Genetic algorithms for match-up rescheduling of the flexible manufacturing systems q Zalmiyah Zakaria a,⇑ , Sanja Petrovic b,1 a Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia b School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK article info Article history: Received 29 April 2009 Received in revised form 24 September 2011 Accepted 2 December 2011 Available online 9 December 2011 Keywords: Dynamic scheduling Match-up strategy Genetic algorithms Flexible manufacturing systems abstract Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufac- turing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS sched- uling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequenc- ing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non- reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction Flexible manufacturing systems (FMS) have been developed to provide a means to tackle a threefold challenge, namely better quality, lower cost and shorter lead times, by integrating the flex- ibility of job shops and the productivity of flow lines (Shukla & Chen, 1996). However, to achieve these advantages successfully, several production management problems (e.g. planning, schedul- ing, etc.) should be considered during the operation phase of FMS (Shi-jin, Li-feng, & Bing-hai, 2007). Among them, scheduling plays a vital role in the production control of an FMS (Liu & MacCarthy, 1996). It involves allocation of a limited set of resources to a num- ber of jobs with the goal of optimizing a given number of perfor- mance criteria over time. As FMS environment is dynamic and unexpected events occur, rescheduling is necessary to update an existing schedule in response to disruptions or changes. The FMS rescheduling problem has been investigated over the years, and it remains to attract the interests of researchers from academia and industry. Many approaches have been proposed to solve FMS rescheduling problem. The approaches to rescheduling can be classified into three categories (Aytug, Lawley, McKay, Mohan, & Uzsoy, 2005): (1) reactive scheduling which produces a schedule over time as the shop floor state changes; (2) robust scheduling in which a schedule is produced in such a way that it can absorb the disruptions on the shop floor; and (3) predictive- reactive scheduling in which a prior schedule which optimizes the shop floor performance is generated and then modified when- ever necessary. In reactive scheduling, the schedule is constructed when neces- sary using local information of the dispatched jobs. Commonly, dis- patching rules or other heuristics are used to prioritize the jobs waiting for processing. This approach has many practical advanta- ges because the schedules are easily generated without computa- tional burden but the quality of the solution is sacrificed due to the myopic nature of the rules (Sabuncuoglu & Bayiz, 2000; Sab- uncuoglu & Kizilisik, 2003). Reactive scheduling is also called in the literature on-line scheduling (Sabuncuoglu & Bayiz, 2000) or dynamic scheduling (Vieira, Herrmann, & Lin 2003). The robust scheduling, also called proactive scheduling, focuses on creating a schedule that is able to absorb disruptions without rescheduling (Goren & Sabuncuoglu, 2010; Sabuncuoglu & Goren, 2009). One example of this work is presented by Davenport, Gefflot, and Beck (2001) who use slack-based techniques to provide each operation with an extra time to execute so that some level of uncertainty can be absorbed without rescheduling. Jensen (2003) introduces 0360-8352/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.cie.2011.12.001 q This manuscript was processed by Area Editor Gursel A. Suer. ⇑ Corresponding author. Tel.: +60 7 5532357; fax: +60 7 5565044. E-mail addresses: zalmiyah@utm.my (Z. Zakaria), sxp@cs.nott.ac.uk (S. Petrovic). 1 Tel.: +44 115 9514222. Computers & Industrial Engineering 62 (2012) 670–686 Contents lists available at SciVerse ScienceDirect Computers & Industrial Engineering journal homepage: www.elsevier.com/locate/caie