Shiftable intervals

Consider a set of n fixed length intervals and a set of n (larger) windows, in one-to-one correspondence with the intervals, and assume that each interval can be placed in any position within its window. If the position of each interval has been fixed, the intersection graph of such set of intervals is an interval graph. By varying the position of each interval in all possible ways, we get a family of interval graphs. In the paper we define some optimization problems related to the clique, stability, chromatic, clique cover numbers and cardinality of the minimum dominating set of the interval graphs in the family, mainly focussing on complexity aspects, bounds and solution algorithms. Some problems are proved to be NP-hard, others are solved in polynomial time on some particular classes of instances. Many practical applications can be reduced to these kind of problems, suggesting the use of Shiftable Intervals as a new interesting modeling framework.     

Project Scheduling under Time Dependent Costs – A Branch and Bound Algorithm

In a given project network, execution of each activity in normal duration requires utilization of certain resources. If faster execution of an activity is desired then additional resources at extra cost would be required. Given a project network, the cost structure for each activity and a planning horizon, the project compression problem is concerned with the determination of optimal schedule (duration) of performing each activity while satisfying given restrictions and minimizing the total cost of project execution. This paper considers the project compression problem with time dependent cost structure for each activity. The planning horizon is divided into several regular time intervals over which the cost structure of an activity may vary. But the cost structure of the activities remains the same (constant) within a time interval. Key events of the project attract penalty for finishing earlier or later than the corresponding target times. The objective is to find an optimal project schedule minimizing the total project cost. We present a mathematical model for this problem, develop some heuristics and an exact branch and bound algorithm. Using simulated problems we provide an insight into the computational performances of heuristics and the branch and bound algorithm.     

Optimal timing of a sequence of tasks with general completion costs

Scheduling a sequence of tasks––in the acceptation of finding the execution times––is not a trivial problem when the optimization criterion is irregular as for instance in earliness–tardiness problems. This paper presents an efficient dynamic programming algorithm to solve the problem with general cost functions depending on the end time of the tasks, idle time costs and variable durations also depending on the execution time of the tasks. The algorithm is also valid when the precedence graph is a tree and it can be adapted to determine the possible execution windows for each task not exceeding a maximum fixed cost.     

Randomized allocation processes

Many dynamic resource allocation and on‐line load balancing problems can be modeled by processes that sequentially allocate balls into bins. The balls arrive one by one and are to be placed into bins on‐line without using a centralized controller. If n balls are sequentially placed into n bins by placing each ball in a randomly chosen bin, then it is widely known that the maximum load in bins is ln n /ln ln n?(1+o(1)) with high probability. Azar, Broder, Karlin, and Upfal extended this scheme, so that each ball is placed sequentially into the least full of d randomly chosen bins. They showed that the maximum load of the bins reduces exponentially and is ln ln n/In d+Θ(1) with high probability, provided d<2. In this paper we investigate various extensions of these schemes that arise in applications in dynamic resource allocation and on‐line load balancing. Traditionally, the main aim of allocation processes is to place balls into bins to minimize the maximum load in bins. However, in many applications it is equally important to minimize the number of choices performed (the allocation time). We study adaptive allocation schemes that achieve optimal tradeoffs between the maximum load, the maximum allocation time, and the average allocation time. We also investigate allocation processes that may reallocate the balls. We provide a tight analysis of a natural class of processes that each time a ball is placed in one of d randomly chosen bins may move balls among these d bins arbitrarily. Finally, we provide a tight analysis of the maximum load of the off‐line process in which each ball may be placed into one of d randomly chosen bins. We apply this result to competitive analysis of on‐line load balancing processes. ©2001 John Wiley & Sons, Inc. Random Struct. Alg., 18: 297–331, 2001     

Interval Routing onk-Trees

A graph has an optimall-interval routing scheme if it is possible to direct messages along shortest paths by labeling each edge with at mostlpairwise-disjoint subintervals of the cyclic interval [1…n] (where each node of the graph is labeled by an integer in the range). Although much progress has been made forl = 1, there is as yet no general tight characterization of the classes of graphs associated with largerl. Bodlaenderet al. have shown that under the assumption of dynamic cost links, each graph with an optimall-interval routing scheme has treewidth of at most 4l. For the setting without dynamic cost links, this paper addresses the complementary question of the number of intervals required to label classes of graphs of treewidthk. Although it has been shown that there exist graphs of treewidth 2 that require a nonconstant number of intervals, our work demonstrates a class of graphs of treewidth 2, namely 2-trees, that are guaranteed to allow 3-interval routing schemes. In contrast, this paper presents a 2-tree that cannot have a 2-interval routing scheme. For generalk, anyk-tree is shown to have an optimal interval routing scheme using 2^{k + 1}intervals per edge.     

