An enhanced genetic-based multi-objective mathematical model for industrial supply chain network

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.