Using ant colony optimisation for improving the execution of material requirements planning for smart manufacturing. Issue 2 (1st February 2022)

• Record Type: Journal Article
• Authors: Ke, Gang; Chen, Ruey-Shun; Chen, Yeh-Cheng; Wang, Shi; Zhang, Xin
• Is Part Of: Enterprise information systems. Volume 16:Issue 2(2022)
• Page Start: 379
• Page End: 401
• Publication Date: 2022-02-01
• Subjects: Smart manufacturing -- ant colony algorithm -- material requirement planning -- approximate solution -- shortest path; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2019.1700552
• Languages: English
• ISSNs: 1751-7575

Abstract

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.