An Optimization Method of Production-Distribution in Multi-Value-Chain

Area of Science:

• Operations Research
• Supply Chain Management
• Computational Intelligence

Background:

• Value chain collaboration enhances enterprise competitiveness by reducing costs and increasing efficiency.
• Existing models combining vertical and horizontal collaboration struggle with task assignment and node constraints in production-distribution processes.
• Dynamic enterprise alliances require advanced models for multi-value-chain (MVC) collaboration.

Purpose of the Study:

• To model and optimize the MVC collaboration process within enterprise dynamic alliances.
• To develop an optimization model for the MVC collaboration network focusing on minimizing production-distribution costs.
• To address the challenges of high-dimensional decision spaces and complex constraints in multi-level production-distribution scenarios.

Main Methods:

• Constructed an MVC collaboration network optimization model with cost minimization as the objective and delivery cycle/task quantity as constraints.
• Employed a genetic algorithm to solve the high-dimensional optimization problem, adjusting gene constraints for node collaboration.
• Proposed two chromosome coding methods (staged and integrated) and an enhanced roulette genetic algorithm (ERGA) with improved elite retention.

Main Results:

• ERGA demonstrated superior performance over simple genetic algorithm (SGA) and strengthened elitist genetic algorithm (SEGA) in terms of time cost and optimization results.
• Comparative experiments and population evolution analysis confirmed ERGA's effectiveness.
• The proposed coding methods and selection operators in ERGA contribute to its enhanced capabilities.

Conclusions:

• ERGA provides a more efficient and effective solution for optimizing MVC collaboration networks compared to existing genetic algorithms.
• The developed model and ERGA are adaptable to various production-distribution environments, offering broad applicability.
• This research offers a robust framework for improving collaboration and efficiency in complex, multi-level supply chains.