Stochastic coloured Petri net (SCPN) models of traditional and flexible kanban systems

Abstract

Modelling and analysis of kanban-controlled (just-in-time) production systems under realistic assumptions presents a number of challenges, including the ability to conduct both qualitative and quantitative analysis, and the ability to model control policies. These challenges are due, in part, to station interdependence, blocking and starvation due to limited bu er spaces, and the necessity of modelling both material and kanban¯ows. Petri nets (PNs) have recently emerged as a promising approach for modelling manufacturing systems. PNs are a graphical and mathematical technique useful for modelling concurrent, asynchronous, distributed, parallel, non-deterministic and stochastic systems. PN models can be analysed to determine both their qualitative and quantitative properties. In this paper, we develop stochastic, coloured PN (SCPN) models of a JIT system utilizing two di erent kanban control policies: a traditional kanban system (TKS) policy and a¯exible kanban system (FKS) policy. The resulting models can be used to represent JIT systems of arbitrary size, producing single or multiple types of products, with ® xed order points 1. The models are shown to be live and bounded, and can be simulated to produce quantitative results. Sample simulation results are presented to illustrate the models' capabilities.

Maintenance Process Simulation Based Maintainability Evaluation by Using Stochastic Colored Petri Net

“…After the transition t has been fired, the markings of the net will be changed according to the following rule [27] ( ) ( , ), -…”

“…Logistic factors are not considered in these studies either. The stochastic colored Petri net (SCPN) is extended from the classical Petri net by introducing colored tokens and stochastic timed transitions [27]. Compared with the classical Petri nets, the SCPN has two advantages in the modeling of the maintenance process simulation.…”

ALPHA/Sim simulation software tutorial

“…ALPHA/Sim and its predecessor, Modeler, have been used to develop a wide array of discrete-event simulation models, such as computer components and systems (e.g., Ethernet system (Brennan, Walenty, and Moore 1995), client-server system, and high-speed disk systems), manufacturing systems (Moore and Gupta 1999), largescale military command and control systems (Moore and Lynch 1990), and business process reengineering and workflow models for a charter air cargo and passenger service system.…”

<title>Modeling operational behavior of a disassembly line</title>

“…Our main focus is in the operational aspect of disassembly and, in particular, scheduling disassembly [1,3,4,5,6,7,16,17,18,19]. Although, the scheduling problems of assembly and disassembly share many characteristics (e.g., the dependent demand concept in discrete parts production systems), they also have their differences, and often the approaches to solve these two kinds of problems are very different [1].…”