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Development of scheduling methodology in a multi-machine flexible manufacturing system without tool delay employing flower pollination algorithm.

Authors :
Mareddy, Padma Lalitha
Narapureddy, Sivarami Reddy
Dwivedula, Venkata Ramamurthy
Karanam, Prahlada Rao
Source :
Engineering Applications of Artificial Intelligence. Oct2022, Vol. 115, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

This paper addresses machines, automated guided vehicles (AGVs), tool transporter (TT), and tools concurrent scheduling in a multi-machine flexible manufacturing system (FMS) for makespan minimization. The fewest number of copies of each tool type is employed to prevent tool delays, and job and tool shift times between machines are taken into account. The tools are placed in a central tool magazine (CTM), which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. This simultaneous scheduling problem is challenging to solve because it entails determining the fewest tool copies of each tool kind without tool delay, assigning AGVs and tool copies to job-operations (jb-ons), ordering jb-ons on machines, and related trip operations such as deadheading and loaded flight times for both TT and AGVs. This paper uses a mixed-integer nonlinear programming (MINLP) framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. For verification, a manufacturing company's industrial problem is employed. The results show that employing two copies each for two tool types and one copy each for the remaining tool types causes no tool delay, reduction in makespan and cost, and the FPA outperforms the Jaya algorithm. • The lowest possible copies of every tool type to avoid tool delay. • Non linear mixed integer programming model is proposed. • Flow chart for makespan and the lowest possible number of copies of every tool type. • Flower pollination algorithm for obtaining minimum makespan is implemented. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09521976
Volume :
115
Database :
Academic Search Index
Journal :
Engineering Applications of Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
159038622
Full Text :
https://doi.org/10.1016/j.engappai.2022.105275