Reactive Scheduling Addressing Unexpected Disturbance in Cellular Manufacturing Systems

Document Type : Original Article


1 Department of Industrial Engineering, University of Kurdistan

2 Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran


Most production environments face random, unexpected events such as machine failure, uncertain processing times, the arrival of new jobs, and cancellation of jobs. For the reduction of the undesirable side effects of an unexpected disruption, the initial schedule needs to be reformed partially or entirely. In this paper, a mathematical model is presented to address the integrated cell formation and cellular rescheduling problems in a cellular manufacturing system. As a reactive model, the model is developed to handle the arrival of a new job as a disturbance to the system. Based on the principle of resistance to change, the reactive model seeks a new solution with the minimum difference from the initial solution. This is realized through a simultaneous minimization of the total completion time and the number of displaced machines. For the investigation of the performance of the proposed model, some numerical examples are solved using the GAMS software. The results demonstrate the ability of the reactive model to obtain solutions resistant to unexpected changes


1.     Vieira, G.E., Herrmann, J.W. and Lin, E., "Rescheduling manufacturing systems: A framework of strategies, policies, and methods", Journal of Scheduling,  Vol. 6, No. 1, (2003), 39-62.
2.     Wojakowski, P. and Warżołek, D., "The classification of scheduling problems under production uncertainty",  Vol. 4, (2014).
3.     Sakhaii, M., Tavakkoli-Moghaddam, R. and Vatani, B., "A robust model for a dynamic cellular manufacturing system with production planning", International Journal of Engineering,  Vol. 27, No. 4, (2014), 587-598. DOI: 10.5829/idosi.ije.2014.27.04a.09
4.     Soolaki, M. and Arkat, J., "Incorporating dynamic cellular manufacturing into strategic supply chain design", The International Journal of Advanced Manufacturing Technology,  Vol. 95, No. 5-8, (2018), 2429-2447.
5.     Arkat, J., Farahani, M.H. and Ahmadizar, F., "Multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling", International Journal of Computer Integrated Manufacturing,  Vol. 25, No. 7, (2012), 625-635.
6.     Rahimi, V., Arkat, J. and Farughi, H., "A vibration damping optimization algorithm for the integrated problem of cell formation, cellular scheduling, and intercellular layout", Computers & Industrial Engineering,  (2020), 106439.
7.     Arkat, J. and Ghahve, H., "Scheduling of virtual manufacturing cells with outsourcing allowed", International Journal of Computer Integrated Manufacturing,  Vol. 27, No. 12, (2014), 1079-1089.
8.     Mehdizadeh, E. and Rahimi, V., "An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts", Applied Soft Computing,  Vol. 42, (2016), 325-341.
9.     Bagheri, F., Safaei, A.S., Kermanshahi, M. and Paydar, M.M., "Robust design of dynamic cell formation problem considering the workers interest", International Journal of Engineering,  Vol. 32, No. 12, (2019), 1790-1797. DOI: 10.5829/IJE.2019.32.12C.12
10.   Feng, H., Xia, T., Da, W., Xi, L. and Pan, E., "Concurrent design of cell formation and scheduling with consideration of duplicate machines and alternative process routings", Journal of Intelligent Manufacturing,  Vol. 30, No. 1, (2019), 275-289.
11.   Rahmani, D. and Ramezanian, R., "A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: A case study", Computers & Industrial Engineering,  Vol. 98, (2016), 360-372.
12.   Olumolade, M., "Reactive scheduling system for cellular manufacturing with failure-prone machines", International Journal of Computer Integrated Manufacturing,  Vol. 9, No. 2, (1996), 131-144.
13.   Weckman, G.R., "A framework for reactive scheduling in a cellular manufacturing environment",  Vol., No.
14.   Li, W.-L. and Murata, T., "Particle swarm optimization method for rescheduling of job processing against machine breakdowns for nondisruptive cell manufacturing system", in 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012), IEEE. (2012), 523-528.
15.   Caruth, D., Middlebrook, B. and Rachel, F., "Overcoming resistance to change", SAM Advanced Management Journal,  Vol. 50, No. 3, (1985), 23-27.
16.   Giangreco, A. and Peccei, R., "The nature and antecedents of middle manager resistance to change: Evidence from an italian context", The International Journal of Human Resource Management,  Vol. 16, No. 10, (2005), 1812-1829.