The marking construction problem (MCP) of Petri nets is defined as follows: “Given a Petri net N, an initial marking Mi and a target marking Mt, construct a marking that is closest to Mt among those which can be reached from Mi by firing transitions.” MCP includes the well-known marking reachability problem of Petri nets. MCP is known to be NP-hard, and we propose two schemas of heuristic algorithms: (i) not using any algorithm for the maximum legal firing sequence problem (MAX LFS) or (ii) using an algorithm for MAX LFS. Moreover, this paper proposes four pseudo-polynomial time algorithms: MCG and MCA for (i), and MCHFk and MC_feideq_a for (ii), where MCA (MC_feideq_a, respectively) is an improved version of MCG (MCHFk). Their performance is evaluated through results of computing experiment.

The 2-vertex-connectivity augmentation problem for a specified set of vertices of a graph with degree constraints, 2VCA-SV-DC, is defined as follows: "Given an undirected graph G = (V,E), a specified set of vertices S ⊆V with |S|3 and a function g:V→Z+∪{∞}, find a smallest set E' of edges such that (V,E ∪E') has at least two internally-disjoint paths between any pair of vertices in S and such that vertex-degree increase of each v ∈V by the addition of E' to G is at most g(v), where Z+ is the set of nonnegative integers." This paper shows a linear time algorithm for 2VCA-SV-DC.

The k-edge-connectivity augmentation problem for a specified set of vertices (kECA-SV for short) is defined by “Given a graph G=(V, E) and a subset Γ ⊆ V, find a minimum set E' of edges such that G'=(V, E ∪ E') has at least k edge-disjoint paths between any pair of vertices in Γ.” Let σ be the edge-connectivity of Γ (that is, G has at least σ edge-disjoint paths between any pair of vertices in Γ). We propose an algorithm for (σ+1)ECA-SV which is done in O(|Γ|) maximum flow operations. Then the time complexity is O(σ2|Γ||V|+|E|) if a given graph is sparse, or O(|Γ||V||BG|log(|V|2/|BG|)+|E|) if dense, where |BG| is the number of pairs of adjacent vertices in G. Also mentioned is an O(|V||E|+|V|2 log |V|) time algorithm for a special case where σ is equal to the edge-connectivity of G and an O(|V|+|E|) time one for σ ≤ 2.

The subject of the paper is to propose a simple O(|V|+|E|) algorithm for finding all 3-edge-components of a given undirected multigraph G=(V, E). An 3-edge-connected component of G is defined as a maximal set of vertices such that G has at least three edge-disjoint paths between every pair of vertices in the set. The algorithm is based on the depth-first search (DFS) technique. For any fixed DFS-tree T of G, cutpairs of G are partitioned into two types: a type 1 pair consists of an edge of T and a back edge; a type 2 pair consists of two edges of T. All type 1 pairs can easily be determined in O(|V|+|E|) time. The point is that an edge set KE(T) in which any type 2 pair is included can be found in O(|V|+|E|) time. All 3-edge-components of G appear as connected components if we delete from G all edges contained in type 1 pairs or in the edge set KE(T).

We consider only P-invariants that are nonnegative integer vectors in this paper. An P-invariant of a Petri net N=(P,T,E,α,β) is a |P|-dimensional vector Y with Yt A = for the place-transition incidence matrix A of N. The support of an invariant is the set of elements having nonzero values in the vector. Since any invariant is expressed as a linear combination of minimal-support invariants (ms-invariants for short) with nonnegative rational coefficients, it is usual to try to obtain either several invariants or the set of all ms-invariants. The Fourier-Motzkin method (FM) is well-known for computing a set of invariants including all ms-invariants. It has, however, critical deficiencies such that, even if invariants exist, none of them may be computed because of memory overflow caused by storing candidate vectors for invariants and such that, even when a set of invariants are produced, many non-ms invariants may be included. We are going to propose the following two methods: (1) FM1_M2 that finds a smallest possible set of invariants including all ms-invariants; (2) STFM that necessarily produces one or more invariants if they exist. Experimental results are given to show their superiority over existing ones.

Invariants of Petri nets are fundamental algebraic characteristics of Petri nets, and are used in various situations, such as checking (as necessity of) liveness, boundedness, periodicity and so on. Any given Petri net N has two kinds of invariants: a P-invariant is a |P|-dimensional vector Y with Yt A = and a T-invariant is a |T|-dimensional vector X with A X = for the place-transition incidence matrix A of N. T-invariants are nonnegative integer vectors, while this is not always the case with P-invariants. This paper deals only with nonnegative integer invariants (invariants that are nonnegative vectors) and shows results common to the two invariants. For simplicity of discussion, only P-invariants are treated. The Fourier-Motzkin method is well-known for computing all minimal support integer invariants. This method, however, has a critical deficiency such that, even if a given Perti net N has any invariant, it is likely that no invariants are obtained because of an overflow in storing intermediate vectors as candidates for invariants. The subject of the paper is to give an overview and results known to us for efficiently computing minimal-support nonnegative integer invariants of a given Petri net by means of the Fourier-Motzkin method. Also included are algorithms for efficiently extracting siphon-traps of a Petri net.

