Scheduling is a challenge that persists in the operational phase of the manufacturing life-cycle. The challenge can be attributed to the complex, dynamic, and stochastic nature of a manufacturing system. Computer simulation is often used to assist with scheduling, as it can sufficiently mimic complex, discrete, dynamic, stochastic processes. We propose an architecture of a real-time simulation scheduling system that incorporates the use of a sensorised-network of a job-shop, mobile devices, and cloud computing with simulation and scheduling methods. A simulation model is also created to describe the environment and operations of a job-shop.
OPSOMMINGSkedulering is 'n uitdaging wat in die operasionele fase van die vervaardigingslewensiklus voortduur. Hierdie uitdaging kan toegeskryf word aan die komplekse, dinamiese, en stogastiese aard van 'n vervaardigingstelsel. Simulasie word dikwels gebruik om te help met die skeduleringsproses, aangesien dit komplekse, diskrete, dinamiese, stogastiese prosesse voldoende kan naboots. Ons stel 'n argitektuur van 'n reële tyd simulasie skeduleringstelsel voor, wat gebruik maak van 'n sensornetwerk in 'n werkswinkel, mobiele toestelle, sowel as simulasie-en skeduleringsmetodes in die elektroniese wolk. 'n Simulasiemodel is ook geskep om die omgewing en aktiwiteite van 'n werkswinkel te beskryf.
INTRODUCTIONManufacturing is described by Groover [1] as the transformation of materials into items of greater value by one or more processing or assembly operations. The processes to accomplish the transformation involve a combination of machinery, tools, power, and manual labour. From Groover's description of manufacturing, the process can be considered a complex engineering endeavour, where the coordination of people, material, equipment, and information to accomplish a manufacturing goal demands considerable time and effort.According to Mitsuishi, Ueda and Kimura [2], the manufacturing system life-cycle can be divided into design, planning, implementation, operation, and termination phases. The coordination challenges that are faced in the design and implementation phases can be overcome by careful planning; however, such challenges persist into the operational phase. For many manufacturing systems, scheduling is one such challenge that can be attributed to the complex, dynamic, and stochastic environments exhibited by these systems.Traditional approaches used to address the scheduling problems involve creating and evaluating schedules prior to beginning production. However, according to Suwa and Sandoh [3], uncertainties that are not expected nor taken into account at the planning phase can cause delays of these schedules. Common uncertainties that occur in a manufacturing system include machine operator absence, material shortages, and machine failure. In such scenarios, the manager must react quickly by selecting a new or revised schedule to ensure that production continues while maintaining the
