A deadlock prevention method for railway networks using monitors for colored Petri nets
|
|
- Martin Holland
- 6 years ago
- Views:
Transcription
1 A deadlock prevention method for railway networks using monitors for colored Petri nets Maria Pia Fanti Dip di Elettrotecnica ed Elettronica Politecnico di Bari, Italy Abstract The real-time traffic control of railway networks authorizes movements of the trains and imposes safety constraints The paper deals with the real time traffic control focusing on deadlock prevention problem Colored Petri nets are used to model the dynamics of the railway network system: places represent tracks and stations, tokens are trains The prevention policy is expressed by a set of linear inequality constraints, called colored Generalized Mutual Exclusion Constraints that are enforced by adding appropriate monitor places Using digraph tools, deadlock situations are characterized and a strategy is established to define off-line a set of Generalized Mutual Exclusion Constraints that prevent deadlock An example shows in detail the design of the proposed control logic Keywords: Railway network system control, colored Petri nets, deadlock prevention Introduction The traffic control of Railway Network Systems (RNS) is a critical task in modern railway transportation The structure of the railway traffic planning and control can be divided in two main hierarchical levels [] The first level is the planning level that works off-line and constructs the traffic plan and timetable The second control level makes decisions in real-time and it has two tasks Firstly, this level analyzes perturbed situations In particular, the goal of the controller is to reduce delays and to make traffic return to planned paths when trains have deviations from the scheduled timetable [] Secondly, the real-time controller has to impose the satisfaction of safeness constraints to avoid collisions and deadlocks This paper focuses on the second task of the real-time controller The RNS is modelled with colored Petri nets (CPNs) [, ] that provide a powerful framework to the analysis and the definition of safeness constraints In the related literature, railway networks are modelled as discrete event systems [] that define a control design problem leading to a non-convex nonlinear optimization problem Moreover, in [8] the railway network is modelled by a Petri net and deadlock avoidance constraints are expressed as Generalized Mutual Exclusion Constraints (GMEC) However, the controller does not distinguish among the routes assigned to the trains, and /3/$7 c 3 IEEE Alessandro Giua, Carla Seatzu Dip di Ing Elettrica ed Elettronica Università di Cagliari, Italy {giua,seatzu}@dieeunicait when forks and joins are present in the network the marking does not describe completely the system state This paper overcomes this problem by using CPN to model the RNS structure and dynamics More precisely, places represent tracks and stations, while transitions are the control points where the train movements are enabled or inhibited Moreover, the trains travelling in the system are represented by colored tokens and their color is the assigned path To characterize deadlock situations we use some results obtained in the field of Automated Manufacturing Systems where deadlock has been widely studied [3, 4, 5, 6] We characterize deadlock states by using digraph tools that describe the interactions between trains and resources (ie, tracks and stations) In addition, a deadlock prevention strategy defines off-line the rules to prevent deadlock in advance In this framework, deadlock and collision prevention constraints are expressed by a set of linear inequality constraints, called colored GMEC A companion paper [7] rigorously defines colored GMEC and shows how the results obtained for GMEC applied to place/transition nets can be extended to colored Petri nets Hence, the controller takes the form of a set of colored monitor places that can be automatically computed An example shows the design technique of the proposed control logic The railway network system A RNS consists of three fundamental elements: railway lines, stations, vehicles (ie, trains, single engines, etc) travelling over these lines Let us consider the set V = {v,, v NV } that collects all the vehicles moving over the lines and the stations The railway lines are divided into several tracks and each track can be occupied by only one vehicle at a time In our framework, each station is described by a resource r i, for i =,, N S, where N S is the number of stations Moreover, each track of the RNS is viewed as a resource that vehicles can acquire and it is denoted by r i, for i = N S +,, N S + N T, where N T is the overall number of tracks Since each station is composed of one or more tracks, and each track can accommodate only one train at a time, a finite capacity C(r i ) is assigned to each station r i, for i =,, N S Moreover, each track r i has unit capacity, ie, C(r i ) = for all i = N S +,, N S + N T We also assume that the terminal stations of the train paths are connected to a virtual docking station r The docking station can accommodate all the trains in
2 r r r 3 r 4 r r 7 6 r 8 Figure : The railway network of Example the system, ie, C(r ) = Finally, we generically call resources or nodes the stations and the tracks Therefore, the set R = {r i, i =,, N S +N T } denotes the resource set of the system Other basic elements of the RNS are the control points where the trains are authorized to enter a generic node by the real-time traffic controller In addition, a path (or route) π k is assigned to each train v k V travelling in the system: each vehicle starts its travel from a station; it reaches a destination station and finally the docking station where a new path can be assigned to it More precisely, each path is described by the following sequence of resources that ends at the docking station: π k = (r k,, r knk, r ) The set A collects all the possible paths planned in the system Example Let us consider the railway composed by five stations r i, for i =,, 5, depicted in Figure The first station is a three track station, while the remaining ones have only two tracks, ie, C(r ) = 3 and C(r i ) = for i =, 3, 4, 5 All intermediate tracks r i, for i = 6, 9,, are single tracks, apart from r 7 and r 8 that represent two track segments This paper deals with the real-time traffic control level whose task is that of authorizing the movement of the trains and imposing safety constraints Such a control can be applied to railway tracks and to station tracks, and its main goal is that of avoiding deadlock and collisions in all subsystems 3 CPN and GMEC In this section we provide some background on colored Petri nets and generalized mutual exclusion constraints For more details we address the reader to the companion paper [7] 3 Overview of CPN A colored Petri net (CPN) is a bipartite directed graph represented by a quintuple N = (P, T, Co, P re, P ost) where P is the set of places, T is the set of transitions, Co : P T Cl is a color function that associates to each element in P T a non empty ordered set of colors in the set of possible colors Cl Therefore, for all p i P, Co(p i ) = {a i,, a i,,, a i,ui } Cl is the ordered set of possible colors of tokens in p i, and u i is the number of possible colors of tokens in p i Analogously, for all t j T, Co(t j ) = {b j,, b j,,, b j,vj } Cl is the ordered set of possible occurrence colors of t j, and v j is the number of possible occurrence colors in t j r In the following we assume that m = P and n = T Matrices P re and P ost are the pre-incidence and the post-incidence m n dimensional matrices respectively In particular, each element P re(p i, t j ) is a mapping from the set of occurrence colors of t j to a non negative multiset over the set of colors of p i, namely, P re(p i, t j ) : Co(t j ) N (Co(p i )), for i =,, m and j =,, n In the following we denote P re(p i, t j ) as a matrix of u i v j non negative integers, whose generic element P re(p i, t j )(h, k) is equal to the weight of the arc from place p i with respect to (wrt) color a i,h to transition t j wrt color b j,k Analogously, P ost(p i, t j ) : Co(t j ) N (Co(p i )), for i =,, m and j =,, n, and we denote P ost(p i, t j ) as a matrix of u i v j non negative integers The generic element P ost(p i, t j )(h, k) is equal to the weight of the arc from transition t j wrt color b j,k to place p i wrt color a i,h The incidence matrix C is an m n matrix, whose generic element C(p i, t j ) : Co(t j ) Z(Co(p i )), for i =,, m and j =, n In particular C(p i, t j ) = P ost(p i, t j ) P re(p i, t j ) For each place p i P, we define the marking m i of p i as a non negative multiset over Co(p i ) The mapping m i : Co(p i ) N associates to each possible token color in p i a non negative integer representing the number of tokens of that color that is contained in place p