An improved salp swarm algorithm for permutation flow shop vehicle routing problem
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Summary
This summary is machine-generated.This study introduces an improved salp swarm algorithm to solve the permutation flow shop vehicle routing problem. The algorithm effectively minimizes production and transportation costs through collaborative optimization, outperforming existing methods.
Area Of Science
- Operations Research
- Manufacturing Engineering
- Logistics Management
Background
- Discrete manufacturing relies on permutation flow shop scheduling.
- Timely delivery and reduced inventory costs are critical business needs.
- Integrating production scheduling with logistics is essential for cost minimization.
Purpose Of The Study
- To address the permutation flow shop vehicle routing problem.
- To develop a collaborative optimization model for production and logistics scheduling.
- To propose an efficient algorithm for solving this integrated problem.
Main Methods
- Mathematical modeling of the permutation flow shop vehicle routing problem.
- Development of an improved salp swarm algorithm (SSA).
- Incorporation of local search operations within the SSA to enhance population exploration.
Main Results
- The improved SSA demonstrated superior optimization capabilities compared to simulated annealing, genetic algorithms, and particle swarm optimization.
- Simulation results validated the algorithm's effectiveness.
- The proposed algorithm successfully solved the permutation flow shop vehicle routing problem in example applications.
Conclusions
- The developed mathematical model and improved SSA provide an effective approach for collaborative optimization of production and logistics scheduling.
- The algorithm's enhanced exploration capability leads to better optimization performance.
- This research offers a practical solution for enterprises seeking to minimize total production and transportation costs.
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