Graph models for scheduling systems with machine saturation property

Let be a set of n independent tasks and a set of m processors. During each time instant, each processor can be used by a single task at most. A schedule is for each task an allocation of one or more time intervals to one or more processors. A schedule is said to be optimal if it minimizes the maximum completion time. We say a schedule S has the machine saturation property (MS property) if, at any time instant of task execution, all the machines are simultaneously busy. In this paper, we analyze the conditions under which a parallel scheduling system allows a schedule with the MS property. While for some simple models the analytical conditions can be easily stated, a graph model approach is required when conflicts of processor usage are present. For this reason, we define the class of saturated graphs that correspond to scheduling systems with the MS property. We present efficient graph recognition algorithms to verify the MS property directly on some classes of saturated graphs     

Preemptive scheduling of interval orders is polynomial

In 1979, Papadimitriou and Yannakakis gave a polynomial time algorithm for the scheduling of jobs requiring unit completion times when the precedence constraints form an interval order. The authors solve here the corresponding problem, for preemptive scheduling (a job can be interrupted to work on more important tasks, and completed at a later time, subject to the usual scheduling constraints.) The m-machine preemptive scheduling problem is shown to have a polynomial algorithm, for both unit time and variable execution times as well, when the precedence constraints are given by an interval order.     

On Conditional Covering Problem

The conditional covering problem (CCP) aims to locate facilities on a graph, where the vertex set represents both the demand points and the potential facility locations. The problem has a constraint that each vertex can cover only those vertices that lie within its covering radius and no vertex can cover itself. The objective of the problem is to find a set that minimizes the sum of the facility costs required to cover all the demand points. An algorithm for CCP on paths was presented by Horne and Smith (Networks 46(4):177–185, 2005). We show that their algorithm is wrong and further present a correct O(n ³) algorithm for the same. We also propose an O(n ²) algorithm for the CCP on paths when all vertices are assigned unit costs and further extend this algorithm to interval graphs without an increase in time complexity.     

Improved analysis of an algorithm for the coupled task problem with UET jobs

The coupled task problem is to schedule n jobs, each one consisting of two subtasks with exact delay times between them, on a single machine. We derive a new lower bound for the problem variant with unit execution times and correct a previously published analysis.     

Random interval graphs

In this paper we introduce a notion ofrandom interval graphs: the intersection graphs of real, compact intervals whose end points are chosen at random. We establish results about the number of edges, degrees, Hamiltonicity, chromatic number and independence number of almost all interval graphs.     

A note on two-machine no-wait flow shop scheduling with deteriorating jobs and machine availability constraints

This paper is concerned with two-machine no-wait flow shop scheduling problems in which the actual processing time of each job is a proportional function of its starting time and each machine may have non-availability intervals. The objective is to minimize the makespan. We assume that the non-availability intervals are imposed on only one machine. Moreover, the number of non-availability intervals, the start time and end time of each interval are known in advance. We show that the problem with a single non-availability interval is NP-hard in the ordinary sense and the problem with an arbitrary number of non-availability intervals is NP-hard in the strong sense.     

Generalizations of the parking problem

The well-known parking problem of the Hungarian mathematician Rényi is about the asymptotic behavior of the mathematical expectation of the number of open unit intervals randomly filling a long interval. The length of the interval being filled increases without bound.The paper studies generalizations of the parking problem in two directions. The first is the case where the length of the placed intervals is a random variable. Unlike in the original setting of the problem, the behavior of the expectations of both the number of placed intervals and the measure of the occupied part of the long interval are studied. The second direction is the case where the distribution of the position of a placed unit interval is nonuniform, unlike in the classical parking problem.     

On the solution of the unidimensional local minimization problem

If the functionf to be minimized is not unimodal, the Fibonacci search and the golden section search can determine final search intervals where the functionf takes greater values than at the borders of the first search interval. This can be avoided by small modifications for sequential search procedures where, in each iteration step,f is evaluated at two interior points of the present search interval. The properties of the point of concentration of the search intervals are given.     

A probabilistic analysis of the set covering problem

A probabilistic analysis of the minimum cardinality set covering problem (SCP) is developed, considering a stochastic model of the (SCP), withn variables andm constraints, in which the entries of the corresponding (m, n) incidence matrix are independent Bernoulli distributed random variables, each with constant probabilityp of success. The behaviour of the optimal solution of the (SCP) is then investigated as bothm andn grow asymptotically large, assuming either an incremental model for the evolution of the matrix (for each size, the matrixA is obtained bordering a matrix of smaller size by new columns and rows) or an independent one (for each size, an entirely new set of entries forA are considered). Two functions ofm are identified, which represent a lower and an upper bound onn in order the (SCP) to be a.e. feasible and not trivial. Then, forn lying within these bounds, an asymptotic formula for the optimum value of the (SCP) is derived and shown to hold a.e.The performance of two simple randomized algorithms is then analyzed. It is shown that one of them produces a solution value whose ratio to the optimum value asymptotically approaches 1 a.e. in the incremental model, but not in the independent one, in which case the ratio is proved to be tightly bounded by 2 a.e. Thus, in order to improve the above result, a second randomized algorithm is proposed, for which it is proved that the ratio between the approximate solution value and the optimum approaches 1 a.e. also in the independent model.     