i, and m i = d Co(p m i) i(d) d In the following we denote m i as a column vector of u i non negative integers, whose h-th component m i (h) is equal to the number of tokens of color a i,h that are contained in p i The marking M of a CPN is an m-dimensional column vector of multisets, ie, M = [ m m m ] T Finally, we denote M = p i P m i the total number of tokens in the net at marking M regardless of their color A colored Petri net system N, M is a colored Petri net N with initial marking M A transition t j T is enabled wrt color b j,k at a marking M if and only if for each place p i P and for all h =,, u i, we have m i (h) P re(p i, t j )(h, k) If an enabled transition t j fires at M wrt color b j,k, then we get a new marking M where, for all p i P and for all h =,, u i, m i (h) = m i(h) + P ost(p i, t j )(h, k) P re(p i, t j )(h, k) We will write M[t j (k) M to denote that t fires at M wrt color b j,k yielding M 3 GMEC and monitors In the Petri nets framework a supervisory controller restricts the reachability set of the closed loop plant to a set of legal markings In many applications, the set of legal markings is expressed by a set of linear inequality Let D be a set A multiset (resp, non negative multiset) over D is defined by a mapping α : D Z (α : D N) and may be represented as = X d D α(d) d where the sum is limited to the elements such that α(d) Let Z(D) (resp, N (D)) denote the set of all multisets (resp, non negative multisets) over D The multiset " is the empty multiset such that for all d D, (d) =
3 constraints called Generalized Mutual Exclusion Constraints (GMEC) In [7] the notion of GMEC is extended to the case of colored Petri nets as follows Definition 3 ([7]) A GMEC is a couple (W, k) where W = [ w w m ], k Z(D), for all i, w i : Co(p i ) Z(D), and D is a set of colors different from Co(p i ), i =,, m The set of legal markings defined by (W, k) can be written as M(W, k) = M = m m m m i N (Co(p i )), W M } m i= w i m i k () Note that each term w i m i is a multiset that can be computed using the usual matrix algebra [7] Moreover, as discussed in detail in [7], a GMEC can be enforced by adding a monitor place p c and a systematic procedure can be used to compute the incidence matrix defining such a monitor place, as well as its initial marking Definition 3 ([7]) Given a colored Petri net system N p, M p,, with N p = (P, T, Co, P re p, P ost p ), and a GMEC (W, k) with k Z(D), the monitor that enforces this constraint is a new place p c with Co(p c ) = D, to be added to N p The resulting system is denoted N, M, with N = (P {p c }, T, Co, P re, P ost) Then N will have incidence matrix C = [ Cp C c ], where C c = W T C p () We are assuming that there are no selfloops containing p c in N, hence P re and P ost may be uniquely determined by C The initial marking of N, M is [ ] M M = p, m, where m c, = k W T M p, (3) c, We assume that the initial marking M p, of the system satisfies the constraint (W, k) In the case of controllable and observable transitions we can prove the following result Theorem 33 ([7]) Let N p, M p, be a CPN system, and (W, k) a colored GMEC Let N, M be the system with the addition of the monitor place p c The monitor place p c enforces the GMEC (W, k) when included in the closed loop system N, M The monitor place p c minimally restricts the behavior of the closed loop system N, M, in the sense that it prevents only transition firings that yield forbidden markings 4 The CPN model of the RNS In this paper we use colored Petri nets to model RNS In particular, places represent resources (stations and tracks), while the firing of transitions represent the flow of vehicles into the system The generic place p i P models resource r i R and there is a one to one relationship between resources and places, thus in the following we always refer to P as R (and to p i as r i ) Moreover, if there exists a link that goes from node r h to node r i, then in the CPN we introduce a transition t j such that t j r h and t j r i Note that each transition represents a control point where the controller can stop the trains or can authorize a train to move on Thus all transitions in the CPN model are assumed to be both controllable and observable A colored token in a place represents a vehicle in a resource The color of each token specifies the vehicle v k or, equivalently, the routing π k assigned to the train As an example, π k = (r h,, r q, r ) can be the path (ie the sequence of resources) assigned to the train v k Hence, for each r i R we have Co(r i ) = {π k π k contains r i }, and for each t j T such that t j r h and t j r i, we have Co(t j ) = {π k π k contains r h and r i in strict succession order} Example 4 Let us consider the RNS described in Example The corresponding CPN model is reported in Figure where place r represents the docking station Let us assume that four trains are travelling in the system, namely, v, v, v 3 and v 4 Moreover, let π = (r, r 6, r, r 7, r 3, r 9, r 4, r ), π = (r 4, r 9, r 3, r 8, r, r 6, r, r ), π 3 = (r, r 6, r, r,, r ), π 4 = (, r, r, r 6, r, r ) Therefore, by definition Co(r ) = {π, π, π 3, π 4 } for i =,,, 6, Co(r i ) = {π, π } for i = 3, 4, 9, Co(r 7 ) = {π }, Co(r 8 ) = {π }, Co(r i ) = {π 3, π 4 } for i = 5,, and Co(t j ) = {π, π 3 } for j =,, 9, Co(t j ) = {π, π 4 } for j = 3, 4,, Co(t j ) = {π 3 } for j = 3, 4, 7, Co(t j ) = {π 4 } for j = 5, 6,, Co(t j ) = {π } for j = 5, 6, 7, 8, 8, Co(t j ) = {π } for j = 9,,,, The pre and post-incidence matrices can be easily deduced by looking at the structure of the net and at the above paths definition As an example P re(r, t ) = P ost(r 6, t ) = π π 3 π π π 3 π 4 Now, let us assume that initially trains v and v 3 are in r, train v is in r 4 and train v 4 is in Thus the CPN system is initially at marking M p, = m, m, m, m 3, m 4, m 5, m 6, m, = π + π 3 π π 4 Using the matrix notation, each term m i, may be written as a column vector of dimension Co(r i ) As an Given a node x P T we denote as x and x the preset and the postset of x, respectively
4 t 4 t 3 t r 8 t t t 9 r r 6 r r 3 r 9 r4 t 5 t t r 7 t 6 t 7 t 8 t 9 t 3 t 4 t t 7 r t6 r t 5 t Figure : The CPN model of the RNS in Figure example, m, = t π [ π π, m 4, = 3 π 4 t 8 ] π 4 Capacity constraints As already said before, all tracks and stations have a finite capacity, apart from the docking station r that is a dummy node To ensure that each resource does not accommodate a number of trains that is greater than the corresponding capacity, we have to introduce appropriate capacity constraints More precisely, for all r i R \ {r }, with i =,, m, we have to impose that u i m i (π jh ) C(r i ) h= where Co(r i ) = {π j,, π jui } and u i = Co(r i ) The capacity constraints may be rewritten in terms of a single GMEC (W, k), that we call capacity GMEC The capacity GMEC will have as color set D = {z,, z m } because we need m capacity constraints, and is defined as follows: W = [ w w w m ], w = w i = π j π jui z z i z i z i+ z m k = [ C(r ) C(r m ) ] T Z(D) π i =,, m (4) Using the above theory, an appropriate monitor place can be added to the open loop net so as to ensure the satisfaction of the capacity constraints Example 4 Let us consider again the CPN system in Example 4 associated to the RNS described in Example In such a case we have m = and k = [ 3 ] T The incidence matrix of the monitor place is equal to C c = W C p where C p is the incidence matrix of the open loop net The incidence matrix of p c has the following structure, As an example, C c = [ C c (p c, t ) C c (p c, t 5 ) ] C c (p c, t ) = π π 3 z z z 3 z 4 z 5 z 6 z 7 z 8 z 9 z while all the other matrices C c (p c, t j ), j =,, 5, are omitted here for sake of brevity Finally, the monitor place p c is initialized at marking m c, = [ ] T 5 Deadlock prevention policy In this section we first provide some background on digraph theory Then we show how some theoretical results firstly obtained in the context of deadlock avoidance in Automated Manufacturing Systems [5, 6], can be used here to derive a deadlock prevention policy 5 Basic definitions A digraph is a couple D = (N, E) where N is the set of vertices and E is the set of edges [9] A path is a subdigraph of D composed by an alternating sequence of distinct vertices and arcs If D contains a path from r i to r j, then r j is said reachable from r i Moreover, if r j (r i ) is reachable from r i (r j ), then r i and r j are said mutually reachable A cycle of D is a nontrivial path in which all vertices are distinct except the first and the last one 3 A subdigraph D µ = (N µ, E µ ) of D is called strong if every two vertices of N µ are mutually reachable Finally, a strong component of D is a maximal strong subdigraph, ie, a strong subdigraph that is not contained in any other strong subdigraph of D Let us finally introduce a relation that is based on the order in which the resources in a RNS are used 3 What we call cycle is sometimes called elementary cycle
