An Optimization-Simulation Approach to Chance-Constraint Programming
2018
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This paper considers a stochastic programming problem with a number of random parameters in the set of constraints. The method used for solving the problem is the iterative optimization- simulation approach. It consists of two phases: optimization phase, which includes solving a deterministic counterpart of the original chance-constrained problem, and a simulation phase in which the original constraints are checked using Monte Carlo simulation. One iteration corresponds to one scenario. If the decision maker is dissatisfied with the results, a new scenario is generated in which the deterministic values of stochastic parameters are changed in the direction that will provide a more robust solution. The deterministic counterpart in the new scenario is formulated depending on the result of the previous iteration. To that end, different heuristics are considered. The main goal is to provide a good insight on the optimization problem under uncertainty by performing a relatively small number ...of iterations. The general approach and results of the proposed framework are illustrated on an example of advertisement placement.
Ključne reči:
Stochastic programming / Simulation / Scenario generation / Heuristics / Chance-constraintsIzvor:
Information Technology and Control, 2018, 47, 2, 310-320Izdavač:
- Kaunas Univ Technology, Kaunas
DOI: 10.5755/j01.itc.47.2.18712
ISSN: 1392-124X
WoS: 000436452300011
Scopus: 2-s2.0-85049132743
Institucija/grupa
Fakultet organizacionih naukaTY - JOUR AU - Marković, Stefan AU - Vujošević, Mirko AU - Makajić-Nikolić, Dragana PY - 2018 UR - https://rfos.fon.bg.ac.rs/handle/123456789/1846 AB - This paper considers a stochastic programming problem with a number of random parameters in the set of constraints. The method used for solving the problem is the iterative optimization- simulation approach. It consists of two phases: optimization phase, which includes solving a deterministic counterpart of the original chance-constrained problem, and a simulation phase in which the original constraints are checked using Monte Carlo simulation. One iteration corresponds to one scenario. If the decision maker is dissatisfied with the results, a new scenario is generated in which the deterministic values of stochastic parameters are changed in the direction that will provide a more robust solution. The deterministic counterpart in the new scenario is formulated depending on the result of the previous iteration. To that end, different heuristics are considered. The main goal is to provide a good insight on the optimization problem under uncertainty by performing a relatively small number of iterations. The general approach and results of the proposed framework are illustrated on an example of advertisement placement. PB - Kaunas Univ Technology, Kaunas T2 - Information Technology and Control T1 - An Optimization-Simulation Approach to Chance-Constraint Programming EP - 320 IS - 2 SP - 310 VL - 47 DO - 10.5755/j01.itc.47.2.18712 UR - conv_1752 ER -
@article{ author = "Marković, Stefan and Vujošević, Mirko and Makajić-Nikolić, Dragana", year = "2018", abstract = "This paper considers a stochastic programming problem with a number of random parameters in the set of constraints. The method used for solving the problem is the iterative optimization- simulation approach. It consists of two phases: optimization phase, which includes solving a deterministic counterpart of the original chance-constrained problem, and a simulation phase in which the original constraints are checked using Monte Carlo simulation. One iteration corresponds to one scenario. If the decision maker is dissatisfied with the results, a new scenario is generated in which the deterministic values of stochastic parameters are changed in the direction that will provide a more robust solution. The deterministic counterpart in the new scenario is formulated depending on the result of the previous iteration. To that end, different heuristics are considered. The main goal is to provide a good insight on the optimization problem under uncertainty by performing a relatively small number of iterations. The general approach and results of the proposed framework are illustrated on an example of advertisement placement.", publisher = "Kaunas Univ Technology, Kaunas", journal = "Information Technology and Control", title = "An Optimization-Simulation Approach to Chance-Constraint Programming", pages = "320-310", number = "2", volume = "47", doi = "10.5755/j01.itc.47.2.18712", url = "conv_1752" }
Marković, S., Vujošević, M.,& Makajić-Nikolić, D.. (2018). An Optimization-Simulation Approach to Chance-Constraint Programming. in Information Technology and Control Kaunas Univ Technology, Kaunas., 47(2), 310-320. https://doi.org/10.5755/j01.itc.47.2.18712 conv_1752
Marković S, Vujošević M, Makajić-Nikolić D. An Optimization-Simulation Approach to Chance-Constraint Programming. in Information Technology and Control. 2018;47(2):310-320. doi:10.5755/j01.itc.47.2.18712 conv_1752 .
Marković, Stefan, Vujošević, Mirko, Makajić-Nikolić, Dragana, "An Optimization-Simulation Approach to Chance-Constraint Programming" in Information Technology and Control, 47, no. 2 (2018):310-320, https://doi.org/10.5755/j01.itc.47.2.18712 ., conv_1752 .