Hamiden Khalifa
Abstract
Investment problem is one of the most important and interesting optimization problems. This problem becomes more difficult when we deal with it in an uncertain and vague environment with chaos data. This paper attempts to study the investment problem with uncertain return data. These data are represented ...
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Investment problem is one of the most important and interesting optimization problems. This problem becomes more difficult when we deal with it in an uncertain and vague environment with chaos data. This paper attempts to study the investment problem with uncertain return data. These data are represented as chaos numbers. Dynamic programming is applied to obtain the optimal policy and the corresponding best return. Finally, a numerical example is given to illustrate the utility, effectiveness, and applicability of the approach to the problem.
Fuzzy optimization
Seyedeh Maedeh Mirmohseni; Seyed Hadi Nasseri; Mohammad Hossein Khaviari
Abstract
In this paper, dynamic programming for sequencing weighted jobs on a single machine to minimizing total tardiness is focused, to significance of fuzzy numbers field, and importance of that for decision makers who are facing on uncertain data, combination of dynamic programming and fuzzy numbers is applied. ...
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In this paper, dynamic programming for sequencing weighted jobs on a single machine to minimizing total tardiness is focused, to significance of fuzzy numbers field, and importance of that for decision makers who are facing on uncertain data, combination of dynamic programming and fuzzy numbers is applied. A random scheduling problem with fuzzy processing times is given and solved. In addition, algorithm consuming time during solving same category problem and different sizes are analyzed that for large problem CPU time usage is extremely unaffordable. Therefore demonstration of near-exact heuristic method such as Genetic Algorithm (GA) appears. In this paper sufficient discussion around solving this kind of problems and their algorithms analysis and a combination between Dynamic Programming (DP) and genetic algorithm as a newly born method is proposed that stand on DP performance and genetic algorithm search power, and finally comparison on the recent developed method has been held. Then this method can deal with real-world problem easily. Thus, decision makers actually can use this modification of dynamic programming for coping with un-crisp problem.
