Online graph exploration algorithms for cycles and trees by multiple searchers

This paper deals with online graph exploration problems by multiple searchers. The information on the graph is given online. As the exploration proceeds, searchers gain more information on the graph. Assuming an appropriate communication model among searchers, searchers can share the information about the environment. Thus, a searcher must decide which vertex to visit next based on the partial information on the graph gained so far by searchers. We assume that all searchers initially start the exploration at the origin vertex, and the goal is that each vertex is visited by at least one searcher and all searchers finally return to the origin vertex. The objective is to minimize the time when the goal is achieved. We study the case of cycles and trees. For the former, we give an optimal online exploration algorithm in terms of competitive ratio, and for the latter, we also give an online exploration algorithm which is optimal among greedy algorithms.     

Two Variations of the Minimum Steiner Problem

Given a set S of starting vertices and a set T of terminating vertices in a graph G = (V,E) with non-negative weights on edges, the minimum Steiner network problem is to find a subgraph of G with the minimum total edge weight. In such a subgraph, we require that for each vertex s S and t T, there is a path from s to a terminating vertex as well as a path from a starting vertex to t. This problem can easily be proven NP-hard. For solving the minimum Steiner network problem, we first present an algorithm that runs in time and space that both are polynomial in n with constant degrees, but exponential in |S|+|T|, where n is the number of vertices in G. Then we present an algorithm that uses space that is quadratic in n and runs in time that is polynomial in n with a degree O(max {max {|S|,|T|}–2,min {|S|,|T|}–1}). In spite of this degree, we prove that the number of Steiner vertices in our solution can be as large as |S|+|T|–2. Our algorithm can enumerate all possible optimal solutions. The input graph G can either be undirected or directed acyclic. We also give a linear time algorithm for the special case when min {|S|,|T|} = 1 and max {|S|,|T|} = 2.The minimum union paths problem is similar to the minimum Steiner network problem except that we are given a set H of hitting vertices in G in addition to the sets of starting and terminating vertices. We want to find a subgraph of G with the minimum total edge weight such that the conditions required by the minimum Steiner network problem are satisfied as well as the condition that every hitting vertex is on a path from a starting vertex to a terminating vertex. Furthermore, G must be directed acyclic. For solving the minimum union paths problem, we also present algorithms that have a time and space tradeoff similar to algorithms for the minimum Steiner network problem. We also give a linear time algorithm for the special case when |S| = 1, |T| = 1 and |H| = 2.An extended abstract of part of this paper appears in Hsu et al. (1996).Supported in part by the National Science Foundation under Grants CCR-9309743 and INT-9207212, and by the Office of Naval Research under Grant No. N00014-93-1-0272.Supported in part by the National Science Council, Taiwan, ROC, under Grant No. NSC-83-0408-E-001-021.     

Online scheduling with rejection and reordering: exact algorithms for unit size jobs

We study an online scheduling problem with rejection on \(m\ge 2\) identical machines, in which we deal with unit size jobs. Each arriving job has a rejection value (a rejection cost or penalty for minimization problems, and a rejection profit for maximization problems) associated with it. A buffer of size \(K\) is available to store \(K\) jobs. A job which is not stored in the buffer must be either assigned to a machine or rejected. Upon the arrival of a new job, the job can be stored in the buffer if there is a free slot (possibly created by evicting other jobs and assigning or rejecting every evicted job). At termination, the buffer must be emptied. We study four variants of the problem, as follows. We study the makespan minimization problem, where the goal is to minimize the sum of the makespan and the penalty of rejected jobs, and the \(\ell _p\) norm minimization problem, where the goal is to minimize the sum of the \(\ell _p\) norm of the vector of machine completion times and the penalty of rejected jobs. We also study two maximization problems, where the goal in the first version is to maximize the sum of the minimum machine load (the cover value of the machines) and the total rejection profit, and in the second version the goal is to maximize a function of the machine completion times (which measures the balance of machine loads) and the total rejection profit. We show that an optimal solution (an exact solution for the offline problem) can always be obtained in this environment, and determine the required buffer size. Specifically, for all four variants we present optimal algorithms with \(K=m-1\) and prove that in each case, using a buffer of size at most \(m-2\) does not allow the design of an optimal algorithm, which makes our algorithms optimal in this respect as well. The lower bounds hold even for the special case where the rejection value is equal for all input jobs.     

