Temat: multilevel programming

Skocz do pozycji: 1.

Tytuł:: A solution method for stochastic multilevel programming problems : a systematic sampling evolutionary approach
Autorzy:: Goshu, Natnael Nigussie
Kassa, Semu Mitiku
Tematy:: multilevel programming
stochastic programming
Stackelberg equilibrium
sample average approximation
systematic sampling
particle swarm optimization; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/58969788.pdf Link otwiera się w nowym oknie
Opis:: Stochastic multilevel programming is a mathematical programming problem with some given number of hierarchical levels of decentralized decision makers and having some kind of randomness properties in the problem definition. The introduction of some randomness property in its hierarchical structure makes stochastic multilevel problems computationally challenging and expensive. In this article, a systematic sampling evolutionary method is adapted to solve the problem. The solution procedure is based on realization of the random variables and systematic partitioning of each hierarchical level’s decision space for searching an optimal reaction. The search goes sequentially upwards starting from the bottom up through the top hierarchical level problem. The existence of solution and convergence of the solution procedure is shown. The solution procedure is implemented and tested on some selected deterministic test problems from literature. Moreover, the proposed algorithm can be used to solve stochastic multilevel programming problems with additional complexity in their problem definition.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: An adaptive method to solve multilevel multiobjective linear programming problems
Autorzy:: Kaci, Mustapha
Radjef, Sonia
Tematy:: multilevel programming
multiobjective linear programming
adaptive method
sub-optimality estimate
nondominated solutions
non-dominated facets; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/27315332.pdf Link otwiera się w nowym oknie
Opis:: This paper is a follow-up to a previous work where we defined and generated the set of all possible compromises of multilevel multiobjective linear programming problems (ML-MOLPP). We introduce a new algorithm to solve ML-MOLPP in which the adaptive method of linear programming is nested. First, we start by generating the set of all possible compromises (set of all non-dominated solutions). After that, an algorithm based on the adaptive method of linear programming is developed to select the best compromise among all the possible settlements achieved. This method will allow us to transform the initial multilevel problem into an ML-MOLPP with bonded variables. Then, apply the adaptive method which is the most efficient to solve all the multiobjective linear programming problems involved in the resolution process instead of the simplex method. Finally, all the construction stages are carefully checked and illustrated with a numerical example.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Branch and bound algorithm for discrete multi- level linear fractional programming problem
Autorzy:: Arora, R.
Gupta, K.
Tematy:: linear fractional programming problem
bilevel programming
multilevel programming
discrete variables
integer solution
branch and bound cut
programowanie dwustopniowe
programowanie wielopoziomowe
zmienne dyskretne; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/406478.pdf Link otwiera się w nowym oknie
Opis:: An algorithm is proposed to find an integer solution for bilevel linear fractional programming problem with discrete variables. The method develops a cut that removes the integer solutions which are not bilevel feasible. The proposed method is extended from bilevel to multilevel linear fractional programming problems with discrete variables. The solution procedure for both the algorithms is elucidated in the paper.
Dostawca treści:: Biblioteka Nauki

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