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| Author | LFVRP | LFVRPTW | HLFRPTW | ELFRPTW | Tabu search | Objective value | Min cost | Sequential insertion | Cost-sharingmethod | Threshold method | Linkage method | Split method | Results | Imp. rate (%) | CPU (GHz) | Ref |
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| Chen et al. | | | | | | | | | Initial solve: 16769 The best solve: 15521 | Initial solve: 16849 The best solve: 15337 | | | (1) Number of small vehicles: LFVRP-VD performs best
(2) Objective values: the VRP approach, followed by the LFVRP-VD performs best
(3) Local search improvement: the LFVRP-VD performs best, followed by the VRP | | | [5] |
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| Chen et al. | | ✓ | | | | LFVRPTW: 22970
VRPTW: 25101 | | | | | | | (1) Number of small vehicles: LFVRPTW are better than those for the VRPTW|
(2) Objective values: LFVRPTW are better than those for the VRPTW | 12.55
2.60 | 2.53 | [8] |
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| Chen and Wang | | | | ✓ | | LFVRPTW: 18719
ELFRPTW: 18300 | | | | | | | (1) Number of small vehicles: ELFVRPTW is advantageous over the LFVRPTW
(2) Objective values: ELFVRPTW is advantageous over the LFVRPTW | 7.8
6.0 | 2.53 | [13] |
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| Chen | | ✓ | | | | | 4type I: 23771.7 8type I: 23370.9 | 4type I: 23080.5 8type I: 22561.32 | | | | | (1) LFVRPTW usually yields better results than the VRPTW
(2) LFVRPTW is beneficial compared with VRPTW
(3) Less restrained time window constraints can yield vast gain to the LFVRPTW | −1.72
−2.3 | 2.53 | [14] |
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| Brandstatter and Reimann | | | | | | | | | | | Cost average: 10778 | Cost average: 11375 | (1) Number of small vehicles: LFVRP is advantageous over the HFVRP
(2) They investigated the impact of random aspects of the algorithms by repeatedly running the approaches 10, 100, and 1000 times
(3) The linkage approach outperforms the break split approach when the depot is much less far off, or SV capacity is big, we discover that the split approach benefits greater strongly from its larger synchronization potential while the depot is further afield or small | Linkage:40.3
Split: 32.8 | 3.1 | [11] |
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| Brandstatter and Reimann | | | | | | | | | | | Average solomon: 1822
Average chen: 2244
Average total: 2054 | Average solomon: 1915
Average chen: 2362
Average total: 2161 | They propose and justify several improvements to original algorithms including metaheuristic strategy (MS), metaheuristic algorithm (MA), multiple solutions strategy (MS), and local search strategy (LS)
Best found results from both approaches:
Average solomon: 1637
Average chen: 2015
Average total: 1845 | 9 | 3.1 | [24] |
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| Brandstatter | | ✓ | ✓ | | | | | | | | | | They able to handle time windows by four improvement strategies including metaheuristic strategy (MS), metaheuristic algorithm (MA), enerating multiple solutions (MS), and local search (LS). | | 3.1 | [12] |
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