5 Definition 5 A resource r j immediately follows a resource r i with respect to path π k if π k = (, r i, r j ) This is also denoted r i k r j 5 Deadlock characterization Given a CPN describing a RNS, a deadlock corresponds to a marking from which all transitions enabled in the plant are disabled by the capacity GMEC: such a marking is said deadlock marking In this section we establish some necessary and sufficient conditions for deadlock occurrence based on the analysis of the digraphs associated to a colored Petri net Note that these results only apply to a CPN representing a RNS as described in Section 4 In the following however, for sake of simplicity, we will omit to precise this in the statement of all results simply referring to a CPN: we will also talk of places as resources, trains as colored tokens, etc We can now define two digraphs associated to a RNS represented by a CPN Definition 5 Given a colored Petri net N p = (R, T, Co, P re, P ost) we may associate to it two main digraphs The route digraph D R = (N R, E R ) describes the paths of all the trains travelling in the system Each vertex in this graph represents a resource, ie, N R = R while E R = {e ij ( π k A) r i k r j }, ie, an edge e i,j belongs to E R if there exists a path where r j follows r i The transition digraph D T (M p ) = (N R, E T (M p )), describes the interactions between trains and resources when the actual marking is M p Each vertex in this graph still represents a resource as in the route digraph, ie, N R = R, while E T (M p ) = {e ij ( π k A)m i π k, r i k r j }, ie, an edge e i,j belongs to E T (M p ) if there exists a train in resource r i at marking M p and r j is the next resource the train has to acquire Obviously, the arc set of the transition digraph changes as the marking is updated Example 53 Figure 3 shows digraph D R corresponding to the system and the CPN described in Examples and 4 Each edge of D R is labelled with the name of the path to which it correspond Moreover, for sake of simplicity, the node r is repeated in Figure 3 Assume that the four trains travelling in the system are in these positions: v in r 6, v and v 3 in r, and v 4 in r Hence, the CPN is at marking M p equal to m 6 = π, m = π + π 3, m = π 4, m i = elsewhere The corresponding transition digraph D T (M p ) is shown in Figure 4 To characterize deadlock markings we also need the following definition Definition 54 A strong component D µ = (N µ, E µ ) of D T (M p )is called a Maximal-weight Zero-outdegree Strong Component (MZSC for brevity) if the following properties hold true: r 8 pa pa4 pa pa pa r pa pa γ r pa pa γ γ3 γ4 γ5 3 pa pa r 3 pa pa r 6 r pa r pa r pa pa pa pa pa γ pa 6 4 pa pa 3 4 r r r pa pa 3 3 γ7 Figure 3: Digraph D R for Example 53 r r 6 r pa r γ pa pa 3 r γ6 Figure 4: Digraph D T (M p ) for Example 53 r 8 r 7 (a) Maximal-weight: all the resources from N µ are busy, ie, the number of tokens in each place r i is equal to the maximal capacity of the place: m i = C(r i ) (b) Zero-outdegree: all the edges of D T (M p ) outgoing from vertices of N µ belong to E µ Remark 55 Note that the node r corresponding to the docking station can not be in a MZSC because it has an infinite capacity and all cycles containing it may be disregarded for deadlock analysis It is possible to give a simple characterization of an MZSC in terms of resources allocated to the trains at a given marking Definition 56 Given a strong subdigraph D µ = (N µ, E µ ) of D R and a marking M p, we denote the set of trains that occupy a resource of N µ and require a resource of N µ at next step as V (M p ) µ = {v k V ( r i, r j N µ ) m i π k, r i k r j, e i,j E µ } The following result holds Proposition 57 A necessary condition for a strong subdigraph D µ = (N µ, E µ ) of D R to be an MZSC in D T (M p ) is that V (M p ) µ = C(N µ ) where C(N µ ) = r i N µ C(r i ) is the sum of the capacities of the resources in N µ Proof First we observe that if D µ is a MZSC then (by the maximal-weight condition) the number of tokens in r 3 r 9 r 4
6 N µ at M p is equal to r i N µ m i = r i N µ C(r i ) = C(N µ ) Furthermore, each of these tokens corresponds to a train in the set of resources N µ that (by the zerooutdegree condition) requires at the next step a resource in N µ, ie, C(N µ ) = V (M p ) µ In [5] and [6] necessary and sufficient conditions for deadlock occurrence have been characterized in terms of digraph analysis Proposition 58 A marking M = [M T p m c ] T is a deadlock marking for a CPN with capacity constraint iff there exists at least one MZSC in D T (M p ) Proof The statement is a slightly different formulation of Theorem from [5] in terms of net marking rather that system state As such, it applies to CPN modelling RNS From proposition 57 and Proposition 58, the following Corollary is derived Corollary 59 If M = [M T p m c ] T is a deadlock marking for a CPN with capacity constraint, then there exists a strong component D µ = (N µ, E µ ) of D R such that V (M p ) µ = C(N µ ) Example 5 Let us consider again Example 53 and the transition digraph D T (M p ) in Figure 4 corresponding to the defined marking M p It is easy to verify that the strong component D µ = γ γ 6 = ({r 6, r, r }, {e 6,, e,, e,, e,6 }) of D R is an MZSC in D T (M p ) Moreover, we obtain: V (M p ) µ = {v, v, v 3, v 4 }, and V (M p ) µ = C(N µ ) = 4 53 Second level deadlocks By imposing constraints of the form V (M p ) µ C(N µ ) we can prevent any strong component of D R from becoming an MZSC in the transition digraph However, avoiding a deadlock marking is not sufficient to guarantee the liveness of the CPN Indeed, it is possible that some critical states are reached that are not deadlocks, but they necessary evolve to a deadlock marking in the next step: these states are called Second Level Deadlocks (SLD) [5] Clearly, if a SLD marking is reached, then a controller that has been designed to prevent reaching a deadlock marking for the original net will create a new deadlock marking We discuss in this subsection how it may be possible to also prevent a SLD A SLD can be characterized in terms of a particular interaction among the cycles of D R that can be represented by a new digraph Definition 5 Given a colored Petri net with route digraph D R = (N R, E R ) we define the second level digraph DR = (N, ER ) such that: the set of vertices N = {γ, γ,, γ N } is equal to the set of cycles of D R that do not contain the dummy node 4 r ; an edge e u,s belongs to E R if: 4 The dummy node r has infinity capacity and all cycles containing it may be disregarded for deadlock analysis as mentioned in remark 55 γ γ γ γ γ 3 γ 4 γ 5 γ 6 γ 7 Figure 5: Digraph D R obtained from digraph D R in Figure 3 γ 3 i) γ u and γ s have only one vertex in common (say r j ) with capacity C(r j ) = ; ii) there exists a path π k A such that r i k r j k r h requiring resources r i, r j, r h in strict order of succession, with e i,j γ u and e j,h γ s Now let γu be a cycle in DR (second level cycle) From the previous definitions it follows that a subset of cycles of D R (say Γ u ) is associated with vertices of γu So, the capacity of γu can be defined as C(γ u) = r {γ γ Γ u} C(r) ie, as the sum of the capacities of all the resources in Γ u Finally, let Γ indicate the subset of cycles of DR, enjoying the following property: γu Γ iff the corresponding set Γ u collects cycles that are all disjoint except for one vertex of unit capacity, common to all of them For each γu Γ and for each marking M p we introduce the following set: V (M p ) γ u = {v k V at marking M p the token representing v k is in r Γ u } As shown by the following proposition easily derived from the results proved in [5], the set Γ plays an important role in defining SLD conditions Proposition 5 If M = [M T p m c ] T is a SLD marking for a CPN with capacity constraint, then there exists in D R a second level cycle γ u Γ such that: V (M p ) γ u = C(γ u) Example 53 Considering the cycles of D R depicted in Figure 3, the second level cycles of DR are derived and shown in Figure 5 We suppose that the four trains in the system are in the following state: v and v are in with π = π = (, r, r, r 6, r, r ), v 3 and v 4 are in r with π 3 = π 4 = (r, r 6, r, r,, r ) Hence, the CPN is at marking M p where m = π 3, m 5 = π 4, and m i = elsewhere The solid lines in Figure 6 depicts the transition digraph D T (M p ) For convenience, Figure 7 also depicts the second transitions (dashed lines) in the residual paths of the trains The described marking exhibits a SLD for the CPN Indeed, the second level cycle γ3 Γ is in second level deadlock condition, were γ3 corresponds to the cycle set Γ u = γ 6 γ 7 = ({r, r, }, {e,, e,5, e 5,, e, })
