Design and evaluation of automated guided vehicle systems for flexible manufacturing systems: an extended timed Petri net-based approach

Abstract

Design and evaluation of automated guided vehicle systems for flexible manufacturing systems: an extended timed Petri net-based approach K. RAVI RAJUt and O. V. KRISHNAIAH CHETTYtThe design and evaluation of automated guided vehiclesystems(AGVSs)for flexible manufacturing systems (FMSs) is complex because of the randomness and number of variables involved. A Petri net-based methodology is proposed in this paper for modelling and simulating AGVSs for FMSs. To this end, the capabilities of time Petri nets are expanded. The proposed methodology is elucidated with an example. Several design and operational and control issues,including the number of vehicles required, buffer sizes,siding sizes and vehicledispatching, are addressed. The effect of machine and AGV scheduling rules on system performance is also investigated. I. IntroductionAutomated guided vehicles (AGVs) are driverless vehicles which carry workpieces rapidly among workstations by following guide paths. They are usually controlled by off-board computers, communication with which is established with dedicated wiring in the floor. AGV systems (AGVSs) are widely used in flexible manufacturing systems (FMSs) on account of their flexibility and adaptability. However, they are highly complex and expensive. To realize the full potential of these systems, careful design and operational planning are essential. Computer simulation is usually employed to design and verify the operation of complex automated handling systems.This paper proposes a faster and more flexible approach for modelling and simulating AGVSs by expanding the power oftimed Petri nets. It also addresses some of the design and operational control issues of AGV-based FMSs. The paper is

Nested coloured timed Petri nets for production configuration of product families

“…Chin et al (2006) put forward methodologies based on integrated definition language and CPNs for modelling and simulating complicated manufacturing processes. Researchers, e.g., Ravi Raju and Krishnaiah Chetty (1993), Jiang et al (2000), Nandula and Dutta (2000), Dotoli and Fanti (2004), to name but a few, also apply CPNs and TPNs to manufacturing systems modelling, analysis and control. An observation on available PN models is that most researchers have adopted the basic ideas of different extensions of classic PNs and further extended them to accommodate the different modelling requirements of their own problem domains.…”

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

“…The problem has been formulated using different modelling techniques. A number of approaches describe the problem with mixed integer linear programming (Anwar and Nagi 1998;Bilge and Ulusoy 1995;Caumond et al 2009;Khayat, Langevin, and Riopel 2006;Lacomme, Moukrim, and Tchernev 2005;Nishi, Hiranaka, and Grossmann 2011;Raman, Talbot, and Rachamadgu 1986;Suri and Desiraju 1997;Zheng, Xiao, and Seo 2014), while a few works have considered PN (Lee and DiCesare 1994a;Raju and Chetty 1993;Sun, Cheng, and Fu 1994), and disjunctive graph modelling (Lacomme, Larabi, and Tchernev 2013). The other works whose methods are based on metaheuristics like genetic (Abdelmaguid et al 2004;Chaudhry, Mahmood, and Shami 2011;Jerald et al 2006;Reddy and Rao 2006;Ulusoy, Sivrikaya-Serifoglu, and Bilge 1997), differential evolution (Kumar, Janardhana, and Rao 2011), and simulated annealing (Deroussi, Gourgand, and Tchernev 2008), use a solution vector with fixed-length strings to represent a schedule called chromosome.…”

Application of Extended Stochastic Petri Nets to Simulation and Modelling of Construction Systems

“…This means that the transition will, when all the input conditions are met, take t time units for the firing process to deposit tokens in the output places (for a full description of the development of TPNs see: Viswanadham and Narahari, 1992;Ravi Raju and Krishnaiah Chetty, 1993;Venkatesh et al, 1994). The time delay can be deterministic or represented by any distribution function.…”