Tika Bunnag

Work place: Rattanakosin College for Sustainable Energy and Environment (RCSEE), Rajamangala University of Technology Rattanakosin, Nakhon Pathom, Thailand

E-mail: tbunnag@hotmail.com


Research Interests: Earth & Environmental Sciences, Earth Sciences


Tika Bunnag (1967-), male, Bangkok, Thailand, supervisor for Ph.D. candidate, he received the B.Eng. (Mechanical), M. Eng. (Thermal Technology) and Ph.D. (Energy Technology) from King Mongkut’s University of Technology Thonburi, in 1993, 1995 and 2003 respectively. In 2011, he joined the Rattanakosin College for Sustainable Energy and Environment, Rajamangala University of Technology Rattanakosin, Thailand, as a Vice Director and Head of Technology Management Research center. His representative published articles lists as follow: Experimental study of a Roof Solar Collector towards the Natural Ventilation of New Habitations (World Renewable Energy Congress, 1996), Experimental Investigation of Free Convection in an Open Ended Vertical Rectangular Channel (Comples journal, 2002), Experimental Investigation of Free Convection in Open Ended Inclined Rectangular Channel Heated from the Top (The Internal Journal of Ambient Energy, 2003), Improvement of Thermal Performance of Double Glass Skylight in Thailand (Asia-Pacific Regional Conference of International Solar Energy Society, 2004) and The Reduce of Building Cooling Load from Double Glass Skylight in Bangkok (Sustainable Energy and Green Architecture III, 2011) etc.
His research interests include Advance Energy Management, Magnetic Free Energy Generator, Green Buildings, Zero Waste Management and Sustainable Energy Navigation.

Author Articles
Traveling Transportation Problem Optimization by Adaptive Current Search Method

By Supaporn Suwannarongsri Tika Bunnag Waraporn Klinbun

DOI: https://doi.org/10.5815/ijmecs.2014.05.05, Pub. Date: 8 May 2014

The adaptive current search (ACS) is one of the novel metaheuristic optimization search techniques proposed for solving the combinatorial optimization problems. This paper aimed to present the application of the ACS to optimize the real-world traveling transportation problems (TTP) of a specific car factory. The total distance of the selected TTP is performed as the objective function to be minimized in order to decrease the vehicle’s energy. To perform its effectiveness, four real-world TTP problems are conducted. Results obtained by the ACS are compared with those obtained by genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS can provide very satisfactory solutions superior to other algorithms. The minimum total distance and the minimum vehicle’s energy of all TTP problems can be achieved by the ACS with the distant error of no longer than 3.05%.

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Energy Resource Management of Assembly Line Balancing Problem using Modified Current Search Method

By Supaporn Suwannarongsri Tika Bunnag Waraporn Klinbun

DOI: https://doi.org/10.5815/ijisa.2014.03.01, Pub. Date: 8 Feb. 2014

This paper aims to apply a modified current search method, adaptive current search (ACS), for assembly line balancing problems. The ACS algorithm possesses the memory list (ML) to escape from local entrapment and the adaptive radius (AR) mechanism to speed up the search process. The ACS is tested against five benchmark unconstrained and three constrained optimization problems compared with genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms and provides superior results. The ACS is used to address the number of tasks assigned for each workstation, while the heuristic sequencing (HS) technique is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The workload variance and the idle time are performed as the multiple-objective functions. The proposed approach is tested against four benchmark ALB problems compared with the GA, TS and CS. As results, the ACS associated with the HS technique is capable of producing solutions superior to other techniques. In addition, the ACS is an alternative potential algorithm to solve other optimization problems.

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