Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Fig 1.

Algorithm 1.

General framework of MOEAs for LRPLCCC.

More »

Fig 1 Expand

Fig 2.

A simple solution representation.

More »

Fig 2 Expand

Fig 3.

Schematic diagrams of 2-opt, swap, and insert moves.

More »

Fig 3 Expand

Table 1.

Parameters in the proposed model.

More »

Table 1 Expand

Table 2.

Vehicle-specific parameters.

More »

Table 2 Expand

Table 3.

Scores of 77 pairs evaluated by HV, IGD, and GD values.

More »

Table 3 Expand

Fig 4.

Effects of pm values on the performance of algorithm.

More »

Fig 4 Expand

Table 4.

Effects of strategies on the performance indicators.

More »

Table 4 Expand

Fig 5.

Pareto fronts of instances using S1 and S2.

More »

Fig 5 Expand

Table 5.

Performance indicators of three models.

More »

Table 5 Expand

Fig 6.

Pareto fronts obtained by three models.

More »

Fig 6 Expand

Table 6.

Performance indicators of seven variants of depot capacity.

More »

Table 6 Expand

Fig 7.

Effects of depot capacity on the performance indicators (only HV, IGD, and RNI).

More »

Fig 7 Expand

Fig 8.

Pareto fronts of seven variants of depot capacity.

More »

Fig 8 Expand

Table 7.

Performance indicators of seven variants of depot capacity.

More »

Table 7 Expand

Fig 9.

Effects of hard time windows on the performance indicators (only HV, IGD, and RNI).

More »

Fig 9 Expand

Fig 10.

Pareto fronts of seven variants of hard time windows of clients.

More »

Fig 10 Expand

Table 8.

Performance indicators of four variants of fleet composition.

More »

Table 8 Expand

Fig 11.

Pareto fronts of four variants of fleet composition.

More »

Fig 11 Expand

Table 9.

Analysis on the number of solutions with different fleet compositions (a special RNI).

More »

Table 9 Expand