7 r r 6 r r pa 3 pa 3 r γ6 γ7 Figure 6: Digraph D T (M p ) for Example 53 Hence, the necessary condition of Proposition 5 is verified, ie, V (M p ) γ 3 = {v, v, v 3, v 4 } = C(γ 3) = 4 54 Deadlock prevention Corollary 59 and Proposition 5 establish the deadlock and SLD prevention conditions on the marking of the CPN To establish a direct relation among V (M p ) µ, where D µ = (N µ, E µ ) is a strong subdigraph of D R, and the marking of the CPN, the following index set is defined: H µ (r i ) = {h a i,h Co(r i ) Co(r k ) with e i,k E µ and r k N µ for each r i N µ } A controller will prevent reaching a deadlock or a SLD marking if it can enforce the following deadlock prevention GMEC: for each strong subdigraph D µ of D R V (M p ) µ = m i (h) C(N µ ) r 8 r 7 r i N µ h H µ (r i ) for each γ u Γ of D R V (M p ) γ u = r i N Γ u r 3 r 9 r 4 (5) m i C(γ u) (6) Note that being the number of trains in the system always limited (say N max ) it may happen that some of the constraints (5) and (6) are trivially verified because N max C(N µ ) or N max C(γ u) Obviously, the deadlock prevention strategy can be simplified by neglecting such constraints The GMEC given by Equations (5) and (6) restrict the reachability set of the closed loop plant, to avoid deadlock and SLD However, the imposed GMEC can eventually lead to a situation similar to a deadlock, which is known as restricted deadlock (RD) because the constraint for SLD may create in turn higher level deadlocks The following proposition establishes the conditions that must be verified to obtain a RD free closed loop system Proposition 54 If M = [M T p m c ] T is a RD marking for a CPN with capacity constraint, then one of the following two conditions are verified: a) there exist at least two cycle sets Γ and Γ of D R corresponding to the cycles γ, γ Γ such that V (M p ) γ = C(γ ) and V (M p ) γ = C(γ ) ; b) there exists at least a cycle set Γ of D R corresponding to γ Γ and a cycle γ of D R such V (M p ) γ = C(γ ) and V (M p ) γ = C(γ ) Proof If M p is a RD marking of the closed loop system, the restricted deadlock is not determined by the GMEC in Equation (5) because M p is not a SLD marking Hence, one of the following conditions is verified a) There exist at least two constraints defined by Equation (6) that determine the RD marking, say V (M p ) γ < C(γ) and V (M p ) γ < C(γ) More precisely there is a transition t j that is disabled by the constraint V (M p ) γ < C(γ) and a transition t j that is disabled by the constraint V (M p ) γ < C(γ) Hence, there exists an edge e v,q E R, corresponding to transition t j, such that r v Γ and r q Γ, and V (M p ) γ = C(γ) Analogously, there exists an edge e i,m E R, corresponding to transition t j, such that r i Γ and r m Γ, and V (M p ) γ = C(γ) This proves the first condition b) There exists at least one constraint defined by Equation (6) (say V (M p ) γ < C(γ) ) and one constraint defined by Equation (5) (say V (M p ) γ < C(γ ) ) that cause the RD marking More precisely there is a transition t j that is disabled by the constraint V (M p ) γ < C(γ) and a transition t j that is disabled by the constraint V (M p ) γ < C(γ ) Hence, there exists an edge e v,q E R, corresponding to transition t j, such that r v Γ and r q Γ, and V (M p ) γ = C(γ) Analogously, there exists an edge e i,m E R, corresponding to transition t j, such that r i γ and r m γ, and V (M p ) γ = C(γ ) This proves the second condition We define the following parameters: { C = C(γ i ) + C(γj ) }, C = min γ i,γ j Γ,i j { min C(γ i ) + C(γ j ) }, γi Γ,γ j D R while C = min{c, C } Corollary 55 If M p, < C, the closed loop system with the monitors that enforce the deadlock prevention GMEC in Equations (5) and (6) is deadlock free Proof Follows from Proposition 54, because M p, is not a RD marking The following example enlightens the proposed prevention strategy Example 56 Let us consider again the CPN in Figure To obtain the deadlock prevention GMEC, we have to determine the sets V (M p ) γv associated with cycle γ v with v =,, 7, the set V (M p ) µ associated with the strong component D µ = γ γ 6 =
8 ({r 6, r, r }, {e 6,, e,, e,, e,6 }) of D R (see Figure 3) and the sets V (M p ) γ u with u =,, 3 associated with the second level cycles γu Γ of DR with u =, 3 Considering the previously defined sets, the following conditions are imposed by the prevention policy Note that we suppose four trains in the system, and we do not consider the always-verified constraints (for example the constraint V (M p ) γ 4) V (M p ) γ 3 m (π ) + m (π 3 )+ () m 6 (π ) + m 6 (π 4 ) 3 V (M p ) γ m (π ) + m (π 4 )+ () m 6 (π ) + m 6 (π 3 ) V (M p ) γ4 m 3 (π ) + m 9 (π ) (3) V (M p ) γ5 m 4 (π ) + m 9 (π ) (4) V (M p ) γ6 m (π 3 ) + m (π ) (5) V (M p ) γ7 m 5 (π ) + m (π ) (6) V (M p ) γ γ 6 3 m (π ) + +m (π 3 )+ (7) m (π 4 ) + m 6 (π )+ m 6 (π 3 ) + m (π ) 3 V (M p ) γ 3 m 3 (π ) + m 3 (π )+ (8) m 4 (π ) + m 4 (π ) +m 9 (π ) + m 9 (π ) 3 V (M p ) γ 3 3 m (π ) + m (π )+ (9) m (π 3 ) + m (π 4 )+ m 5 (π ) + m 5 (π )+ m (π ) + m (π ) 3 Moreover, we obtain C = min{6, 5} = 5 Since the initial marking is such that M p, < C, the resulting closed loop system is deadlock and RD free The above 9 constraints can be rewritten in terms of a single GMEC (W, k ), that we call deadlock prevention GMEC The deadlock prevention GMEC will have as color set D = {z,, z 9} because we have to impose 9 constraints, and is defined as follows: W = [ w w w ] w = k = [ ] T Z(D ) As an example, we report here the numerical values of w and w 3, while the other w i s are omitted for sake of brevity: w = π π π 3 π 4 z z z 3 z 4 z 5 z 6 z 7 z 8 z 9 w 3 = π π z z z 3 z 4 z 5 z 6 z 7 z 8 z 9 Note that each matrix w i has as many rows as the number of constraints (ie, 9 rows) and as many columns as the number of colors that may be contained in place r i Moreover, by looking at matrix w we may observe that its first row is null because the marking m is not involved in the first constraint On the contrary, the non null elements in its second row, relative to π and π 4 are due to the fact that m (π ) and m (π 4 ) are involved in the second constraint The value is due to the fact that is the associated coefficient in the corresponding linear constraint 6 Conclusions In this paper, we provide a Colored Petri Net model to describe a Railway Network System and to derive the traffic controller The introduced framework allows us to define a supervisor controller guaranteeing safeness and deadlock freeness in the railway traffic control system Starting from the analysis of deadlock on the basis of digraph tools, a deadlock prevention strategy is defined and expressed by a set of linear inequality constraints Moreover, we have shown how collision and deadlock prevention constraints can be expressed as colored GMEC and the controller can be realized by a set of monitor places References [] B De Schutter, T van den Boom, Model predictive control for railway networks, IEEE/ASME Int Conf on Advanced Intelligent Mechatronics, Como, Italy, July [] AA Desrochers, RY Al-Jaar, Applications of Petri nets in manufacturing systems, New York, IEEE Press, 995 [3] J Ezpeleta, JM Colom, J Martinez, A Petri net based deadlock prevention policy for flexible manufacturing systems, IEEE Trans on Robotics and Automation, Vol, No, pp 73-84, 995 [4] MP Fanti, B Maione, S Mascolo, B Turchiano, Control policies conciliating deadlock avoidance and flexibility in FMS resource allocation, ETFA95, INRIA/IEEE Symp on Emerging Technologies and Factory Automation, Paris, France, October 995 [5] MP Fanti, B Maione, S Mascolo, B Turchiano, Event based feedback control for deadlock avoidance in flexible production systems, IEEE Trans on Robotics and Automation, Vol 3, No 3, pp , 997 [6] MP Fanti, B Maione, B Turchiano, Deadlock avoidance in flexible production systems with multiple capacity resources, Studies in Informatics and Control, Vol 7, No 4, pp , 998 [7] MP Fanti, A Giua, C Seatzu, A deadlock prevention method for railway networks using monitors for colored Petri nets, 3 IEEE Int Conf on Systems, Man and Cybernetics, Washington, USA, October 3 [8] A Giua, C Seatzu, Liveness enforcing supervisors for railway networks using ES PR Petri nets, Int Workshop on Discrete Event Systems, WODES, Zaragoza, Spain, October [9] F Harary, RZ Norman, D Cartwright, Structural models: an introduction to the theory of directed graphs, John Wiley & Sons, Inc New York, 965 [] K Jensen, Colored Petri nets: basic concepts, analysis methods and practical use, Vol, New York Springer, 99 [] M Missikoff, An object-oriented approach to an information and decision support systems for railway traffic control, Emerging Application of Artificial Intelligence, Vol, pp 5-4, 998 [] EAG Weits, Simulation of railway traffic control, Int Trans Operational Research, Vol 5, No 6, pp , 998
NONBLOCKING CONTROL OF PETRI NETS USING UNFOLDING. Alessandro Giua Xiaolan Xie
NONBLOCKING CONTROL OF PETRI NETS USING UNFOLDING Alessandro Giua Xiaolan Xie Dip. Ing. Elettrica ed Elettronica, U. di Cagliari, Italy. Email: giua@diee.unica.it INRIA/MACSI Team, ISGMP, U. de Metz, France.
