By d12 = 4 + = 64/21, the distance needed is obtained.The pseudo codes of GSOS as shown in Pseudocode 1.4. Simulations and ResultsTo verify the feasibility of GSOS, we have tested Tipifarnib price two simulations of different scales.Case 1 ��According to [9], there are 41 workpieces of 10 orders, detailed information are shown in Tables Tables33 and and4;4; target of optimization is maximizing the number of weighted whole-set orders.Table 3Orders and weights.Table 4Information of orders.In Table 4, numbers in row-1 account for deadlines, in line-1 account for orders and in brackets for processing time. According to the above information, we contrast the results between GSOS and GA.

Simulation environment: Microsoft Windows-XP system, AMD-A6-3400M CPU, 2G-RAM, and the codes are programmed by MATLAB2012a, the population size is fixed n = 400, maximum iteration Tmax = 80, and the program run 10 times independently.The efficiency of results is shown in Table 5. From Table 5, we can find that average solving time of GSOS is shortened by about 34.74 seconds compared with GA, which increases by about 29.3%.Table 5Comparison of time consumption.The results of optimization numbers are shown in Table 6. Form Table 6, we realize that GSOS performs better than GA in terms of average value, minimum value, and variance.Table 6Results of numbers.Case 2 ��To insure the performance of GSOS, Example 2 is generated randomly, information is in detail in Tables Tables77 and and88.Table 7Orders and weights.Table 8(a) Information of orders for Job 1�C10.

(b) Information of orders for Job 11�C21.The results of optimization numbers are shown in Table 9, and the efficiency of results are shown in Table 10. Table 9Results of numbers.Table 10Comparison of time consumption.From Tables Tables99 and and10,10, we can find that the proposed algorithm is better than GA.The searching curves of GSOS and GA are shown in Figures Figures22 and and3,3, there solid lines account for GSOS while dotted lines for GA. Figure 2Curves in case 1.Figure 3 Curves in case 2.According to the Figures Figures22 and and3,3, GSOS and GA both have a high rate of convergence, but GSOS performs better than GA in terms of average value, minimum value, and variance, which proved its high-accuracy of solutions, the solving time of GSOS decreases by about 29% compared with GA which reflects its efficiency.

In conclusion, GSOS is more suitable for solving whole-set orders problem.5. ConclusionsAn improved glowworm swarm optimization for scheduling (GSOS) is proposed in this paper; we have verified its high rate of convergence, efficiency, accuracy, and easy operation through simulations on different scales of whole-set orders problem. To test its performance on parallel machines and Anacetrapib bigger scales that will be our research direction later on.