Development of an intelligent approach to dynamic scheduling in robotic flexible assembly cells

Abstract

Manufacturing systems are mostly operating in dynamic environments. These systems are often subject to dynamic events that may cause deviations from the generated schedules, and the schedule plan may become impractical to implement when it is released to the system (Ouelhadj and Petrovic, 2009, Du, 2013). Hence, the influencing events must be taken into consideration when dealing with dynamic problems in any given production system (Gholami et al., 2009, Jain AK, 1997). The influencing events may be broadly categorised into two types (Vieira et al., 2003, Cowling and Johansson, 2002, Stoop and Weirs, 1996): the first is resourcerelated such as resource breakdown, tool failure, loading limits, shortage of material; the second is job-related, such as due date changing, early or late arrival time of jobs, changing of processing time, urgency of jobs, job cancellation and so on. (Zhang et al., 2013, Qiu and Lau, 2013, Nie et al., 2013, Rajabinasab and Mansour, 2011, Viond and Sridharan, 2008, Tang et al., 2005) indicate that the arrival time and due date are the most significant factors that affect dynamic job shop scheduling.