More informationModelling of Railway Network Using Petri Nets
Modelling of Railway Network Using Petri Nets MANDIRA BANIK 1, RANJAN DASGUPTA 2 1 Dept. of Computer Sc. & Engg., National Institute of Technical Teachers' Training & Research, Kolkata, West Bengal, India
More informationCONTROL AND DEADLOCK RECOVERY OF TIMED PETRI NETS USING OBSERVERS
5 e Conférence Francophone de MOdélisation et SIMulation Modélisation et simulation pour l analyse et l optimisation des systèmes industriels et logistiques MOSIM 04 du 1 er au 3 septembre 2004 - Nantes
More informationLiveness enforcing supervisors for railway networks using ES 2 PR Petri nets
Liveness enforcing supervisors for railway networks using ES 2 PR Petri nets Alessandro Giua, Carla Seatzu Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d Armi 923
More informationLinear programming techniques for analysis and control of batches Petri nets
Linear programming techniques for analysis and control of batches Petri nets Isabel Demongodin, LSIS, Univ. of Aix-Marseille, France (isabel.demongodin@lsis.org) Alessandro Giua DIEE, Univ. of Cagliari,
More informationOPTIMAL TOKEN ALLOCATION IN TIMED CYCLIC EVENT GRAPHS
OPTIMAL TOKEN ALLOCATION IN TIMED CYCLIC EVENT GRAPHS Alessandro Giua, Aldo Piccaluga, Carla Seatzu Department of Electrical and Electronic Engineering, University of Cagliari, Italy giua@diee.unica.it
More informationPetri Net Modeling of Irrigation Canal Networks
Petri Net Modeling of Irrigation Canal Networks Giorgio Corriga, Alessandro Giua, Giampaolo Usai DIEE: Dip. di Ingegneria Elettrica ed Elettronica Università di Cagliari P.zza d Armi 09123 CAGLIARI, Italy
More informationA Deadlock Prevention Policy for Flexible Manufacturing Systems Using Siphons
Proceedings of the 2001 IEEE International Conference on Robotics & Automation Seoul, Korea May 21-26, 2001 A Deadlock Prevention Policy for Flexible Manufacturing Systems Using Siphons YiSheng Huang 1
More informationElementary Siphons of Petri Nets and Deadlock Control in FMS
Journal of Computer and Communications, 2015, 3, 1-12 Published Online July 2015 in SciRes. http://www.scirp.org/journal/jcc http://dx.doi.org/10.4236/jcc.2015.37001 Elementary Siphons of Petri Nets and
More informationHYPENS Manual. Fausto Sessego, Alessandro Giua, Carla Seatzu. February 7, 2008
HYPENS Manual Fausto Sessego, Alessandro Giua, Carla Seatzu February 7, 28 HYPENS is an open source tool to simulate timed discrete, continuous and hybrid Petri nets. It has been developed in Matlab to
More informationResource-Oriented Petri Nets in Deadlock Avoidance of AGV Systems
Proceedings of the 2001 IEEE International Conference on Robotics & Automation Seoul, Korea May 21-26, 2001 Resource-Oriented Petri Nets in Deadlock Avoidance of AGV Systems Naiqi Wu Department of Mechatronics
More informationControl of Hybrid Petri Nets using Max-Plus Algebra
Control of Hybrid Petri Nets using Max-Plus Algebra FABIO BALDUZZI*, ANGELA DI FEBBRARO*, ALESSANDRO GIUA, SIMONA SACONE^ *Dipartimento di Automatica e Informatica Politecnico di Torino Corso Duca degli
More informationDecidability of Single Rate Hybrid Petri Nets
Decidability of Single Rate Hybrid Petri Nets Carla Seatzu, Angela Di Febbraro, Fabio Balduzzi, Alessandro Giua Dip. di Ing. Elettrica ed Elettronica, Università di Cagliari, Italy email: {giua,seatzu}@diee.unica.it.
