An Organizing Optimization Model and Its Algorithm of Container Multimodal Transportation

Tang, Jianqiao; Ci-guang, Wang

doi:10.1061/9780784412602.0008

Cited by 1 publication

(3 citation statements)

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“…To avoid the hybrid algorithm stagnating search, the pheromone on each arc must fall within the interval ( , ). The initial pheromone range of better solution available by the GA can be determined by Formulas (14) and (15), where, L(Sbest) is the better solution of the GA algorithm, taking the median of the range, ( + )/2 as .…”

Section: Dynamic Pheromone Update Strategymentioning

confidence: 99%

“…( ) = ( ) 20 (15) After the end of the GA, ( ) is determined by Formula (16), and ( ) is still determined by Formula (15). At this time, ( ) represents a better solution of the hybrid algorithm, and taking the median of its range, .…”

Section: Dynamic Pheromone Update Strategymentioning

confidence: 99%

“…At this time, the next node can be selected from the option in a random manner. This randomness can change the original search trajectory of the algorithm, effectively prevent the pheromone accumulating in the local optimal solution, thereby improving the efficiency of ants for searching the best solution [15]. The ant a at node i randomly selects the next node according to the Formula (22):…”

Section: Local Optimum Solutionmentioning

confidence: 99%

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Design of Multimodal Transport Path Optimization Model and Dual Pheromone Hybrid Algorithm

Lu¹,

Xiao²,

Zhang³

et al. 2019

JESA

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This paper aims to optimize the largescale complex multimodal transport path in an efficient and accurate manner. To this end, the genetic algorithm (GA) was embedded into the ant colony optimization (ACO), creating a dual pheromone hybrid algorithm. The GA guides and speeds up the ACO to avoid the local optimum trap and enhance the optimization ability.Considering the scale effect of transport cost, a combinatorial optimization model was established to minimize the cost under the constraints of delivery time and path capacity, and tested in largescale multimodal transport networks. The test results show that the hybrid algorithm converged to the optimal solution through 46.17% and 45.25% fewer iterations, and lowered the transport cost by 4.23% and 2.39% than the GA and ACO, respectively. In addition, the hybrid algorithm was relied on to explore the factors affecting path selection. The research findings lay a solid basis for decision-making on multimodal transport.

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Section: Dynamic Pheromone Update Strategymentioning

confidence: 99%

Section: Dynamic Pheromone Update Strategymentioning

confidence: 99%

Section: Local Optimum Solutionmentioning

confidence: 99%

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