### Book

## Lecture Notes in Computer Science

This volume contains the refereed proceedings of the 18th international conference on Mathematical Optimization Theory and Operations Research (MOTOR 2019)1 held during July 8–12, 2019, near Ekaterinburg, Russia. The conference brings together a wide research community in the fields of mathematical programming and global optimization, discrete optimization, complexity theory and combinatorial algorithms, optimal control and games, and their applications in relevant practical problems of operations research, mathematical economy, and data analysis

In the paper, a two-level infinitely repeated hierarchical game with one player (center) C0 on the first level and S1...Sn subordinate players on the second is considered. On each stage of the game player C0 selects vector x=(x1....xn) from a given set X, in which each component represents a vector of resources delivered by C0 to one of the subordinate players, i.e. (formula presented). At the second level, Si i=1,2..,n, choose the controls (formula presented), where Yi(xi) depends upon the choice of player C0. In this game, a set of different Nash equilibrium also based on threat and punishment strategies is obtained. In one case, the center enforces special behavior of subordinate firms (vector of manufactured goods), threatening to deprive them of resources on the next steps if the subordinate firms refuse to implement the prescribed behavior. In another case, the subordinate firms can force the center to use a certain resource allocation threatening to stop production. Using different combinations of such behaviors on different stages of the game, we obtain a wide class of Nash equilibrium in the game under consideration. The cooperative version of the game is also considered. The conditions are derived under which the cooperative behavior can be supported by Nash Equilibrium or Strong Nash Equilibrium (Nash Equilibrium stable against deviations of coalitions).

In this paper, we consider the minimizing total weighted completion time in preemptive equal-length job with release dates scheduling problem on a single machine. This problem is known to be open. Here, we give some properties of optimal schedules for the problem and its special cases.

Consideration was given to a graphic realization of the method of dynamic programming. Its concept was demonstrated by the examples of the partition and knapsack problems. The proposed method was compared with the existing algorithms to solve these problems.

We study the scheduling problem for single machine with preemptions of jobs. On a machine it is necessary to process a set of n jobs. Simultaneous processing is prohibited, but interrupts in processing jobs is possible. Each job j of the set is characterize by it's weight w_j, release date r_j = j - 1 and processing time p_j = 2. The only restriction is that weights w_j are non-decreasing. The objective function can be expressed as the sum of weighted completion times. We suggest the polynomial algorithm with complexity O(n^4) operations which gives us the Pareto-optimal schedules for each set of jobs. In the algorithm we use generalized Smith's rule, to obtain particular schedules after moment r_n and to prove some important lemmas for reduction of search of suitable schedules.

This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.

A form for an unbiased estimate of the coefficient of determination of a linear regression model is obtained. It is calculated by using a sample from a multivariate normal distribution. This estimate is proposed as an alternative criterion for a choice of regression factors.