More informationONE NOVEL COMPUTATIONALLY IMPROVED OPTIMAL CONTROL POLICY FOR DEADLOCK PROBLEMS OF FLEXIBLE MANUFACTURING SYSTEMS USING PETRI NETS
Proceedings of the IASTED International Conference Modelling, Identification and Control (AsiaMIC 2013) April 10-12, 2013 Phuket, Thailand ONE NOVEL COMPUTATIONALLY IMPROVED OPTIMAL CONTROL POLICY FOR
More informationA REACHABLE THROUGHPUT UPPER BOUND FOR LIVE AND SAFE FREE CHOICE NETS VIA T-INVARIANTS
A REACHABLE THROUGHPUT UPPER BOUND FOR LIVE AND SAFE FREE CHOICE NETS VIA T-INVARIANTS Francesco Basile, Ciro Carbone, Pasquale Chiacchio Dipartimento di Ingegneria Elettrica e dell Informazione, Università
More informationBasis Marking Representation of Petri Net Reachability Spaces and Its Application to the Reachability Problem
Basis Marking Representation of Petri Net Reachability Spaces and Its Application to the Reachability Problem Ziyue Ma, Yin Tong, Zhiwu Li, and Alessandro Giua June, 017 Abstract In this paper a compact
More informationTime and Timed Petri Nets
Time and Timed Petri Nets Serge Haddad LSV ENS Cachan & CNRS & INRIA haddad@lsv.ens-cachan.fr DISC 11, June 9th 2011 1 Time and Petri Nets 2 Timed Models 3 Expressiveness 4 Analysis 1/36 Outline 1 Time
More informationA reachability graph partitioning technique for the analysis of deadlock prevention methods in bounded Petri nets
2010 American Control Conference Marriott Waterfront, Baltimore, M, USA June 30-July 02, 2010 ThB07.3 A reachability graph partitioning technique for the analysis of deadlock prevention methods in bounded
More informationTHE simulation of a continuous or discrete time system
770 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 28, NO. 6, DECEMBER 1998 Discrete Event Representation of Qualitative Models Using Petri Nets Alessandra Fanni, Member,
More informationTime(d) Petri Net. Serge Haddad. Petri Nets 2016, June 20th LSV ENS Cachan, Université Paris-Saclay & CNRS & INRIA
Time(d) Petri Net Serge Haddad LSV ENS Cachan, Université Paris-Saclay & CNRS & INRIA haddad@lsv.ens-cachan.fr Petri Nets 2016, June 20th 2016 1 Time and Petri Nets 2 Time Petri Net: Syntax and Semantic
More informationMarking Estimation in Labelled Petri nets by the Representative Marking Graph
DOI: 10.1109/XXXXXXXXXXXXXXXX. Marking Estimation in Labelled Petri nets by the Representative Marking Graph Ziyue Ma, Yin Tong, Zhiwu Li, and Alessandro Giua July 2017 Abstract In this paper a method
More informationNECESSARY AND SUFFICIENT CONDITIONS FOR DEADLOCKS IN FLEXIBLE MANUFACTURING SYSTEMS BASED ON A DIGRAPH MODEL
Asian Journal of Control, Vol. 6, No. 2, pp. 217-228, June 2004 217 NECESSARY AND SUFFICIENT CONDITIONS FOR DEADLOCKS IN FLEXIBLE MANUFACTURING SYSTEMS BASED ON A DIGRAPH MODEL Wenle Zhang, Robert P. Judd,
More informationStructural Analysis of Resource Allocation Systems with Synchronization Constraints
Structural Analysis of Resource Allocation Systems with Synchronization Constraints Spyros Reveliotis School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta, GA 30332 USA Abstract
More informationSupervisory Control of Petri Nets with. Uncontrollable/Unobservable Transitions. John O. Moody and Panos J. Antsaklis
Supervisory Control of Petri Nets with Uncontrollable/Unobservable Transitions John O. Moody and Panos J. Antsaklis Department of Electrical Engineering University of Notre Dame, Notre Dame, IN 46556 USA
More informationDES. 4. Petri Nets. Introduction. Different Classes of Petri Net. Petri net properties. Analysis of Petri net models
4. Petri Nets Introduction Different Classes of Petri Net Petri net properties Analysis of Petri net models 1 Petri Nets C.A Petri, TU Darmstadt, 1962 A mathematical and graphical modeling method. Describe
More informationIntuitionistic Fuzzy Estimation of the Ant Methodology
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 2 Sofia 2009 Intuitionistic Fuzzy Estimation of the Ant Methodology S Fidanova, P Marinov Institute of Parallel Processing,
More informationModeling Continuous Systems Using Modified Petri Nets Model
Journal of Modeling & Simulation in Electrical & Electronics Engineering (MSEEE) 9 Modeling Continuous Systems Using Modified Petri Nets Model Abbas Dideban, Alireza Ahangarani Farahani, and Mohammad Razavi
More informationPetri nets. s 1 s 2. s 3 s 4. directed arcs.
Petri nets Petri nets Petri nets are a basic model of parallel and distributed systems (named after Carl Adam Petri). The basic idea is to describe state changes in a system with transitions. @ @R s 1
More informationModeling and Control for (max, +)-Linear Systems with Set-Based Constraints
2015 IEEE International Conference on Automation Science and Engineering (CASE) Aug 24-28, 2015. Gothenburg, Sweden Modeling and Control for (max, +)-Linear Systems with Set-Based Constraints Xavier David-Henriet
More informationMethods for the specification and verification of business processes MPB (6 cfu, 295AA)
Methods for the specification and verification of business processes MPB (6 cfu, 295AA) Roberto Bruni http://www.di.unipi.it/~bruni 08 - Petri nets basics 1 Object Formalization of the basic concepts of
More informationGeorg Frey ANALYSIS OF PETRI NET BASED CONTROL ALGORITHMS
Georg Frey ANALYSIS OF PETRI NET BASED CONTROL ALGORITHMS Proceedings SDPS, Fifth World Conference on Integrated Design and Process Technologies, IEEE International Conference on Systems Integration, Dallas,
More informationDesigning Reversibility-Enforcing Supervisors of Polynomial Complexity for Bounded Petri Nets through the Theory of Regions
Designing Reversibility-Enforcing Supervisors of Polynomial Complexity for Bounded Petri Nets through the Theory of Regions Spyros A. Reveliotis 1 and Jin Young Choi 2 1 School of Industrial & Systems
More informationModeling and Stability Analysis of a Communication Network System
Modeling and Stability Analysis of a Communication Network System Zvi Retchkiman Königsberg Instituto Politecnico Nacional e-mail: mzvi@cic.ipn.mx Abstract In this work, the modeling and stability problem
More informationSub-Optimal Scheduling of a Flexible Batch Manufacturing System using an Integer Programming Solution
Sub-Optimal Scheduling of a Flexible Batch Manufacturing System using an Integer Programming Solution W. Weyerman, D. West, S. Warnick Information Dynamics and Intelligent Systems Group Department of Computer
More informationOn the number of cycles in a graph with restricted cycle lengths
On the number of cycles in a graph with restricted cycle lengths Dániel Gerbner, Balázs Keszegh, Cory Palmer, Balázs Patkós arxiv:1610.03476v1 [math.co] 11 Oct 2016 October 12, 2016 Abstract Let L be a
More informationc 2011 Nisha Somnath
c 2011 Nisha Somnath HIERARCHICAL SUPERVISORY CONTROL OF COMPLEX PETRI NETS BY NISHA SOMNATH THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Aerospace
More informationLoad balancing on networks with gossip-based distributed algorithms
1 Load balancing on networks with gossip-based distributed algorithms Mauro Franceschelli, Alessandro Giua, Carla Seatzu Abstract We study the distributed and decentralized load balancing problem on arbitrary
More informationAnalysis and Optimization of Discrete Event Systems using Petri Nets
Volume 113 No. 11 2017, 1 10 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Analysis and Optimization of Discrete Event Systems using Petri Nets
More informationFault Tolerance, State Estimation and Fault Diagnosis in Petri Net Models
Fault Tolerance, State Estimation and Fault Diagnosis in Petri Net Models Christoforos Hadjicostis Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign March 27,
More informationSynthesis of Controllers of Processes Modeled as Colored Petri Nets
Discrete Event Dynamic Systems: Theory and Applications, 9, 147 169 (1999) c 1999 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands. Synthesis of Controllers of Processes Modeled as Colored
More informationComplexity Analysis of Continuous Petri Nets
Fundamenta Informaticae 137 (2015) 1 28 1 DOI 10.3233/FI-2015-1167 IOS Press Complexity Analysis of Continuous Petri Nets Estíbaliz Fraca Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad
More informationChapter 3: Discrete Optimization Integer Programming
Chapter 3: Discrete Optimization Integer Programming Edoardo Amaldi DEIB Politecnico di Milano edoardo.amaldi@polimi.it Website: http://home.deib.polimi.it/amaldi/opt-16-17.shtml Academic year 2016-17
More informationLoad balancing on networks with gossip-based distributed algorithms
Load balancing on networks with gossip-based distributed algorithms Mauro Franceschelli, Alessandro Giua, Carla Seatzu Abstract We study the distributed and decentralized load balancing problem on arbitrary
More informationCycle Time Analysis for Wafer Revisiting Process in Scheduling of Single-arm Cluster Tools
International Journal of Automation and Computing 8(4), November 2011, 437-444 DOI: 10.1007/s11633-011-0601-5 Cycle Time Analysis for Wafer Revisiting Process in Scheduling of Single-arm Cluster Tools
More informationColoured Petri Nets Based Diagnosis on Causal Models
Coloured Petri Nets Based Diagnosis on Causal Models Soumia Mancer and Hammadi Bennoui Computer science department, LINFI Lab. University of Biskra, Algeria mancer.soumia@gmail.com, bennoui@gmail.com Abstract.
