Advances in Production Engineering & Management
Volume 19 | Number 3 | September 2024 | pp 315–332
https://doi.org/10.14743/apem2024.3.509
Optimization of cold chain multimodal transportation routes considering carbon emissions under hybrid uncertainties
Hou, D.N.; Liu, S.C.
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A B S T R A C T
As the third largest greenhouse gas emission industry across the globe, the transportation industry dominates a major position in the total carbon emission. Multimodal transportation has incomparable advantages over single-modal transportation, and choosing transportation routes and modes under the background of carbon peaking and carbon neutrality is crucial. The uncertainties of demand and transportation time were described using the maximum regret value and Lyapunov central limit theorem, respectively, and a robust optimization model for cold chain multimodal transportation routes considering hybrid uncertainty of carbon emissions was established. Then, a dual-pheromone ant colony algorithm was designed, and the improved niche genetic algorithm was nested into the ant colony algorithm to solve the model. Results showed that the changes in transportation time and node transfer time lead to the changes in transportation cost and transportation scheme, and the robust optimization of the multimodal transportation route under hybrid uncertainties is affected by the regret value constraint and the fluctuation range of uncertain factors, resulting in the increase in transportation cost and carbon emission cost. Therefore, the decision-makers of cold chain multimodal transportation must predict the influence of uncertain factors, choose the appropriate maximum regret value, and pay attention to the mixed time window constraints of transportation time and node transfer time to reduce costs and improve efficiency.
A R T I C L E I N F O
Keywords • Cold chain multimodal transportation; Route optimization; Hybrid uncertainties; Niche genetic algorithm; Dual-pheromone Ant Colony Algorithm; Robust optimization; Carbon emissions
Corresponding author • Hou, D.N.
Article history • Received 27 September 2024, Revised 26 October 2024, Accepted 28 October 2024
Published on-line • 31 October 2024
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