A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr class="thead"><td align="left"> </td><td align="center">Maximum</td><td align="center">Minimum</td><td align="center">Mean</td><td align="center">Standard deviation</td><td align="center">Coefficient of variation</td></tr><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr><td align="left">HGSAA</td><td align="center">5.93</td><td align="center">3.35</td><td align="center">4.68</td><td align="center">0.80</td><td align="center">0.17</td></tr><tr><td align="left">GA</td><td align="center">7.35</td><td align="center">3.63</td><td align="center">4.93</td><td align="center">1.18</td><td align="center">0.24</td></tr><tr class="table-tr"><td colspan="6"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Statistics results of CPU time for calculation of two algorithms.

The Scientific World Journal

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Table 3

Table 3: A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment 

Table 3 | A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment 