Treffer: Decentralized Vs. Centralized Sequencing in a Complex Job-Shop Scheduling

Title:
Decentralized Vs. Centralized Sequencing in a Complex Job-Shop Scheduling
Contributors:
Institute for Systems and Computer Engineering, Technology and Science Braga (INESC TEC), Hermann Lödding, Ralph Riedel, Klaus-Dieter Thoben, Gregor von Cieminski, Dimitris Kiritsis, TC 5, WG 5.7
Source:
IFIP Advances in Information and Communication Technology ; IFIP International Conference on Advances in Production Management Systems (APMS) ; https://inria.hal.science/hal-01666204 ; IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. pp.467-474, ⟨10.1007/978-3-319-66923-6_55⟩
Publisher Information:
CCSD
Springer International Publishing
Publication Year:
2017
Subject Geographic:
Document Type:
Konferenz conference object
Language:
English
DOI:
10.1007/978-3-319-66923-6_55
Rights:
http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.AB190294
Database:
BASE

Weitere Informationen

Part 7: Operations Planning, Scheduling and Control ; International audience ; Allocation of jobs to machines and subsequent sequencing each machine is known as job scheduling problem. Classically, both operations are done in a centralized and static/offline structure, considering some assumptions about the jobs and machining environment. Today, with the advent of Industry 4.0, the need to incorporate real-time data in the scheduling decision process is clear and facilitated. Recently, several studies have been conducted on the collection and application of distributed data in real-time of operations, e.g., job scheduling and control. In practice, pure distribution and decentralization is not yet fully realizable because of e.g., transformation complexity and classical resistance to change. This paper studies a combination of decentralized sequencing and central optimum allocation in a lithography job-shop problem. It compares the level of applicability of two decentralized algorithms against the central scheduling. The results show better relative performance of sequencing in stochastic cases.