Modeling and Optimization Algorithm of FMS Logistics System Based on Petri Nets and PSO - Advanced Research on Computer Education, Simulation and Modeling - page 360

completion time minimization scheduling problem. In addition, the ant colony opti-
mization algorithm [3] and immune algorithm were applied to optimize the schedul-
ing problem, and achieved a better optimization results. Particle swarm optimization
(PSO) has the advantages ,such as easy achieving, high precision and fast conver-
gence speed.

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Fig. 1. Block diagram of FMS Fig. 2. Operation mode of FMS logistics system

On the other hand, in order to ensure the FMS in the production process has effi-
ciency and high stability, accurate modeling and simulation of its work before produc-
ing is meaningful. Petri net modeling approach is an ideal graphical modeling tool. In
this paper, it takes a small FMS logistics system as an example, using Petri nets to
model it and optimize the work process with the particle swarm optimization, which
uses the PPS-PPR encoding method.
For large high-tech flexible manufacturing systems, automatic storage and AGV
are often identified as the structural unit. Researching on FMS logistics subsystem
control methods, improving the efficiency of the logistics process, are essential for
achieving coordinated operation of the FMS system and improve of the production
efficiency [5].

2 Modeling of Logistics System Based on Petri Net

2.1 Petri Net

Petri net is a mathematical models of studying information systems and their relation-
ship. Petri net can express the static structure and dynamic changes of discrete event
dynamic system well, and use the form of network diagram to simulate discrete event
systems simply and intuitively. It's the most promising real-time control system mod-
eling tools set with system modeling, analysis, production planning and scheduling,
design and control.
The structure of Petri nets is a directed graph described by five elements:

PN=(P,T,I,O,M 0 )
Annotation: (1) P=(P 1, P 2 …P n ) is the finite set of place, n is the number of place and
n >0;(2) T=(T 1, T 2 …T m ) is the finite set of transition, m is the number transition and
P∩T=Φ ;(3) I:P×T->N is the input function. It defines the set including the repeated
number of directed arc or the right from P to T , and N={0,1,…, }is a non-negative
integer;(4) O:T×P->N is the output function, which defines the set including the re-
peated number of directed arc or the right from T to P ;(5) M 0 is the initial state that
initially identified markers.