The minimum initial marking problem of Petri nets (MIM) is defined as follows: "Given a Petri net and a firing count vector X, find an initial marking M0, with the minimum total token number, for which there is a sequence δ of transitions such that each transition t appears exactly X(t) times in δ, the first transition is enabled at M0 and the rest can be fired one by one subsequently." In a production system like factory automation, economical distribution of initial resources, from which a schedule of job-processings is executable, can be formulated as MIM. AAD is known to produce best solutions among existing algorithms. Although solutions by AMIM+ is worse than those by AAD, it is known that AMIM+ is very fast. This paper proposes new heuristic algorithms AADO and AMDLO, improved versions of existing algorithms AAD and AMIM+, respectively. Sharpness of solutions or short CPU time is the main target of AADO or AMDLO, respectively. It is shown, based on computing experiment, that the average total number of tokens in initial markings by AADO is about 5.15% less than that by AAD, and the average CPU time by AADO is about 17.3% of that by AAD. AMDLO produces solutions that are slightly worse than those by AAD, while they are about 10.4% better than those by AMIM+. Although CPU time of AMDLO is about 180 times that of AMIM+, it is still fast: average CPU time of AMDLO is about 2.33% of that of AAD. Generally it is observed that solutions get worse as the sizes of input instances increase, and this is the case with AAD and AMIM+. This undesirable tendency is greatly improved in AADO and AMDLO.

The subject of this paper is maximum weight matchings of graphs. An edge set M of a given graph G is called a matching if and only if any pair of edges in M share no endvertices. A maximum weight matching is a matching whose total weight (total sum of edge-weights) is maximum among those of G. The maximum weight matching problem (MWM for short) is to find a maximum weight matching of a given graph. Polynomial algorithms for finding an optimum solution to MWM have already been proposed: for example, an O(|V|4) time algorithm proposed by J. Edmonds, and an O(|E||V|log |V|) time algorithm proposed by H.N. Gabow. Some applications require obtaining a matching of large total weight (not necessarily a maximum one) in realistic computing time. These existing algorithms, however, spend extremely long computing time as the size of a given graph becomes large, and several fast approximation algorithms for MWM have been proposed. In this paper, we propose six approximation algorithms GRS+, GRS_F+, GRS_R+, GRS_S+, LAM_a+ and LAM_as+. They are enhanced from known approximation ones by adding some postprocessings that consist of improved search of weight augmenting paths. Their performance is evaluated through results of computing experiment.

The k-edge-connectivity augmentation problem with bipartition constraints (kECABP, for short) is defined by “Given an undirected graph G=(V, E) and a bipartition π = {VB, VW} of V with VB ∩ VW = ∅, find an edge set Ef of minimum cardinality, consisting of edges that connect VB and VW, such that G'=(V, E ∪ Ef) is k-edge-connected.” The problem has applications for security of statistical data stored in a cross tabulated table, and so on. In this paper we propose a fast algorithm for finding an optimal solution to (σ + 1)ECABP in O(|V||E| + |V2|log |V|) time when G is σ-edge-connected (σ > 0), and show that the problem can be solved in linear time if σ ∈ {1, 2}.

This paper proposes a new heuristic algorithm FMDB for the minimum initial marking problem MIM of Petri nets: "Given a Petri net and a firing count vector X, find an initial marking M0, with the minimum total token number, for which there is a sequence δ of transitions such that each transition t appears exactly X(t) times in δ, the first transition is firable on M0 and the rest can be fired one by one subsequently. " Experimental results show that FMDB produces better solutions than any known algorithm.

An edge-weighted directed graph is referred to as a network in this paper, and an edge operation is an operation that increases or decreases an edge weight. Decreasing an edge weight from the infinite to a finite value or increasing any edge weight from a finite one to the infinite corresponds to addition or deletion of this edge, respectively. The dynamic shortest path problem (DSPP for short) is defined by "Given any network with a specified vertex (denoted as s), and any sequence of edge operations, construct a shortest path tree of each network obtained by executing those edge operations one by one in the order of the sequence." As an application, fast routing for an interior network using link state protocols, such as OSPF and IS-IS, requires solving DSPP efficiently. In this paper, among as many existing algorithms as possible, including those which execute several edge operations simultaneously, fundamental and/or important algorithms are implemented and their capability is evaluated based on the results of computational experiments.

A branch-and-bound algorithm (BB for short) is the most general technique to deal with various combinatorial optimization problems. Even if it is used, computation time is likely to increase exponentially. So we consider its parallelization to reduce it. It has been reported that the computation time of a parallel BB heavily depends upon node-variable selection strategies. And, in case of a parallel BB, it is also necessary to prevent increase in communication time. So, it is important to pay attention to how many and what kind of nodes are to be transferred (called sending-node selection strategy). In this paper, for the graph coloring problem, we propose some sending-node selection strategies for a parallel BB algorithm by adopting MPI for parallelization and experimentally evaluate how these strategies affect computation time of a parallel BB on a PC cluster network.

