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The robustness of scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

Wei Feng, Li Zheng, Jingshan Li

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

In this paper, a continuous time Markov chain model is introduced to study multi-product manufacturing systems with sequence-dependent setup times and finite buffers under seven scheduling policies, i.e.; cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing times), longest queue, longest processing time, and longest overall time. In manufacturing environments, optimal solution may not be applicable due to uncertainty and variation in system parameters. Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies. Specifically, a policy that can deliver good and stable performance resilient to variations in system parameters (such as buffer sizes, processing rates, and setup times) is viewed as a "robust" policy. Numerical studies indicate that the cyclic and longest queue policies exhibit robustness in subject to parameter changes. This could provide production engineers a guideline in operation management. © 2012 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)1145-1153
JournalComputers and Industrial Engineering
Volume63
Issue number4
DOIs
Publication statusPublished - Dec 2012
Externally publishedYes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work is supported in part by the National Natural Science Foundation of China (Grant No. 70771058/60834004 ), the 863 Program of China (2008AA04Z102), and NSF Grant No. CMMI-1063656 of USA.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Research Keywords

  • Markov chain
  • Multi-product
  • Robustness
  • Scheduling policies
  • Sequence-dependent setup
  • Throughput

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