The hierarchical model for load balancing on two machines

Following previous work, we consider the hierarchical load balancing model on two machines of possibly different speeds. We first focus on maximizing the minimum machine load and show that no competitive algorithm exists for this problem. We overcome this barrier in two ways, both related to previously known models. The first one is fractional assignment, where each job can be arbitrarily split between the machines. The second one is a semi-online model where the sum of jobs is known in advance. We design algorithms of best possible competitive ratios for both these cases. Furthermore, we show that the combination of the two models leads to the existence of an optimal algorithm (i.e., an algorithm of competitive ratio 1). This algorithm is clearly optimal for the makespan minimization problem as well. For the latter problem, we consider the fractional assignment model and design an algorithm of best possible competitive ratio for it. This work was submitted as the M.Sc. thesis of the first author.     

OPTIMAL BUFFER ALLOCATION IN ASYNCHRONOUS CYCLIC MIXED-MODEL ASSEMBLY LINES

This paper considers a problem of optimal buffer allocation in cyclic asynchronous mixedmodel assembly lines with deterministic processing times. An analytical model is used to provide new insights into properties of optimal buffer allocation, that is, a buffer configuration that guarantees the highest possible throughput rate on the assembly line with a minimum number of buffers. Optimal buffer configuration is characterized, and an efficient algorithm to find such a configuration is developed. The approach proposed in this paper also provides insights on how to allocate a given number of buffers to workstations on the assembly line to maximize the throughput rate.     

A novel approach to subgraph selection with multiple weights on arcs

In this paper, an extension of the minimum cost flow problem is considered in which multiple incommensurate weights are associated with each arc. In the minimum cost flow problem, flow is sent over the arcs of a graph from source nodes to sink nodes. The goal is to select a subgraph with minimum associated costs for routing the flow. The problem is tractable when a single weight is given on each arc. However, in many real-world applications, several weights are needed to describe the features of arcs, including transit cost, arrival time, delay, profit, security, reliability, deterioration, and safety. In this case, finding an optimal solution becomes difficult. We propose a heuristic algorithm for this purpose. First, we compute the relative efficiency of the arcs by using data envelopment analysis techniques. We then determine a subgraph with efficient arcs using a linear programming model, where the objective function is based on the relative efficiency of the arcs. The flow obtained satisfies the arc capacity constraints and the integrality property. Our proposed algorithm has polynomial runtime and is evaluated in rigorous experiments.

     

Multi-start iterated tabu search for the minimum weight vertex cover problem

The minimum weight vertex cover problem (MWVCP) is one of the most popular combinatorial optimization problems with various real-world applications. Given an undirected graph where each vertex is weighted, the MWVCP is to find a subset of the vertices which cover all edges of the graph and has a minimum total weight of these vertices. In this paper, we propose a multi-start iterated tabu search algorithm (MS-ITS) to tackle MWVCP. By incorporating an effective tabu search method, MS-ITS exhibits several distinguishing features, including a novel neighborhood construction procedure and a fast evaluation strategy. Extensive experiments on the set of public benchmark instances show that the proposed heuristic is very competitive with the state-of-the-art algorithms in the literature.     

A Unifying Augmentation Algorithm for Two-Edge Connectivity and Biconnectivity

Given an undirected graph G and two vertex subsets H1 and H2, the bi-level augmentation problem is that of adding to G the smallest number of edges such that the resulting graph contains two internally vertex-disjoint paths between every pair of vertices in H1 and two edge-disjoint paths between every pair of vertices in H2. We present an algorithm to solve this problem in linear time. By properly setting H1 and H2, this augmentation algorithm subsumes existing optimal algorithms for several graph augmentation problems.     

Acyclically 3-colorable planar graphs

In this paper we study the acyclic 3-colorability of some subclasses of planar graphs. First, we show that there exist infinite classes of cubic planar graphs that are not acyclically 3-colorable. Then, we show that every planar graph has a subdivision with one vertex per edge that is acyclically 3-colorable and provide a linear-time coloring algorithm. Finally, we characterize the series-parallel graphs for which every 3-coloring is acyclic and provide a linear-time recognition algorithm for such graphs.     

