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Appl. Sci. 2017, 7(1), 23; doi:10.3390/app7010023

A Genetic Regulatory Network-Based Method for Dynamic Hybrid Flow Shop Scheduling with Uncertain Processing Times

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Author to whom correspondence should be addressed.
Academic Editors: Naiqi Wu, Mengchu Zhou, Zhiwu Li and Yisheng Huang
Received: 2 November 2016 / Revised: 6 December 2016 / Accepted: 19 December 2016 / Published: 4 January 2017
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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Abstract

The hybrid flow shop is a typical discrete manufacturing system. A novel method is proposed to solve the shop scheduling problem featured with uncertain processing times. The rolling horizon strategy is adopted to evaluate the difference between a predictive plan and the actual production process in terms of job delivery time. The genetic regulatory network-based rescheduling algorithm revises the remaining plan if the difference is beyond a specific tolerance. In this algorithm, decision variables within the rolling horizon are represented by genes in the network. The constraints and certain rescheduling rules are described by regulation equations between genes. The rescheduling solutions are generated from expression procedures of gene states, in which the regulation equations convert some genes to the expressed state and determine decision variable values according to gene states. Based on above representations, the objective of minimizing makespan is realized by optimizing regulatory parameters in regulation equations. The effectiveness of this network-based method over other ones is demonstrated through a series of benchmark tests and an application case collected from a printed circuit board assembly shop. View Full-Text
Keywords: hybrid flow shop; uncertain processing time; genetic regulatory network; event-driven rescheduling strategy hybrid flow shop; uncertain processing time; genetic regulatory network; event-driven rescheduling strategy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lv, Y.; Zhang, J.; Qin, W. A Genetic Regulatory Network-Based Method for Dynamic Hybrid Flow Shop Scheduling with Uncertain Processing Times. Appl. Sci. 2017, 7, 23.

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