Recent Advances of the Russian Operations Research Society
This collection of articles highlights the most interesting new results from the IX Moscow International Operations Research Conference, the largest Russian meeting in this field, held every three years for leading experts. These papers will interest researchers and organizations specialized in OR, Game Theory, System Analysis, Macro- and Micro-economic Modelling, and Actuarial Mathematics. The volume may be a source for PhD and Master students in the specified areas. The proposed methods for optimal decision making will be useful for insurance and auditing companies, banks, and others.
The volume consists of three parts; the first includes game-theoretic models, the second part considers innovations and their possible opposite impact to the growth of GDP and social welfare, as well as new methods for improving reliability of banks’ credit risks, monopolistic competition under heterogeneous labor, interregional trade and different ways of developing the Russian banking system. The last part contains articles on a wide range of optimization problems and their applications.
This volume comprises the proceedings of the IX Moscow International Conference on Operations Research (ORМ 2018 – Germeyer100) in the scope of fundamental research and applications of decision-making theory under uncertainty, operations research in multiple areas as well as numerical methods of operations research. The conference is devoted to the centenary of the outstanding Soviet scientist, professor Yuri Germeyer who contributed greatly to the development of mathematical foundations of the decision making theory and played a key role in the formation of the domestic school of operations research and the game theory. The reports demonstrate a further and profound development of the Germeyer scientific school.
We consider a problem of the astronaut training scheduling. Each astronaut has his own set of tasks which should be performed with respect to resource and time constraints. The problem is to determine start moments for all considered tasks. For this issue a mathematical model based on integer linear programming is proposed. Computational results of the implemented model and experiments on real data are presented.
On the one hand, the relevance of this research is determined by an attempt of solving the problem of optimal inventory allocation, which can open the possibilities for increase in stock turnover. On the other hand, there was an attempt to extend the list of problems which can be solved by operations research methods. The potential of application of operations research methods (transport model as a specific case of linear programming, in particular) is underestimated. According to Taha , a transport model is a problem of finding optimal allocation of homogeneous objects from accumulators (a i ) to receivers (b i ) with minimizations of costs on displacement or movement. In our opinion, the canonical form of a transport model represents accumulators as points of departures, receivers as clients and cost on displacement as transport costs. The paradigm of using this model is constrained by using the latter in transport logistics only. In fact we can apply this model in much more problems (micro, meso or macro level). This study shows that objects and variables from the canonical transport model can be represented as objects from different fields (beyond logistics) thus helping to find an optimal solution to a certain problem. Our study represents accumulators as nominal cells where work-in-process (WIP) product is in the warehouse, receivers - as production lines and costs on displacements as mileage of loaders. Thus, the cost function Z that we want to optimize is the function of mileage of loaders. Minimizing function Z will enable us to find the optimal allocation of WIP products to production lines (next production stage)
EURO 2013 Abstract Book
Wildfires are a naturally occurring phenomenon in many places of 4 the world. While they perform a number of important ecological functions, the 5 proximity of human activities to forest landscapes requires a measure of control/pre- 6 paredness to address safety concerns and mitigate damage. An important technique 7 utilized by forest managers is that of wildfire fuel management, in which a portion 8 of the available combustible material in the forest is disposed of through a variety 9 of fuel treatment activities. A number of operations research approaches have been 10 applied to locate and schedule these fuel treatment activities, and herein we review 11 and discuss the various models and approaches in the literature
Single track segments are common in various railway networks, in particular in various supply chains. For such a segment, connecting two stations, the trains form two groups, depending on what station is the initial station for the journey between these two stations. Within a group the trains differ by their cost functions. It is assumed that the single track is sufficiently long so several trains can travel in the same direction simultaneously. The paper presents polynomial-time algorithms for different versions of this two-station train scheduling problem with a single railway track. The considered models differ from each other by their objective functions.
The cosmonauts training planning problem is a problem of construc- tion of cosmonauts training timetable. Each cosmonaut has his own set of tasks which should be performed with respect to resource and time con- straints. The problem is to determine start moments for all considered tasks. This problem is a generalization of the resource-constrained project scheduling problem with “time windows”. In addition, the investigated problem is extended with restrictions of different kinds. Previously, for solving this problem the authors proposed an approach based on methods of integer linear programming. However, this approach turned out to be ineffective for high-dimensional problems. A new heuristic method based on constraint programming is developed. The effectiveness of the method is verified on real data.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.