Fast On-Line/Off-Line Algorithms for Optimal Reinforcement of a Network and its Connections with Principal Partition

The problem of computing the strength and performing optimal reinforcement for an edge-weighted graph G(V, E, w) is well-studied. In this paper, we present fast (sequential linear time and parallel logarithmic time) on-line algorithms for optimally reinforcing the graph when the reinforcement material is available continuously on-line. These are the first on-line algorithms for this problem. We invest O(|V|³|E|log|V|) time (equivalent to (|V|) invocations of the fastest known algorithms for optimal reinforcement) in preprocessing the graph before the start of our algorithms. It is shown that the output of our on-line algorithms is as good as that of the off-line algorithms. Thus our algorithms are better than the fastest off-line algorithms in situations when a sequence of more than (|V|) reinforcement problems need to be solved. The key idea is to make use of ideas underlying the theory of Principal Partition of a Graph. Our ideas are easily generalized to the general setting of polymatroid functions. We also present a new efficient algorithm for computation of the Principal Sequence of a graph.     

A rearrangement of adjacency matrix based approach for solving the crossing minimization problem

In this paper, we first present a binary linear programming formulation for the crossing minimization problem (CMP) in bipartite graphs. Then we use the models of a modified minimum cost flow problem (MMCF) and a travelling salesman problem (TSP) to approximatively solve the CMP by rearranging the adjacency matrix of the bipartite graph. Our approaches are useful for problems defined on dense bipartite graphs. In addition, we compute the exact crossing numbers for some general dense graphs.     

On risk-averse maximum weighted subgraph problems

In this work, we consider a class of risk-averse maximum weighted subgraph problems (R-MWSP). Namely, assuming that each vertex of the graph is associated with a stochastic weight, such that the joint distribution is known, the goal is to obtain a subgraph of minimum risk satisfying a given hereditary property. We employ a stochastic programming framework that is based on the formalism of modern theory of risk measures in order to find minimum-risk hereditary structures in graphs with stochastic vertex weights. The introduced form of risk function for measuring the risk of subgraphs ensures that optimal solutions of R-MWS problems represent maximal subgraphs. A graph-based branch-and-bound (BnB) algorithm for solving the proposed problems is developed and illustrated on a special case of risk-averse maximum weighted clique problem. Numerical experiments on randomly generated Erdös-Rényi graphs demonstrate the computational performance of the developed BnB.     

Scheduling Packets with Values and Deadlines in Size-Bounded Buffers

Motivated by providing quality-of-service differentiated services in the Internet, we consider buffer management algorithms for network switches. We study a multi-buffer model. A network switch consists of multiple size-bounded buffers such that at any time, the number of packets residing in each individual buffer cannot exceed its capacity. Packets arrive at the network switch over time; they have values, deadlines, and designated buffers. In each time step, at most one pending packet is allowed to be sent and this packet can be from any buffer. The objective is to maximize the total value of the packets sent by their respective deadlines. A 9.82-competitive online algorithm (Azar and Levy in Lect Notes Comput Sci 4059:5–16 2006) and a 4.73-competitive online algorithm (Li in Lect Notes Comput Sci 5564:265–278, 2009) have been provided for this model, but no offline algorithms have yet been described. In this paper, we study the offline setting of the multi-buffer model. Our contributions include a few optimal offline algorithms for some variants of the model. Each variant has its unique and interesting algorithmic feature.     

The broadcast median problem in heterogeneous postal model

We propose the problem of finding broadcast medians in heterogeneous networks. A heterogeneous network is represented by a graph G=(V,E), in which each edge has a weight that denotes the communication time between its two end vertices. The overall delay of a vertex v∈V(G), denoted as b(v,G), is the minimum sum of the communication time required to send a message from v to all vertices in G. The broadcast median problem consists of finding the set of vertices v∈V(G) with minimum overall delay b(v,G) and determining the value of b(v,G). In this paper, we consider the broadcast median problem following the heterogeneous postal model. Assuming that the underlying graph G is a general graph, we show that computing b(v,G) for an arbitrary vertex v∈V(G) is NP-hard. On the other hand, assuming that G is a tree, we propose a linear time algorithm for the broadcast median problem in heterogeneous postal model.     

