# Multi-objective production scheduling optimization strategy based on fuzzy mathematics theory

## TL;DR

This paper introduces a new fuzzy mathematics-based strategy to improve production scheduling by addressing conflicts and uncertainties more effectively.

## Contribution

The paper proposes novel kernel allocation methods and demonstrates improved scheduling efficiency using fuzzy mathematics theory.

## Key findings

- Fuzzy mathematics theory reaches equilibrium at 22 generations with a satisfaction degree of 2.345.
- A hybrid algorithm achieves equilibrium in the third generation with a higher satisfaction degree of 2.445.
- The competitive strategy significantly accelerates evolutionary equilibrium and improves customer satisfaction.

## Abstract

Multi-objective production scheduling faces the problems of inter-objective conflicts, many uncertainty factors and the difficulty of traditional optimization algorithms to deal with complexity and ambiguity, and there is an urgent need to introduce the theory of fuzzy mathematics in order to improve the scheduling efficiency and optimization effect. Aiming at the shortcomings of existing kernel allocation methods, the proportional gain, weighted marginal, and average cost-saving allocation methods are innovatively proposed, all proven to be effective kernel allocation strategies. This paper analyzes the existing conditions of fuzzy mathematical scheduling solutions and probes into their relationship with fuzzy mathematical kernel allocation. It compares the similarities and differences between fuzzy mathematical scheduling solutions and other scheduling solutions. The experimental results show that the fuzzy mathematics theory reaches equilibrium when it evolves to 22 generations, and the maximum satisfaction degree is 2.345. The hybrid algorithm achieves equilibrium in the third generation, increasing the maximum satisfaction to 2.445. This shows that competitive strategy improves customer satisfaction and significantly accelerates the achievement of evolutionary equilibrium.