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International Journal of Production Research
Article . 2017 . Peer-reviewed
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Job shop scheduling with a combination of four buffering constraints

Authors: Shi Qiang Liu; Erhan Kozan; Mahmoud Masoud; Yu Zhang 0087; Felix T. S. Chan;

Job shop scheduling with a combination of four buffering constraints

Abstract

In this paper, a new scheduling problem is investigated in order to optimise a more generalised Job Shop Scheduling system with a Combination of four Buffering constraints (i.e. no-wait, no-buffer, limited-buffer and infinite-buffer) called CBJSS. In practice, the CBJSS is significant in modelling and analysing many real-world scheduling systems in chemical, food, manufacturing, railway, health care and aviation industries. Critical problem properties are thoroughly analysed in terms of the Gantt charts. Based on these properties, an applicable mixed integer programming model is formulated and an efficient heuristic algorithm is developed. Computational experiments show that the proposed heuristic algorithm is satisfactory for solving the CBJSS in real time.

Country
Australia
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Keywords

best-insertion-heuristic algorithm, no-wait, constructive algorithm, job shop scheduling, blocking, 650, mixed integer programming, buffer management, 004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Top 10%
Green