Labelling algorithms for paired-domination problems in block and interval graphs

Let G=(V,E) be a graph without isolated vertices. A set S⊆V is a paired-dominating set if every vertex in V−S is adjacent to a vertex in S and the subgraph induced by S contains a perfect matching. The paired-domination problem is to determine the paired-domination number, which is the minimum cardinality of a paired-dominating set. Motivated by a mistaken algorithm given by Chen, Kang and Ng (Discrete Appl. Math. 155:2077–2086, 2007), we present two linear time algorithms to find a minimum cardinality paired-dominating set in block and interval graphs. In addition, we prove that paired-domination problem is NP-complete for bipartite graphs, chordal graphs, even for split graphs.     

Some graph optimization problems with weights satisfying linear constraints

In this paper, we study several graph optimization problems in which the weights of vertices or edges are variables determined by several linear constraints, including maximum matching problem under linear constraints (max-MLC), minimum perfect matching problem under linear constraints (min-PMLC), shortest path problem under linear constraints (SPLC) and vertex cover problem under linear constraints (VCLC). The objective of these problems is to decide the weights that are feasible to the linear constraints, and find the optimal solutions of corresponding graph optimization problems among all feasible choices of weights. We find that these problems are NP-hard and are hard to be approximated in general. These findings suggest us to explore various special cases of them. In particular, we show that when the number of constraints is a fixed constant, all these problems are polynomially solvable. Moreover, if the total number of distinct weights is a fixed constant, then max-MLC, min-PMLC and SPLC are polynomially solvable, and VCLC has a 2-approximation algorithm. In addition, we propose approximation algorithms for various cases of max-MLC.

     

Median problems with positive and negative weights on cycles and cacti

This paper deals with facility location problems on graphs with positive and negative vertex weights. We consider two different objective functions: In the first one (MWD) vertices with positive weight are assigned to the closest facility, whereas vertices with negative weight are assigned to the farthest facility. In the second one (WMD) all the vertices are assigned to the nearest facility. For the MWD model it is shown that there exists a finite set of points in the graph which contains the locations of facilities in an optimal solution. Furthermore, algorithms for both models for the 2-median problem on a cycle are developed. The algorithm for the MWD model runs in linear time, whereas the algorithm for the WMD model has a time complexity of  O(n²)\mathcal{O}(n^{2}) .     

An inverse approach to convex ordered median problems in trees

The convex ordered median problem is a generalization of the median, the k-centrum or the center problem. The task of the associated inverse problem is to change edge lengths at minimum cost such that a given vertex becomes an optimal solution of the location problem, i.e., an ordered median. It is shown that the problem is NP-hard even if the underlying network is a tree and the ordered median problem is convex and either the vertex weights are all equal to 1 or the underlying problem is the k-centrum problem. For the special case of the inverse unit weight k-centrum problem a polynomial time algorithm is developed.     

有限缓冲区的多节点订单接受模型与算法

订单接受问题广泛存在于生产管理中,而现有多节点订单接受问题中大多不考虑缓冲区约束对订单接受的影响。针对这一问题,以缓冲区约束的多节点生产为背景,建立了订单接受模型。利用改进NEH算法、离散和声搜索算法和变邻域搜索的混合算法对模型进行求解。实验结果显示,当问题规模较小时,算法取得较好的计算效果。问题规模较大时,求解效果一般。缓冲区的大小对订单完工时间影响较小,与无限缓冲区的计算结果相似。混合算法具有较好的求解速度,能够有效求解问题模型。     

Efficient identifications of structural similarities for graphs

Measuring and detecting graph similarities is an important topic with numerous applications. Early algorithms often incur quadratic time or higher, making them unsuitable for graphs of very large scales. Motivated by the cooling process of an object in a thermodynamic system, we devise a new method for measuring graph similarities that can be carried out in linear time. Our algorithm, called Random Walker Termination (RWT), employs a large number of random walkers to capture the structure of a given graph using termination rates in a time sequence. To verify the effectiveness of the RWT algorithm, we use three major graph models, namely, the Erd?s-Rényi random graphs, the Watts-Strogatz small-world graphs, and the Barabási-Albert preferential-attachment graphs, to generate graphs of different sizes. We show that the RWT algorithm performs well for graphs generated by these models. Our experiment results agree with the actual similarities of generated graphs. Using self-similarity tests, we show that RWT is sufficiently stable to generate consistent results. We use the graph edge rerouting test and the cross model test to demonstrate that RWT can effectively identify structural similarities between graphs.