The transportation problem under uncertainty

被引：0

作者：

Pavlov A.A. ^{[1
]}

Zhdanova E.G. ^{[1
]}

机构：

[1] National Technical University of Ukraine, Igor Sikorsky Kiev Polytechnic Institute, Kiev

来源：

Journal of Automation and Information Sciences | 2020年 / 52卷 / 04期

关键词：

Combinatorial optimization; Compromise solution; Transportation problem; Uncertainty;

D O I：

10.1615/JAutomatInfScien.v52.i4.10

中图分类号：

学科分类号：

摘要：

The efficiency of applying the general theoretical positions proposed by A.A. Pavlov to find a compromise solution for one class of combinatorial optimization problems under uncertainty by the example of solving the transportation linear programming problem is studied. The studied class of problems is characterized as follows: 1) the optimization criterion is a weighted linear convolution of arbitrary numerical characteristics of a feasible solution; 2) there exists an efficient algorithm to solve the problem in deterministic formulation that does not allow one to change the constraints structure; 3) by the uncertainty is meant the ambiguity of values of the weight coefficients included in the optimization criterion. We search for compromise solutions according to one of five criteria. A mathematical model of transportation problem is formulated in which uncertainty is caused by the fact that the matrix of transportation costs-per-unit can take one of several possible values at the stage of solution implementation. Practical situations which lead to such a model are described. The method of finding the compromise solution is illustrated by the examples of some individual transportation problems under uncertainty. The research confirmed the efficiency of practical application of the general theoretical principles and allowed us to expand significantly the class of combinatorial optimization problems under uncertainty for which these theoretical results are applicable. © 2020 by Begell House Inc.

引用

页码：1 / 13

页数：12

共 12 条

[1]

Pavlov A.A., Optimization for one class of combinatorial problems under uncertainty, Adaptyvni systemy avtomatychnogo upravlinnya, 1, 34, pp. 81-89, (2019)

[2]

Pavlov A.A., Combinatorial optimization under uncertainty and formal models of expert estimation, Visnyk Natsionalnogo tekhnichnogo universytetu "KhPI", 1, pp. 3-7, (2019)

[3]

Zgurovsky M.Z., Pavlov A.A., Combinatorial optimization problems in planning and decision making: Theory and applications, 1st edn, Studies in systems, Decision and Control, 173, (2019)

[4]

Zgurovsky M.Z., Pavlov A.A., Intractable combinatorial optimization problems in planning and decision making, (2016)

[5]

Nogin V.D., Linear convolution of criteria in multicriteria optimization, Iskusstvennyi intellect i prinyatie resheniy, 4, pp. 73-82, (2014)

[6]

Gorelik V.A., Zolotova T.V., Problem of selecting an optimal portfolio with a probabilistic risk function, Journal of Mathematical Sciences, 216, 5, pp. 603-611, (2016)

[7]

Kharchenko A., Halay I., Zagorodna N., Bodnarchuk I., Trade-off optimal decision of the problem of software system architecture choice, Xth International scientific and technical conference "Computer Sciences and Information Technologies" (CSIT), pp. 198-205, (2015)

[8]

Novikova N.M., Pospelova I.I., Zenyukov A.I., Convolution method in multicriteria problems with uncertainty, Izvestiya RAN. Teoriya i sistemy upravleniya, 5, pp. 27-45, (2017)

[9]

Bindima T., Elias E., A novel design and implementation technique for low complexity variable digital filters using multi-objective artificial bee colony optimization and a minimal spanning tree approach, Engineering Applications of Artificial Intelligence, 59, pp. 133-147, (2017)

[10]

Mirzapour S.M.J., Hashem Al.-E., Malekly H., Aryanezhad M.B., A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty, International Journal of Production Economics, 134, pp. 28-42, (2011)

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