A siphon-trap of a Petri net N is defined as a place set S with S = S, where S = { u| N has an edge from u to a vertex of S} and S = { v| N has an edge from a vertex of S to v}. A minimal siphon-trap is a siphon-trap such that any proper subset is not a siphon-trap. The following polynomial-time algorithms are proposed: (1) FDST for finding, if any, a minimal siphon-trap or even a maximal class of mutually disjoint minimal siphon-traps of a given Petri net; (2) FDSTi that repeats FDST i times in order to extract more minimal siphon-traps than FDST. (3) STFM_T (STFM_Ti, respectively) which is a combination of the Fourier-Motzkin method and FDST (FDSTi) and which has high possibility of finding, if any, at least one minimal-support nonnegative integer invariant.

The legal firing sequence problem (LFS) asks if it is possible to fire each transition some prescribed number of times in a given Petri net. It is a fundamental problem in Petri net theory as it appears as a subproblem, or as a simplified version of marking reachability, minimum initial resource allocation, liveness, and some scheduling problems. It is also known to be NP-hard, however, even under various restrictions on nets (and on firing counts), and no efficient algorithm has been previously reported for any class of nets having general edge weights. We show in this paper that LFS can be solved in polynomial time (in O(n log n) time) for a subclass of state machines, called cacti, with arbitrary edge weights allowed (if each transition is asked to be fired exactly once).

The (k + δ)-edge-connectivity augmentation problem for a specified set of vertices ((k + δ)ECA-SV) is defined as follows: "Given an undirected graph G =(V,E), a specified set of vertices Γ V, a subgraph G ′=(V,E ′) with λ(Γ;G ′) = k of G and a cost function c: E Z+ (nonnegative integers), find a set E* E - E ′of edges, each connecting distinct vertices of V, of minimum total cost such that λ(Γ;G″) k + δ for G"=(V,E ′∪E*)," where λ(Γ;G″) is the minimum value of the maximum number of edge disjoint paths between any pair of vertices in Γ of G". The paper proposes an O(Δ+|V||E|) time 2-approximation algorithm FSAR for (k + 1)ECA-SV with a restriction λ(V;G ′) = λ(Γ;G ′), where Δ is the time complexity of constructing a structural graph of a given graph G ′.

A vertex cover of a given graph G = (V,E) is a subset N of V such that N contains either u or v for any edge (u,v) of E. The minimum weight vertex cover problem (MWVC for short) is the problem of finding a vertex cover N of any given graph G = (V,E), with weight w(v) for each vertex v of V, such that the sum w(N) of w(v) over all v of N is minimum. In this paper, we consider MWVC with w(v) of any v of V being a positive integer. We propose simple procedures as postprocessing of algorithms for MWVC. Furthremore, five existing approximation algorithms with/without the proposed procedures incorporated are implemented, and they are evaluated through computing experiment.

Let G = (D ∪ S,E) be an undirected graph with a vertex set D ∪ S and an (undirected) edge set E, where the vertex set is partitioned into two subsets, a demand vertex set D and a supply vertex set S. We assume that D ≠ and S ≠ in this paper. Each demand or supply vertex v has a positive real number d(v) or s(v), called the demand or supply of v, respectively. For any subset V' ⊆ D ∪ S, the demand of V' is defined by d(V') = Σv∈V'∩Dd(v) if V' ∩ D ≠ or d(V') = 0 if V' ∩ D = . Let s(S) = Σv∈S s(v). Any partition π = {V1,..., Vr} (|S| r |D ∪ S|) of D ∪ S is called a feasible partition of G if and only if π satisfies the following (1) and (2) for any k = 1,..., r: (1) |Vk ∩ S|1, and (2) if Vk ∩ S = {uk} then the induced subgraph G[Vk] of G is connected and d(Vk)s(uk). The demand d(π) of π is defined by d(π)=d(Vk). The "Maximum-Supply Partitioning Problem (MSPP)" is to find a feasible partition π of G such that d(π) is maximum among all feasible partitions of G. We implemented not only existing algorithms for obtainity heuristic or optimum solutions to MSPP but also those that are corrected or improved from existing ones. In this paper we show comparison of their capability based on computational experiments.

The minimum initial marking problem MIM of Petri nets is described as follows: "Given a Petri net and a firing count vector X, find an initial marking M0, with the minimum total token number, for which there is a sequence δ of transitions such that each transition t appears exactly X(t) times in δ, the first transition is enabled at M0 and the rest can be fired one by one subsequently." This paper proposes two heuristic algorithms AAD and AMIM + and shows the following (1) and (2) through experimental results: (1) AAD is more capable than any other known algorithm; (2) AMIM + can produce M0, with a small number of tokens, even if other algorithms are too slow to compute M0 as the size of an input instance gets very large.

The k-edge-connectivity augmentation problem with multipartition constraints (kECAMP, for short) is defined by “Given a multigraph G=(V,E) and a multipartition π={V1,...,Vr} (r≥2) of V, that is, $V = igcup_{h = 1}^r V_h$ and Vi∩Vj=∅ (1≤i