Ordered sets with interval representation and (m,n)-Ferrers relation

Semiorders may form the simplest class of ordered sets with a not necessarily transitive indifference relation. Their generalization has given birth to many other classes of ordered sets, each of them characterized by an interval representation, by the properties of its relations or by forbidden configurations. In this paper, we are interested in preference structures having an interval representation. For this purpose, we propose a general framework which makes use of n-point intervals and allows a systematic analysis of such structures. The case of 3-point intervals shows us that our framework generalizes the classification of Fishburn by defining new structures. Especially we define three classes of ordered sets having a non-transitive indifference relation. A simple generalization of these structures provides three ordered sets that we call “d-weak orders”, “d-interval orders” and “triangle orders”. We prove that these structures have an interval representation. We also establish some links between the relational and the forbidden mode by generalizing the definition of a Ferrers relation.     

Real time task scheduling allowing fuzzy due dates

The paper deals with periodical task scheduling. The tasks are described by fuzzy due dates and fuzzy execution times. The goal of scheduling is to find an optimal assignment of priorities such that the satisfaction associated with due dates and execution times be minimized. The paper shows how the rules associated with task priorities improve the optimal assignment search.     

Asymptotic optimality of statistical multiplexing in pipelined processing

The throughput of pipelined processing ofheterogeneous multitasked jobs is computed and optimized in this study. There areK job classes. Each job hasM tasks which have to be processed in a given order (same for all tasks) on a pipeline ofM processors. Tasks have random processing times. The jobs of each class form a stationary and ergodic sequence (with respect to their task processing times). Classes are differentiated by distinct statistics and may not be jointly stationary or ergodic. Thus, the jobs are overall statistically heterogeneous. We are interested in the average execution time per job , when the job populations of the various classes become very large (asymptotically). This is shown to depend on the order in which jobs enter the pipeline. Under the natural class-based ordering, where all jobs of the first class enter first, followed by those of the second, third, and so on, the quantity is computed, but is shownnot to attain its minimal value in general. On the contrary, appropriate statistical multiplexing of jobs of different classes on the pipeline is shown to minimize the average execution time per job on every sample path (with probability one). The procedure, calledbalanced statistical multiplexing, is constructed and the minimal is computed in terms of the average execution times of the job tasks.     

Periodic load balancing

Multiprocessor load balancing aims to improve performance by moving jobs from highly loaded processors to more lightly loaded processors. Some schemes allow only migration of new jobs upon arrival, while other schemes allow migration of jobs in progress. A difficulty with all these schemes, however, is that they require continuously maintaining detailed state information. In this paper we consider the alternative of periodic load balancing, in which the loads are balanced only at each T time units for some appropriate T. With periodic load balancing, state information is only needed at the balancing times. Moreover, it is often possible to use slightly stale information collected during the interval between balancing times. In this paper we study the performance of periodic load balancing. We consider multiple queues in parallel with unlimited waiting space to which jobs come either in separate independent streams or by assignment (either random or cyclic) from a single stream. Resource sharing is achieved by periodically redistributing the jobs or the work in the system among the queues. The performance of these systems of queues coupled by periodic load balancing depends on the transient behavior of a single queue. We focus on useful approximations obtained by considering a large number of homogeneous queues and a heavy load. When the number of queues is sufficiently large, the number of jobs or quantity of work at each queue immediately after redistribution tends to evolve deterministically, by the law of large numbers. The steady-state (limiting) value of this deterministic sequence is obtained as the solution of a fixed point equation, where the initial value is equal to the expected transient value over the interval between successive redistributions conditional on the initial value. A refined approximation based on the central limit theorem is a normal distribution, where the mean and variance are obtained by solving a pair of fixed-point equations. With higher loads, which is natural to consider when load balancing is performed, a heavy-traffic limit theorem shows that one-dimensional reflected Brownian motion can be used to approximately describe system performance, even with general arrival and service processes. With these approximations, we show how performance depends on the assumed arrival pattern of jobs and the model parameters. We do numerical calculations and conduct simulation experiments to show the accuracy of the approximations. This revised version was published online in June 2006 with corrections to the Cover Date.     

Multicategory classification via discrete support vector machines

Discrete support vector machines (DSVM), originally proposed for binary classification problems, have been shown to outperform other competing approaches on well-known benchmark datasets. Here we address their extension to multicategory classification, by developing three different methods. Two of them are based respectively on one-against-all and round-robin classification schemes, in which a number of binary discrimination problems are solved by means of a variant of DSVM. The third method directly addresses the multicategory classification task, by building a decision tree in which an optimal split to separate classes is derived at each node by a new extended formulation of DSVM. Computational tests on publicly available datasets are then conducted to compare the three multicategory classifiers based on DSVM with other methods, indicating that the proposed techniques achieve significantly higher accuracies. This research was partially supported by PRIN grant 2004132117.     

Complexity of necessary efficiency in interval linear programming and multiobjective linear programming

We present some complexity results on checking necessary efficiency in interval multiobjective linear programming. Supposing that objective function coefficients perturb within prescribed intervals, a feasible point x* is called necessarily efficient if it is efficient for all instances of interval data. We show that the problem of checking necessary efficiency is co-NP-complete even for the case of only one objective. Provided that x* is a non-degenerate basic solution, the problem is polynomially solvable for one objective, but remains co-NP-hard in the general case. Some open problems are mentioned at the end of the paper.