Benchmark Problems and Solutions

Toma, Shouji; Chen, W. F.

doi:10.1201/9781351069519-7

Cited by 1 publication

(5 citation statements)

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“…In the simulation with the Solomon benchmark data, the paper firstly uses the total route distances as the objective function to calculate the fitness of NR-EGA, and makes the simulation comparison between the NR-EGA and the algorithm [31] with the results shown in Table 4. The overall travel distances and the vehicle capacities in NR-EGA are less than those in the algorithm [31]. The number of used vehicles is also fewer.…”

Section: Discussionmentioning

confidence: 99%

“…In RC201 and RC205, when the customer number enhances from 50 to 100, the load capacity limitation also enhances. While, the limitations are still less than those in the algorithm [31].…”

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 98%

“…For discussing the routing performance of the NR-EGA, the paper makes the simulation comparisons with Solomon benchmark VRPTW data [30][31][32].…”

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 99%

“…After 500 optimizing iterations, the NR-EGA finds the optimal route. The compared performances between the NR-EGA and the algorithm [31] are shown in Table 4. In the table, the overall travel distances and the vehicle capacities in NR-EGA are less than those in the algorithm [31].…”

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 99%

“…The compared performances between the NR-EGA and the algorithm [31] are shown in Table 4. In the table, the overall travel distances and the vehicle capacities in NR-EGA are less than those in the algorithm [31]. The number of used vehicles is also smaller.…”

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 99%

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The NR-EGA for the EVRP Problem with the Electric Energy Consumption Model

Zhu

Lee

2022

Energies

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Nowadays, in researches on electric vehicle routing problems, in order to improve the delivery efficiency and reduce the routing cost, many important elements are broad discussed such as the customer time window, the routing algorithm, the electric energy consumption, etc. In these, the routing algorithm is the key element to achieve a good solution. Based on this background, the paper investigates the routing algorithm, then adopts the elitist genetic algorithm and proposes an improved neighbor routing initialization method for solving the electric vehicle routing problem. In our method, the electric vehicle energy consumption is used as the main component of the routing system. The neighbor routing initialization enables the routing system to choose the close route from a suitable first customer in the initialization, which makes the routing search faster and find the global optimal route easily. The simulations on the Solomon benchmark data and the Hiland Dairy milk delivery example in Dallas, Texas, USA verifies the good performance of the method.

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Section: Discussionmentioning

confidence: 99%

“…In RC201 and RC205, when the customer number enhances from 50 to 100, the load capacity limitation also enhances. While, the limitations are still less than those in the algorithm [31].…”

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 98%

“…For discussing the routing performance of the NR-EGA, the paper makes the simulation comparisons with Solomon benchmark VRPTW data [30][31][32].…”

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 99%

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 99%

Section: Simulation Analysis Using Solomon Benchmark Datamentioning

confidence: 99%

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