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Business & Information Systems Engineering

, Volume 61, Issue 3, pp 299–309 | Cite as

An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

  • Zhengcai Cao
  • Lijie Zhou
  • Biao HuEmail author
  • Chengran Lin
Research Paper
  • 204 Downloads

Abstract

Modern manufacturing systems build on an effective scheduling scheme that makes full use of the system resource to increase the production, in which an important aspect is how to minimize the makespan for a certain production task (i.e., the time that elapses from the start of work to the end) in order to achieve the economic profit. This can be a difficult problem, especially when the production flow is complicated and production tasks may suddenly change. As a consequence, exact approaches are not able to schedule the production in a short time. In this paper, an adaptive scheduling algorithm is proposed to address the makespan minimization in the dynamic job shop scheduling problem. Instead of a linear order, the directed acyclic graph is used to represent the complex precedence constraints among operations in jobs. Inspired by the heterogeneous earliest finish time (HEFT) algorithm, the adaptive scheduling algorithm can make some fast adaptations on the fly to accommodate new jobs which continuously arrive in a manufacturing system. The performance of the proposed adaptive HEFT algorithm is compared with other state-of-the-art algorithms and further heuristic methods for minimizing the makespan. Extensive experimental results demonstrate the high efficiency of the proposed approach.

Keywords

Makespan Flexible job shop Adaptive scheduling HEFT 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable feedback. This work was supported in part by the Beijing Municipal Natural Science Foundation under Grant 4162046, in part by the National Natural Science Foundation of China under Grant 61802013, in part by the Talent Foundation of Beijing University of Chemical University under Grant buctrc201811, in part by the Open Research Project of the State Key Laboratory of Synthetical Automation for Process Industries under Grant H2018294, and in part by the Fundamental Research Funds for the Central Universities under Grant XK1802-4.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Zhengcai Cao
    • 1
  • Lijie Zhou
    • 1
  • Biao Hu
    • 1
    Email author
  • Chengran Lin
    • 1
  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina

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