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Author: search.filters.author.Ritthipakdee, AmaritaSubject: search.filters.subject.Optimization

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    Fuel Costs Optimization for Long-Haul Flight with Refueling Layovers
    (2024-01-01)
    Leenawong, Chartchai
    ;
    Ritthipakdee, Amarita
    This study introduced a mathematical model aimed at optimizing fuel costs for long-haul flights, particularly those requiring refueling. The primary objective was to minimize fuel expenses by considering key factors such as flight routes, aircraft types, refueling points, and refueling quantities. The proposed solution used a 0-1 mixed-integer linear programming (MILP) model, supported by auxiliary variables, to effectively manage the constraints of this optimization problem. The MILP model also considered differences in fuel costs at refueling points, including the departure airport. For validation, a case study was conducted involving a long-haul flight from airport AAA to DDD, with refueling options at airports BBB and CCC. The model effectively determined the most economical flight route, assessed the necessity of refueling, and calculated the required fuel amounts at each refueling location. In summary, this study demonstrated that the proposed model could successfully address the challenges of optimizing fuel costs in long-haul flight.
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    Primate swarm algorithm for continuous optimization problems
    (2017-08-29)
    Ritthipakdee, Amarita
    ;
    Thammano, Arit
    In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the primate. The communication among them is studied and is also a key part in their food finding strategy. Our proposed algorithm developed upon the behavior is tested with twelve standard benchmark functions and most of which converged to the optimal value.
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    A new selection operator to improve the performance of genetic algorithm for optimization problems
    (2013-11-25)
    Ritthipakdee, Amarita
    ;
    Thammano, Arit
    ;
    Premasathian, Nol
    ;
    Uyyanonvara, Bunyarit
    Nature-inspired algorithms, such as Particle swarm optimization (PSO), Ant colony optimization (ACO), and Firefly algorithm, are well known for solving NP-hard optimization problems. They are capable of obtaining optimal solutions in a reasonable time. The algorithm presented in this paper is a combination of a firefly mating concept and genetic algorithm. Genetic algorithm is used as the core of the algorithm while a firefly mating concept is used to compose a new selection operator. The proposed algorithm is tested on four standard benchmark functions. Experimental results have confirmed that the proposed algorithm is not only computationally more efficient than both the original firefly algorithm and the genetic algorithm but also almost always ensure the optimal solutions. © 2013 IEEE.