BIOINFORMATICS AND MEDICINE
IMAGE PROCESSING METHODS
TEXT MINING
MATHEMATICAL MODELING
CONTROL SYSTEMS
Y.M. Tsodikov Information model for solving the infeasible problem of optimal production planning
DATA PROCESSING AND ANALYSIS
Y.M. Tsodikov Information model for solving the infeasible problem of optimal production planning

Abstract.

The article deals with the problem of interpretation of an infeasible solution for a large-scale of the problem of optimal production planning. The formulation of the problem of optimal planning refinery is given. This formulation of the problem provides a solution by the method of successive linear programming (SLP). The complexity of the interpretation of infeasible constraints for large-scale planning problems is shown. A method of sequential choice of variants for the analysis of infeasible constraints is proposed. This way of selecting options has been tested on plant models. This tool was used in training specialists and in developing models of plants. The justification of the proposed method for selecting the variant for the analysis of infeasible constraints is given, based on the geometry of constraint space. An example of infeasible constraints is the increase in the complexity of interpreting the causes of the infeasible solution with the increase in the dimension of the problem. This is well matched with the solution experience and known data for a large-scale of the optimization problems.

Keywords:

optimal production planning, successive linear programming (SLP), infeasible problem.

PP. 55-62.

DOI 10.14357/20718632180406

References

1. Dantzig G.B. 1998. Linear programming and extensions. Princeton University Press.
2. Eremin I.I. 1988. Protivorechivye modeli optimal'nogo planirovaniya [Contradictory models of optimal planning]. Moscow: Nauka. 305 p.
3. Orchard-Hays W. 1968. Advanced Linear-Programming Computing Techniques. McGraw-Hill. 355 p.
4. Orchard-Hays W. Some additional views on the simplex method and the geometry of constraint space. IIASA, 1976. 76 p.
5. Coxhead R.E. Integrated Planning and Scheduling Systems for the Refining Industry // Optimization in industry. Mathematical Programming and Modeling Techniques in Practice. Ed. Ciriani T.A., Leachman R.C. J. - Wiley&Sons, 1994. - P. 185-199.
6. Tsodikov Y.M., Hohlov A.S. 2013. Nelineinye modeli optimalnogo planirovaniya neftepererabatyvayschego zavoda. [“Nonlinear Model for Optimal Planning of Refinery”] Trudy “VII Moscow mezhdunarodnaiya konferentsii po issledovaniyu operatsii” [“VII Moscow International Conference on Operation Research (ORM2013)” Proceedings]. Moscow 54–56.
7. Refinery and Petrochemical Modeling System (RPMS). WWW.Honeywell.com. (accessed December 10, 2017).
8. Popov L.D. 2012. Primeneniye bar'yernykh funktsiy dlya optimal'noy korrektsii nesobstvennykh zadach lineynogo programmirovaniya 1-go roda [Application of barrier functions for optimal correction of improper linear programming problems of the first kind]. // Avtomatika i telemekhanika [Automation and telemechanics] 3:3–11.
9. Skarin V.D. 2012. O nekotorykh universal'nykh metodakh korrektsii nesobstvennykh zadach vypuklogo programmirovaniya [On some universal methods for correcting improper convex programming problems]. Avtomatika i telemekhanika [Automation and telemechanics] 3:99-110.
10. Dudnikov E.E., Tsodikov Y.M.1979. Tipovye zadachi operativnogo upravleniya nepreryvnym proizvodstvom [Typical problems of operational management of continuous production]. Moscow: Energy 272 p.
11. Lasdon L.S. An improved successive linear programming algorithm // Management Science. – 1985, –Vol. 31, N10, – P.1312–1331.
12. Hobotov E.N. 2008. Modeli planirovaniya i upravleniya po smesheniyu masel [Models of planning and management of oil mixing]. Avtomatika i telemekhanika [Automation and telemechanics] 11:178-189.
 

2024 / 02
2024 / 01
2023 / 04
2023 / 03

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".