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<p>In the digital era, logistics supply chain scheduling has become the key to enhancing the competitiveness of e-commerce, and the fine management and scheduling optimization of the e-commerce logistics supply chain is particularly important. Based on the three-layer supply chain scheduling model, the study combines the production cost, transportation cost, and transportation time of each member of the supply chain with other influencing indexes. It establishes a multi-stage supply chain scheduling model (SCISM) with dual-objective. The rotational algorithm in linear programming is used to solve the model after it has been constructed. The effectiveness of the SCISM model for intelligent e-commerce logistics supply chains is explored through algorithmic and arithmetic validation of the SCISM model. The results show that the SCISM model algorithm determines the optimal solution <bold>σ</bold>∗ = (1,2,4,6,3,5) and the optimal objective generalized function value <italic>J</italic>(<bold>σ</bold>∗) = −49328 in only 0.95 seconds, which significantly improves the solution efficiency. Compared to Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO), the SCISM algorithm quickly achieves the global optimal solution with the minimum number of iterations (100).</p>
</abstract>

In the digital era, logistics supply chain scheduling has become the key to enhancing the competitiveness of e-commerce, and the fine management and scheduling optimization of the e-commerce logistics supply chain is particularly important. Based on the three-layer supply chain scheduling model, the study combines the production cost, transportation cost, and transportation time of each member of the supply chain with other influencing indexes. It establishes a multi-stage supply chain scheduling model (SCISM) with dual-objective. The rotational algorithm in linear programming is used to solve the model after it has been constructed. The effectiveness of the SCISM model for intelligent e-commerce logistics supply chains is explored through algorithmic and arithmetic validation of the SCISM model. The results show that the SCISM model algorithm determines the optimal solution σ∗ = (1,2,4,6,3,5) and the optimal objective generalized function value J(σ∗) = −49328 in only 0.95 seconds, which significantly improves the solution efficiency. Compared to Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO), the SCISM algorithm quickly achieves the global optimal solution with the minimum number of iterations (100).

Intelligent E-commerce Logistics Supply Chain Management and Scheduling Optimisation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the digital era, logistics supply chain scheduling has become the key to enhancing the competitiveness of e-commerce, and the fine management and scheduling...

Intelligent E-commerce Logistics Supply Chain Management and Scheduling Optimisation

Pubblicato online: 02 lug 2024

Pagine: -

Ricevuto: 04 mar 2024

Accettato: 28 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1519

Parole chiave
Rotational algorithm, SCISM, Supply chain scheduling, E-commerce logistics

© 2024 Wendong Zou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Intelligent E-commerce Logistics Supply Chain Management and Scheduling Optimisation

Pubblicato online: 02 lug 2024

Pagine: -

Ricevuto: 04 mar 2024

Accettato: 28 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1519

Parole chiaveRotational algorithm, SCISM, Supply chain scheduling, E-commerce logistics

© 2024 Wendong Zou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Rotational algorithm, SCISM, Supply chain scheduling, E-commerce logistics