The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

(1987). Applied Probability and Queues. Journal of the Operational Research Society: Vol. 38, No. 11, pp. 1095-1096.

https://link.springer.com/content/pdf/10.1057%2Fjors.1987.184.pdf

Applied Probability and Queues

/pdf/reversibility-and-stochastic-networks-17kl9p5ino.pdf

Reversibility and Stochastic Networks

Convergence Of Probability Measures

In this paper we investigate an M/M/? queue whose parameters depend on an external random environment that we assume to be a semi-Markovian process with finite state space. For this model we show a recursive formula that allows to compute all the factorial moments for the number of customers in the system in steady state. The used technique is based on the calculation of the raw moments of the measure of a bidimensional random set. Finally the case when the random environment has only two states is deeper analyzed. We obtain an explicit formula to compute the above mentioned factorial moments when at least one of the two states has sojourn time exponentially distributed.

M/M/∞ queues in semi-Markovian random environment

In this paper we consider the first passage process of a spectrally negative Markov additive process (MAP). The law of this process is uniquely characterized by a certain matrix function, which plays a crucial role in fluctuation theory. We show how to identify this matrix using the theory of Jordan chains associated with analytic matrix functions. This result provides us with a technique that can be used to derive various further identities.

/pdf/first-passage-of-a-markov-additive-process-and-generalized-4sp11i0p35.pdf

First passage of a Markov additive process and generalized Jordan chains

In this paper we report on a computational experience with a local search algorithm for High-school Timetabling Problems. The timetable has to satisfy "hard" requirements, that are mandatory, and should minimize the violation of "soft" constraints. In our approach, we combine Simulated Annealing with a Very Large-Scale Neighborhood search where the neighborhood is explored by solving an Integer Programming problem. We report on a computational experience validating the usefulness of the proposed approach.

A computational study of local search algorithms for Italian high-school timetabling

The single-machine scheduling problem (SMSP) with release dates concerns the optimal allocation of a set of jobs on a single machine that is not able to process more than one job at a time. Each job is ready to be processed at a release date and without interruption. The goal is to minimize the total weighted completion time of the jobs. In this paper the time-indexed formulation is considered and a new lagrangean heuristic is proposed, based on the observation that lagrangean relaxation of job constraints leads to a weighted stable set problem on an interval graph. The relaxed problem is polynomially solvable by a dynamic-programming algorithm. We report computational experience, showing that instances up to 400 jobs and maximum processing time 50 (around 5,000,000 variables) are solved in less than 40 minutes on a personal computer, yielding duality gaps never exceeding 3%. We also test a set of hard instances, built to produce bad performances where we yield duality gaps less than 5%.

/pdf/near-optimal-solutions-of-large-scale-single-machine-11rrs76op1.pdf

Near-Optimal Solutions of Large-Scale Single-Machine Scheduling Problems

https://pure.tue.nl/ws/files/3151967/602035.pdf

Bernardo D'Auria

Papers

M/M/∞ queues in semi-Markovian random environment

First passage of a Markov additive process and generalized Jordan chains

A computational study of local search algorithms for Italian high-school timetabling

Near-Optimal Solutions of Large-Scale Single-Machine Scheduling Problems

Stochastic decomposition of the M/G/∞ queue in a random environment