Thammano, Arit

Loading...
Thumbnail Image
Preferred name
Thammano, Arit
Alternative Name
Thammano, A.
Thummano, Arit
Main Affiliation
Email
arit.th@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A hybrid evolutionary algorithm for the resource-constrained project scheduling problem
    (2012-12-01)
    Thammano, Arit
    ;
    Phu-ang, Ajchara
    The resource-constrained project scheduling problem (RCPSP) is an NP-hard optimization problem. RCPSP is one of the most important and challenging problems in the project management field. In the past few years, many researches have been proposed for solving the RCPSP. The objective of this problem is to schedule the activities under limited resources so that the project makespan is minimized. This paper proposes a new algorithm for solving RCPSP that combines the concepts of negative selection mechanism of the biologic immune system, simulated annealing algorithm (SA), tabu search algorithm (TS) and genetic algorithm (GA) together. The performance of the proposed algorithm is evaluated and compared to current state-of-the-art metaheuristic algorithms. In this study, the benchmark data sets used in testing the performance of the proposed algorithm are obtained from the project scheduling problem library. The performance is measured in terms of the average percentage deviation from the critical path lower bound. The experimental results show that the proposed algorithm outperforms the state-of-the-art metaheuristic algorithms on all standard benchmark data sets. © 2012 ISAROB.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Memetic algorithm based on marriage in honey bees optimization for flexible job shop scheduling problem
    (2017-12-01)
    Phu-ang, Ajchara
    ;
    Thammano, Arit
    This paper proposes a new memetic algorithm based on marriage in honey bees optimization (MBO) algorithm for solving the flexible job shop scheduling problem. The proposed algorithm introduces four new features to the standard MBO algorithm, mainly to get the search to move away from the local optimum: (1) the use of a harmony memory to improve the quality of initial population; (2) the introduction of a new crossover operator called triparental crossover to help increase the genetic diversity in the offspring; (3) the addition of adaptive crossover probability (P <inf>c</inf>) and mutation probability (P <inf>m</inf>) to remove the need for users to specify these probabilities; and (4) the incorporation of simulated annealing algorithm embedded with a set of heuristics to enhance the local search capability. The proposed algorithm was evaluated and compared to several state-of-the-art algorithms in the literature. The experimental results on five sets of standard benchmarks show that the proposed algorithm is very effective in solving the flexible job shop scheduling problems.