Using ant colony optimisation for improving the execution of material requirements planning for smart manufacturing

ABSTRACT

In this paper, ant colony optimization algorithm is used, and then the records in the supply and demand documents in the material requirement planning (MRP) are used to simulate the city points that the salesperson moves, so that the artificial ants can move between cities. To find the shortest path through all cities, that is, to find the shortest path of MRP in the main file of supply and demand, to reduce the system execution time, improve the efficiency of related personnel. Experimental results show that compared with other algorithms, ACO algorithm can effectively shorten the deployment time of MRP and greatly improve the implementation efficiency.

KEYWORDS:

• Smart manufacturing
• ant colony algorithm
• material requirement planning
• approximate solution
• shortest path

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Guangdong University Youth Innovative Talents Program of China (2017GkQNCX116); Technical Skills Expert Project of DongGuan Polytechnic (2019JY05); Guangdong University Student Science and Technology Innovation and Cultivation Fund (phdj2019b0900).