More informationOn the Design of Adaptive Supervisors for Discrete Event Systems
On the Design of Adaptive Supervisors for Discrete Event Systems Vigyan CHANDRA Department of Technology, Eastern Kentucky University Richmond, KY 40475, USA and Siddhartha BHATTACHARYYA Division of Computer
More informationCycle time optimization of deterministic timed weighted marked graphs by transformation
Cycle time optimization of deterministic timed weighted marked graphs by transformation Zhou He, Zhiwu Li, Alessandro Giua, Abstract Timed marked graphs, a special class of Petri nets, are extensively
More informationMinimum cost transportation problem
Minimum cost transportation problem Complements of Operations Research Giovanni Righini Università degli Studi di Milano Definitions The minimum cost transportation problem is a special case of the minimum
More informationEfficient Deadlock Prevention in Petri Nets through the Generation of Selected Siphons
2009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 10-12, 2009 FrB12.3 Efficient Deadlock Prevention in Petri Nets through the Generation of Selected Siphons Luigi Piroddi,
More informationDecentralized Stabilization of Heterogeneous Linear Multi-Agent Systems
1 Decentralized Stabilization of Heterogeneous Linear Multi-Agent Systems Mauro Franceschelli, Andrea Gasparri, Alessandro Giua, and Giovanni Ulivi Abstract In this paper the formation stabilization problem
More informationChapter 3: Discrete Optimization Integer Programming
Chapter 3: Discrete Optimization Integer Programming Edoardo Amaldi DEIB Politecnico di Milano edoardo.amaldi@polimi.it Sito web: http://home.deib.polimi.it/amaldi/ott-13-14.shtml A.A. 2013-14 Edoardo
More informationCourse : Algebraic Combinatorics
Course 18.312: Algebraic Combinatorics Lecture Notes #29-31 Addendum by Gregg Musiker April 24th - 29th, 2009 The following material can be found in several sources including Sections 14.9 14.13 of Algebraic
More informationDECOMPOSITION OF PETRI NETS
Cybernetics and Systems Analysis, Vol. 40, No. 5, 2004 DECOMPOSITION OF PETRI NETS D. A. Zaitsev UDC 519.74 The problem of splitting any given Petri net into functional subnets is considered. The properties
More informationMonomial subdigraphs of reachable and controllable positive discrete-time systems
Monomial subdigraphs of reachable and controllable positive discrete-time systems Rafael Bru a, Louis Caccetta b and Ventsi G. Rumchev b a Dept. de Matemàtica Aplicada, Univ. Politècnica de València, Camí
More informationSynchronizing sequences. on a class of unbounded systems using synchronized Petri nets
Synchronizing sequences 1 on a class of unbounded systems using synchronized Petri nets Marco Pocci, Isabel Demongodin, Norbert Giambiasi, Alessandro Giua Abstract Determining the state of a system when
More informationAlgorithms for a Special Class of State-Dependent Shortest Path Problems with an Application to the Train Routing Problem
Algorithms fo Special Class of State-Dependent Shortest Path Problems with an Application to the Train Routing Problem Lunce Fu and Maged Dessouky Daniel J. Epstein Department of Industrial & Systems Engineering
More informationPerformance Control of Markovian Petri Nets via Fluid Models: A Stock-Level Control Example
Performance Control of Markovian Petri Nets via Fluid Models: A Stock-Level Control Example C. Renato Vázquez Manuel Silva Abstract Petri nets is a well-know formalism for studying discrete event systems.
More informationIN AN industrial automated manufacturing system (AMS),
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, VOL. 45, NO. 11, NOVEMBER 2015 1459 Lexicographic Multiobjective Integer Programming for Optimal and Structurally Minimal Petri Net Supervisors
More information748 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 54, NO. 4, APRIL 2009
748 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 54, NO 4, APRIL 2009 An Efficient Approach for Online Diagnosis of Discrete Event Systems Francesco Basile, Member, IEEE, Pasquale Chiacchio, Gianmaria De
More informationAn Efficient Heuristics for Minimum Time Control of Continuous Petri nets
An Efficient Heuristics for Minimum Time Control of Continuous Petri nets Hanife Apaydin-Özkan, Jorge Júlvez, Cristian Mahulea, Manuel Silva Instituto de Investigación en Ingeniería de Aragon (I3A), Universidad
More informationA Master-Slave Algorithm for the Optimal Control of Continuous-Time Switched Affine Systems
A Master-Slave Algorithm for the Optimal Control of Continuous-Time Switched Affine Systems Alberto Bemporad Dip. Ing. dell Informazione, Università di Siena Via Roma 56, 53 Siena, Italy bemporad@dii.unisi.it
More informationOn max-algebraic models for transportation networks
K.U.Leuven Department of Electrical Engineering (ESAT) SISTA Technical report 98-00 On max-algebraic models for transportation networks R. de Vries, B. De Schutter, and B. De Moor If you want to cite this
More informationc 2014 Vijayalakshmi Deverakonda
c 214 Vijayalakshmi Deverakonda DISJUNCTIVE NORMAL FORMULA BASED SUPERVISORY CONTROL POLICY FOR GENERAL PETRI NETS BY VIJAYALAKSHMI DEVERAKONDA THESIS Submitted in partial fulfillment of the requirements
More informationInventory optimization of distribution networks with discrete-event processes by vendor-managed policies
Inventory optimization of distribution networks with discrete-event processes by vendor-managed policies Simona Sacone and Silvia Siri Department of Communications, Computer and Systems Science University
More informationThe matrix approach for abstract argumentation frameworks
The matrix approach for abstract argumentation frameworks Claudette CAYROL, Yuming XU IRIT Report RR- -2015-01- -FR February 2015 Abstract The matrices and the operation of dual interchange are introduced
More informationDESIGN OF LIVENESS-ENFORCING SUPERVISORS VIA TRANSFORMING PLANT PETRI NET MODELS OF FMS
Asian Journal of Control, Vol. 1, No., pp. 0, May 010 Published online February 010 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.100/asjc.18 DESIGN OF LIVENESS-ENFORCING SUPERVISORS VIA TRANSFORMING
More informationResearch Article Comparison and Evaluation of Deadlock Prevention Methods for Different Size Automated Manufacturing Systems
Mathematical Problems in Engineering Volume 2015, Article ID 537893, 19 pages http://dx.doi.org/10.1155/2015/537893 Research Article Comparison and Evaluation of Deadlock Prevention Methods for Different
More informationOut-colourings of Digraphs
Out-colourings of Digraphs N. Alon J. Bang-Jensen S. Bessy July 13, 2017 Abstract We study vertex colourings of digraphs so that no out-neighbourhood is monochromatic and call such a colouring an out-colouring.
