OPTIMIZATION OF PROJECT SCHEDULING UNDER UNCERTAIN PARAMETERS
Abstract
This article considers the problem of operational planning of one-subject production.
The organization of machine-building production is a complex set of works to determine the interrelated
indicators that characterize the activities of the enterprise. Enterprises of this type have a
complex hierarchical structure. It is also necessary to take into account that when planning the
production process, the number of parameters is large and not all of them can be accurately determined,
which affects the efficiency of the enterprise. To solve the problem of effective planning,
the optimality criteria for serial one-subject production were analyzed. One-subject production
includes those where parts of the same name are processed, that is, a production line is formed.
Consequently, the task of optimizing production is to distribute the entire set of work between the
machines and operators servicing this machine in such a way that the planned task is completed
within a given time and the total cost of completing the task is minimal. The article considers the
problem of assignment under uncertainty, carried out experimental calculations and analyzed the
results obtained, which justifies the use of the proposed apparatus of fuzzy sets for solving the
problem of production planning. It is concluded that under conditions of uncertainty, when there is
no exact or statistical information, the apparatus of fuzzy sets makes it possible to analyze theeffectiveness of production activities when setting parameters that reflect the possible values of the
system. In such cases, the use of fuzzy logic mechanisms in the problems of making production
decisions will make it possible to determine optimal or close to optimal solutions.
References
2013, 303 p.
2. Mayer J.H., Winter R., Mohr T. Situational management support systems, Business & Information
Systems Engineering, 2021, 4, pp. 331-345.
3. Ivert L.K., Jonsson P. The potential benefits of advanced planning and scheduling systems in
sales and operations planning, Indus. Manage. Data Syst., 2010, 110 (5), pp. 659-681.
4. Grimson J.A., Pyke D.F. Sales and operations planning: an exploratory study and framework,
The International Journal of Logistics Management, 2007, 18 (3), pp. 322-346.
5. Kolinski A., Śliwczyński B. IT support of production efficiency analysis in ecologi-cal aspect.
In: Golinska, P., Kawa, A. (eds.) Technology Management for Sustainable Production and Logistics.
Springer Verlag, Berlin, 2015, pp. 205-219.
6. Olhager J., Johansson P. Linking long-term capacity management for manufacturing and service
operations, Journal of Engineering and Technology Management, 2012, 29 (1), pp. 22-33.
7. Adamczak M., Domański R., Hadaś Ł., Cyplik P. The integration between produc-tion-logistics
system and its task environment chosen aspects, IFAC-PapersOnline, 2016, 49 (12), pp. 656-661.
8. Hentschel B., Domański R., Adamczak M., Cyplik P., Hadaś L., Kupczyk M., Pruska Z. Ranking
of integration factors within supply chains of forward and backward types—
recommendations from researches, Logforum, 2015, 11 (2), pp. 161-169.
9. Berghman L., Leus R., Spieksma F. Optimal solutions for a dock assignment prob-lem with
trailer transportation, Annals of Operations Research, 2014, 213, pp. 3-25.
10. Van der Aalst W.M.P., Adriansyah A., Alves de Medeiros A.K. Process Mining Manifesto,
Lecture Notes in Business Information Processing, 2012, 99, pp. 169-194.
11. Khandelwal A. A modified approach for assignment method, International Journal of Latest
Research in Science and Technology, 2014, 3 (2), pp. 136-138.
12. Ahmed A., Ahmad A. A new method for finding an optimal solution of assignment problem,
International Journal of Modern Mathematical Sciences, 1014, 12 (1), pp. 10-15.
13. Thiruppathi A., Iranian D. An Innovative Method for Finding Optimal Solution to Assignment
problems, IJIRSET, 2015, 4 (8), pp. 7366-7370.
14. Ghadle K.P., Ingle S.M., Hamoud A.A. Optimal solution of fuzzy transshipment problem using
generalized hexagonal fuzzy numbers, International Journal of Engi-neering and Technology
(UAE), 2018, 7 (4.10 Special Issue 10), pp. 558-561.
15. Kumar A., Gupta A. Assignment and Travelling Salesman Problems with Coeffi-cients as LR
Fuzzy Parameters, International Journal of Applied Science and Engi-neering, 2012, 10 (3),
pp. 155-170.
16. Kumar A., Gupta A., Kaur A. Method for solving fully fuzzy assignment problems using triangular
fuzzy numbers, International Journal of Computer and Information Engineering, 2009,
3 (7), pp. 1889-1892.
17. Dehghan M., Hashemi B., Ghatee M. Computational methods for solving fully fuzzy linear
systems, Applied Mathematics and Computation, 2006, 179, pp. 328-343.
18. Kacprzyk J. Group decision making with a fuzzy linguistic majority, Fuzzy Sets and Systems,
1986, 18 (2), pp. 105-118.
19. Wasserstein R., Lazar N. The ASA Statement on p-Values: Context, Process, and Purpose, The
American Statistician, 2016, 70 (2), pp. 129-133.
20. Kosenko O., Bozhenyuk A., Belyakov S., Knyazeva M. Optimization of Spa-tial-Time Planning
Resource Allocation Under Uncertainty, Advances in Intelligent Systems and Computing.
Springer, 2021, 1197, pp. 1475-1482.
21. Kosenko O., Bozhenyuk A., Knyazeva M. The Task of Optimizing Production Plan-ning with
Fuzzy Parameters, Lecture Notes in Networks and Systems, 2022, 307, pp. 546-553.
