Table 2. Computational results for E1 Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited

Table 2 Computational results for E1 Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited

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Table 1. Summary of the experimental frameworks errors are averaged fora group of 100 instances. The Tables also report the number of instances (out of 100) that have been solved to optimality (sub-columns labeled “o”). This number, for the families El, E2, and E3, was determined by a comparison to the lower bound value L,, whereas for the family E4 it was determined by a comparison to the optimum value. In Tables 2-5, the last rows report the overall average percentage errors on all instances (columns “gap”), and the total number of instances that have been solved to optimality (columns “o”). Finally, com- putational times are not reported given these were fractions of a second for all the algorithms.
Table 1. Summary of the experimental frameworks errors are averaged fora group of 100 instances. The Tables also report the number of instances (out of 100) that have been solved to optimality (sub-columns labeled “o”). This number, for the families El, E2, and E3, was determined by a comparison to the lower bound value L,, whereas for the family E4 it was determined by a comparison to the optimum value. In Tables 2-5, the last rows report the overall average percentage errors on all instances (columns “gap”), and the total number of instances that have been solved to optimality (columns “o”). Finally, com- putational times are not reported given these were fractions of a second for all the algorithms.
The results for El (Table 2) show that DJMS and LISTFIT algorithms always out- perform all other approaches in terms of the considered measures of accuracy (number of optimal solutions and average percentage er- rors). By comparing DJMS and LISTFIT, Table 2 shows that DJMS finds the optimal solution in 996 out of 1800 test problems, whereas the LISTFIT algorithm finds the optimum in 949. Moreover, the overall average percentage error of the new algorithm is practically identical to that of LISTFIT, but it is smaller than those of other algorithms. With respect to the average percentage errors, D/MS outperforms LISTFIT in 9 out of 18 experimental points, LJSTFIT outperforms DJMS in 3 points, while in the other 6 both methods perform identically. With respect to the number of optimal solu- tions, DJMS outperforms LISTFIT in 8 out of 18 experimental points, LJSTFIT outperforms DJMS in one point, while in the other 9 both methods perform identically. 24 International Journal of Operations Research and Information Systems, 6(1), 19-29, January-March 2015 considered measures of accuracy (number of
The results for El (Table 2) show that DJMS and LISTFIT algorithms always out- perform all other approaches in terms of the considered measures of accuracy (number of optimal solutions and average percentage er- rors). By comparing DJMS and LISTFIT, Table 2 shows that DJMS finds the optimal solution in 996 out of 1800 test problems, whereas the LISTFIT algorithm finds the optimum in 949. Moreover, the overall average percentage error of the new algorithm is practically identical to that of LISTFIT, but it is smaller than those of other algorithms. With respect to the average percentage errors, D/MS outperforms LISTFIT in 9 out of 18 experimental points, LJSTFIT outperforms DJMS in 3 points, while in the other 6 both methods perform identically. With respect to the number of optimal solu- tions, DJMS outperforms LISTFIT in 8 out of 18 experimental points, LJSTFIT outperforms DJMS in one point, while in the other 9 both methods perform identically. 24 International Journal of Operations Research and Information Systems, 6(1), 19-29, January-March 2015 considered measures of accuracy (number of
Table 4. Computational results for E3 International Journal of Operations Research and Information Systems, 6(1), 19-29, January-March 2015 25
Table 4. Computational results for E3 International Journal of Operations Research and Information Systems, 6(1), 19-29, January-March 2015 25
Related topics:
Computer ScienceMathematical OptimizationHopfield neural networkJob shop schedulingSimulation experimentPenalty FunctionEnergy functiondynamic Neural Network
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