IMAGE PROCESSING METHODS
PATTERN RECOGNITION
MATHEMATICAL MODELING
A. V. Ilyin, V. D. Ilyin Solving Situationally Definable Linear Problems of Resource Planning: a Review of Updated Technology
A. V. Ilyin, V. D. Ilyin Solving Situationally Definable Linear Problems of Resource Planning: a Review of Updated Technology

Abstract.

The situational resource planning is considered as essential part of situational management. A review presents the updated technology for solving situationally definable linear problems of resource planning, including the method of resource allocation by the target displacement of solution and the method of interval cost planning. The definition of a new class of planning and management problems – the situationally definable ones – is proposed. The statement of problems is oriented to the mode of computational experiment, taking into account the dynamically changing object awareness, the conditions of the object functioning and the clarified goals of the decision makers, whose expert knowledge plays an important role. State of the managed system and the planning conditions are represented by portraits of situations. At each step of the plan search, statement of the problem is described by a system of mandatory and orienting requirements, formed on the basis of results of the situation portraits analysis. The proposed methods are designed for implementation in online services operating in the digital twins environment. An example of application of the updated technology to the situational management of electricity production is given.

Keywords:

situationally definable problem, portrait of situation, system of mandatory and orienting requirements, target displacement of solution, interval cost planning, situational managament of electricity generation.

PP. 99-106.

DOI 10.14357/20718632190309

References

1. Foote, B. L., A. Ravindran, and S. Lashine. 1988. Production planning and scheduling: computational feasibility of multi-criteria models of production, planning and scheduling. Computers and Industrial Engineering. 15:129–138. doi: 10.1016/0360-8352(88)90075-7.
2. Nam, S. J. and R. Logendran. 1992. Aggregate production planning – a survey of models and methodologies. European Journal of Operational Research. 61(3): 255–272. doi: 10.1016/0377-2217(92)90356-E.
3. Byrne, M. D. and M. A. Bakir. 1999. Production planning using a hybrid simulation – analytical approach. International Journal of Production Economics. 59:305– 311. doi: 10.1016/S0925-5273(98)00104-2.
4. Hung, Y. F., C. C. Shih, and C. P. Chen. 1999. Evolutionary algorithms for production planning problems with setup decisions. Journal of the Operational Research Society. 50(8):857–866. doi: 10.1057/palgrave.jors.2600778.
5. Wang, R. C. and H. H. Fang. 2001. Aggregate production planning with multiple objectives in a fuzzy environment. European Journal of Operational Research. 133(3):521–536. doi: 10.1007/s00170-003-1885-6.
6. Leung, S. C., Y. Wu, and K. K. Lai. 2003. Multi-site aggregate production planning with multiple objectives: a goal programming approach. Production Planning & Control. 14(5):425-436. doi: 10.1080/0953728031000154264.
7. Luedtke, J. and G. L. Nemhauser. 2009. Strategic planning with start-time dependent variable costs. Operations Research. 57(5):1250-1261. doi: 10.1287/opre.1080.0649.
8. Vujovic, A., Z. Krivokapic, J. Jovanovic, and G. Kijanovic. 2016. Production optimization using agentbased system. International Journal for Quality Research. 10(1):193–204. doi: 10.18421/IJQR10.01-10.
9. Jede, A. and F. Teuteberg. 2016. Understanding sociotechnical impacts arising from software as-a-service usage in companies. Business & Information Systems Engineering. 58(3):161–176. doi: 10.1007/s12599-016-0429-1.
10. Ilyin, A. V. 2013. Ekspertnoye planirovaniye resursov [Expert resource planning]. Moscow: IPIRAN [Institute of Informatics Problems of the Russian Academy of Sciences]. 58 p.
11. Ilyin, A. V. and V. D. Ilyin. 2016. Variational online budgeting taking into account the priorities of expense items. Agris On-line Papers in Economics and Informatics. 8(3):51–56. doi: 10.7160/aol.2016.080305
12. Klykov, Yu. I. 1974. Situatsionnoye upravleniye bol’shimi sistemami [Situational management of large systems]. Moscow: Energiya. 134 p.
13. Pospelov, D. A. 1986. Situatsionnoye upravleniye: teoriya i praktika [Situational management: theory and practice]. Moscow: Nauka. 228 p.
14. Ilyin, V. D. 1996. Osnovaniya situatsionnoy informatizatsii [Fundamentals of situational informatization]. Moscow: Nauka. Fizmatlit. 180 p.
15. Kantorovich, L. V. 1939. Matematicheskiye metody organizatsii i planirovaniya proizvodstva [Mathematical methods of organization and production planning]. Leningrad: LGU [Leningrad State University]. 67 p.
16. Dantzig, G. B. 1963. Linear programming and extensions. Princeton: Princeton University Press and the RAND Corporation. 656 p.
17. Karmarkar, N. 1984. A new polynomial-time algorithm for linear programming. Combinatorica. 4(4):373-395.
18. Ilyin, A. V. and V. D. Ilyin. 2013. The technology of interactive resource allocation in accordance with the customizable system of rules. Applied Mathematical Sciences. 143(7):7105-7111. doi: 10.12988/ams.2013.311649.
19. Tikhonov, A. N. and V. Y. Arsenin. 1977. Solutions of ill-posed problems. Washington DC: V. H. Winston & Sons. 259 p.
20. Ilyin, A. V. and V. D. Ilyin. 2018. Model’ raspredeleniya potokov v odnoproduktovoy setevoy sisteme [The model of flows distribution in a single-product network system]. Sistemy i sredstva informatiki [Systems and means of informatics]. 28(2):170-177. doi: 10.14357/08696527180213.
21. Ilyin, A. V. and V. D. Ilyin. 2018. Situatsionnoye planirovaniye proizvodstva v setevoy M2M-sisteme [Situational production planning in network M2Msystem]. Sistemy i sredstva informatiki [Systems and means of informatics]. 28(4):136-144. doi: 10.14357/08696527180413.
 

 

 

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