More informationSimulation of Spiking Neural P Systems using Pnet Lab
Simulation of Spiking Neural P Systems using Pnet Lab Venkata Padmavati Metta Bhilai Institute of Technology, Durg vmetta@gmail.com Kamala Krithivasan Indian Institute of Technology, Madras kamala@iitm.ac.in
More informationA Polynomial-Time Algorithm for Checking Consistency of Free-Choice Signal Transition Graphs
Fundamenta Informaticae XX (2004) 1 23 1 IOS Press A Polynomial-Time Algorithm for Checking Consistency of Free-Choice Signal Transition Graphs Javier Esparza Institute for Formal Methods in Computer Science
More informationZero controllability in discrete-time structured systems
1 Zero controllability in discrete-time structured systems Jacob van der Woude arxiv:173.8394v1 [math.oc] 24 Mar 217 Abstract In this paper we consider complex dynamical networks modeled by means of state
More informationT -choosability in graphs
T -choosability in graphs Noga Alon 1 Department of Mathematics, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. and Ayal Zaks 2 Department of Statistics and
More informationarxiv: v1 [cs.dc] 3 Oct 2011
A Taxonomy of aemons in Self-Stabilization Swan ubois Sébastien Tixeuil arxiv:1110.0334v1 cs.c] 3 Oct 2011 Abstract We survey existing scheduling hypotheses made in the literature in self-stabilization,
More informationMODELING AND SIMULATION BY HYBRID PETRI NETS. systems, communication systems, etc). Continuous Petri nets (in which the markings are real
Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds. MODELING AND SIMULATION BY HYBRID PETRI NETS Hassane Alla Latéfa Ghomri
More informationComputers and Mathematics with Applications. Module-based architecture for a periodic job-shop scheduling problem
Computers and Mathematics with Applications 64 (2012) 1 10 Contents lists available at SciVerse ScienceDirect Computers and Mathematics with Applications journal homepage: www.elsevier.com/locate/camwa
More informationApplications of Petri Nets
Applications of Petri Nets Presenter: Chung-Wei Lin 2010.10.28 Outline Revisiting Petri Nets Application 1: Software Syntheses Theory and Algorithm Application 2: Biological Networks Comprehensive Introduction
More informationTermination Problem of the APO Algorithm
Termination Problem of the APO Algorithm Tal Grinshpoun, Moshe Zazon, Maxim Binshtok, and Amnon Meisels Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva, Israel Abstract. Asynchronous
More informationMethods for the specification and verification of business processes MPB (6 cfu, 295AA)
Methods for the specification and verification of business processes MPB (6 cfu, 295AA) Roberto Bruni http://www.di.unipi.it/~bruni 17 - Diagnosis for WF nets 1 Object We study suitable diagnosis techniques
More informationEmbedded Systems 6 REVIEW. Place/transition nets. defaults: K = ω W = 1
Embedded Systems 6-1 - Place/transition nets REVIEW Def.: (P, T, F, K, W, M 0 ) is called a place/transition net (P/T net) iff 1. N=(P,T,F) is a net with places p P and transitions t T 2. K: P (N 0 {ω})
More informationfakultät für informatik informatik 12 technische universität dortmund Petri nets Peter Marwedel Informatik 12 TU Dortmund Germany
12 Petri nets Peter Marwedel Informatik 12 TU Dortmund Germany Introduction Introduced in 1962 by Carl Adam Petri in his PhD thesis. Focus on modeling causal dependencies; no global synchronization assumed
More information7. Queueing Systems. 8. Petri nets vs. State Automata
Petri Nets 1. Finite State Automata 2. Petri net notation and definition (no dynamics) 3. Introducing State: Petri net marking 4. Petri net dynamics 5. Capacity Constrained Petri nets 6. Petri net models
More informationRao s degree sequence conjecture
Rao s degree sequence conjecture Maria Chudnovsky 1 Columbia University, New York, NY 10027 Paul Seymour 2 Princeton University, Princeton, NJ 08544 July 31, 2009; revised December 10, 2013 1 Supported
More informationTHROUGHPUT ANALYSIS OF MANUFACTURING CELLS USING TIMED PETRI NETS
c 1994 IEEE. Published in the Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, October 2 5, 1994. Personal use of this material is permitted. However,
More informationAnalysis of real-time system conflict based on fuzzy time Petri nets
Journal of Intelligent & Fuzzy Systems 26 (2014) 983 991 DOI:10.3233/IFS-130789 IOS Press 983 Analysis of real-time system conflict based on fuzzy time Petri nets Zhao Tian a,, Zun-Dong Zhang a,b, Yang-Dong
More informationNEW COLOURED REDUCTIONS FOR SOFTWARE VALIDATION. Sami Evangelista Serge Haddad Jean-François Pradat-Peyre
NEW COLOURED REDUCTIONS FOR SOFTWARE VALIDATION Sami Evangelista Serge Haddad Jean-François Pradat-Peyre CEDRIC-CNAM Paris 292, rue St Martin, 75003 Paris LAMSADE-CNRS UMR 7024 Université Paris 9 Place
More informationThe Multi-Agent Rendezvous Problem - The Asynchronous Case
43rd IEEE Conference on Decision and Control December 14-17, 2004 Atlantis, Paradise Island, Bahamas WeB03.3 The Multi-Agent Rendezvous Problem - The Asynchronous Case J. Lin and A.S. Morse Yale University
More informationMin-Max Message Passing and Local Consistency in Constraint Networks
Min-Max Message Passing and Local Consistency in Constraint Networks Hong Xu, T. K. Satish Kumar, and Sven Koenig University of Southern California, Los Angeles, CA 90089, USA hongx@usc.edu tkskwork@gmail.com
More informationGRAPH ALGORITHMS Week 7 (13 Nov - 18 Nov 2017)
GRAPH ALGORITHMS Week 7 (13 Nov - 18 Nov 2017) C. Croitoru croitoru@info.uaic.ro FII November 12, 2017 1 / 33 OUTLINE Matchings Analytical Formulation of the Maximum Matching Problem Perfect Matchings
More informationEnumeration Schemes for Words Avoiding Permutations
Enumeration Schemes for Words Avoiding Permutations Lara Pudwell November 27, 2007 Abstract The enumeration of permutation classes has been accomplished with a variety of techniques. One wide-reaching
More informationAn Holistic State Equation for Timed Petri Nets
An Holistic State Equation for Timed Petri Nets Matthias Werner, Louchka Popova-Zeugmann, Mario Haustein, and E. Pelz 3 Professur Betriebssysteme, Technische Universität Chemnitz Institut für Informatik,
More informationA Siphon-based Deadlock Prevention Policy for a Class of Petri Nets - S 3 PMR
Proceedings of the 17th World Congress The International Federation of Automatic Control A Siphon-based eadlock Prevention Policy for a Class of Petri Nets - S 3 PMR Mingming Yan, Rongming Zhu, Zhiwu Li
More informationChapter 29 out of 37 from Discrete Mathematics for Neophytes: Number Theory, Probability, Algorithms, and Other Stuff by J. M.
29 Markov Chains Definition of a Markov Chain Markov chains are one of the most fun tools of probability; they give a lot of power for very little effort. We will restrict ourselves to finite Markov chains.
More informationOn Controllability of Timed Continuous Petri Nets
On Controllability of Timed Continuous Petri Nets C Renato Vázquez 1, Antonio Ramírez 2, Laura Recalde 1, and Manuel Silva 1 1 Dep de Informática e Ingeniería de Sistemas, Centro Politécnico Superior,
More informationA Mixed Integer Linear Program for Optimizing the Utilization of Locomotives with Maintenance Constraints
A Mixed Integer Linear Program for with Maintenance Constraints Sarah Frisch Philipp Hungerländer Anna Jellen Dominic Weinberger September 10, 2018 Abstract In this paper we investigate the Locomotive
More information5 Flows and cuts in digraphs
5 Flows and cuts in digraphs Recall that a digraph or network is a pair G = (V, E) where V is a set and E is a multiset of ordered pairs of elements of V, which we refer to as arcs. Note that two vertices
More informationarxiv: v1 [math.oc] 21 Feb 2018
Noname manuscript No. (will be inserted by the editor) On detectability of labeled Petri nets with inhibitor arcs Kuize Zhang Alessandro Giua arxiv:1802.07551v1 [math.oc] 21 Feb 2018 Received: date / Accepted:
More informationDirect mapping of low-latency asynchronous
School of Electrical, Electronic & Computer Engineering Direct mapping of low-latency asynchronous controllers from STGs D.Sokolov, A.Bystrov, A.Yakovlev Technical Report Series NCL-EECE-MSD-TR-2006-110
More informationOn the complexity of maximally permissive deadlock avoidance in multi-vehicle traffic systems
1 On the complexity of maximally permissive deadlock avoidance in multi-vehicle traffic systems Spyros A. Reveliotis and Elzbieta Roszkowska Abstract The establishment of collision-free and live vehicle
More informationStochastic Petri Net
Stochastic Petri Net Serge Haddad LSV ENS Cachan & CNRS & INRIA haddad@lsv.ens-cachan.fr Petri Nets 2013, June 24th 2013 1 Stochastic Petri Net 2 Markov Chain 3 Markovian Stochastic Petri Net 4 Generalized
More informationDecentralized Control of Discrete Event Systems with Bounded or Unbounded Delay Communication
Decentralized Control of Discrete Event Systems with Bounded or Unbounded Delay Communication Stavros Tripakis Abstract We introduce problems of decentralized control with communication, where we explicitly
More information