An agent-based architecture for production scheduling in dynamic job-shop manufacturing system-Reference-Cited by-同舟云学术

An agent-based architecture for production scheduling in dynamic job-shop manufacturing system

Published:2018-06-01 Issue:6 Volume:66 Page:492-502
ISSN:2196-677X
Container-title:at - Automatisierungstechnik
language:en
Short-container-title:

Author:

Shukla Om Ji¹,Soni Gunjan¹,Kumar Rajesh²,A Sujil²

Affiliation:

1. Department of Mechanical Engineering , Malaviya National Institute of Technology Jaipur , Rajasthan , India

2. Department of Electrical Engineering , Malaviya National Institute of Technology Jaipur , Rajasthan , India

Abstract

Abstract In a highly competitive environment, effective production is one of the key issues which can be addressed by efficient production planning and scheduling in the manufacturing system. This paper develops an agent-based architecture which enables integration of production planning and scheduling. In addition, this architecture will facilitate real time production scheduling as well as provide a multi-agent system (MAS) platform on which multiple agents will interact to each other. A case study of job-shop manufacturing system (JMS) has been considered in this paper for implementing the concept of MAS. The modeling of JMS has been created in SimEvents which integrates an agent-based architecture developed by Stateflow to transform into dynamic JMS. Finally, the agent-based architecture is evaluated using utilization of each machine in the shop floor with respect to time.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

https://www.degruyter.com/document/doi/10.1515/auto-2017-0119/pdf

Reference30 articles.

1. Pinedo, M., (2015). Scheduling: Theory, algorithms and systems. USA: Prentice Hall.

2. Vinod, V. and Sridharan, R., (2008). Scheduling a dynamic job shop production system with sequence-dependent setups: an experimental study. Robotics and Computer-Integrated Manufacturing, 24(3), pp. 435–449.10.1016/j.rcim.2007.05.001

3. Allahverdi, A., Ng, C., Cheng, T. C. E. and Kovalyov, M. Y., (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), pp. 985–1032.10.1016/j.ejor.2006.06.060

4. Kundakcı, N. and Kulak, O., (2016). Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Computers & Industrial Engineering, 96, pp. 31–51.10.1016/j.cie.2016.03.011

5. Lei, D. M., (2012). Minimizing makespan for scheduling stochastic job shop with random breakdown. Applied Mathematics and Computation, 218(24), pp. 11851–11858.10.1016/j.amc.2012.04.091

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