Route Optimization for Hazardous Chemicals Transportation under Time-Varying Conditions

Zou, Zongfeng; Kang, Shuangping

doi:10.3390/su16020779

Cited by 1 publication

(1 citation statement)

References 35 publications

(59 reference statements)

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“…Wang et al [36] utilized this algorithm to develop an energy consumption model for the Flying Power Transmission Line Inspection Robot (FPTLIR), targeting its high energy use and lengthy mission durations. Additionally, under dynamic and time-varying conditions, Zou et al [37] crafted a multi-objective optimization model using NSGA-II to refine hazardous chemical transport routes, balancing risk, cost, and carbon emissions. Furthermore, to achieve the rational placement of strain sensors in a structural-health-monitoring system, Del Priore et al [38] innovated a sensor placement strategy for structural-health-monitoring systems with NSGA-II.…”

Section: Algorithm Selectionmentioning

confidence: 99%

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Gao,

Liu,

Jiang

et al. 2024

Mathematics

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To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

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Section: Algorithm Selectionmentioning

confidence: 99%