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

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Mehrsai, A (Author) Other; figueira, g (Author) FEUP; Santos, N (Author) Other; Pedro Amorim (Author) FEUP; Bernardo Almada Lobo (Author) FEUP

Conference proceedings International

Title: IFIP Advances in Information and Communication Technology

Pages: 467-474

IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2017

3 September 2017 through 7 September 2017

Indexing

Scopus - 5 Citations

Other information

Authenticus ID: P-00N-0ZG

DOI: 10.1007/978-3-319-66923-6_55

Abstract (EN): 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. © IFIP International Federation for Information Processing 2017.

Language: English

Type (Professor's evaluation): Scientific