An enhanced genetic-based multi-objective mathematical model for industrial supply chain network
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an improved genetic algorithm to optimize industrial supply chains, significantly cutting costs, operational time, and improving resource scheduling efficiency for better network balance.
Area Of Science
- Operations Research
- Industrial Engineering
- Computer Science (Soft Computing)
Background
- Industrial supply chains require comprehensive cost analysis, product delivery coordination, and enhanced network efficiency.
- Existing methodologies often overlook the specific complexities of industrial supply chain networks.
Purpose Of The Study
- To develop a novel model for multi-objective industrial supply chain problems.
- To enhance efficiency and balance within emerging industrial supply chain networks.
Main Methods
- A meta-heuristic approach using an improved genetic algorithm (GA).
- A hybrid method combining topology theory and random search for initial population generation.
- Enhanced crossover and mutation operations with probabilities determined by elite selection and roulette methods.
Main Results
- Reduced supply load from 0.678 to 0.535.
- Decreased labor costs from 1832 to 1790 yuan.
- Lowered operational time by 39.5% (from 48 to 29.5 seconds).
- Significantly reduced variation in node utilization rates (from 30.1% to 12.25%).
Conclusions
- The improved genetic algorithm effectively addresses multi-objective industrial supply chain challenges.
- Enhanced resource scheduling efficiency and overall supply chain balance were achieved.
- The developed model offers a robust solution for optimizing complex industrial networks.
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