Application of Genetic Algorithm to Classroom Scheduling with the Aim to Reduce Cooling Load: A Case Study
Keywords:
Classroom Scheduling, Cooling Load, Genetic AlgorithmAbstract
Increasing annual electricity charge of a case-study unit had led the researcher to investigate means to reduce electricity usage due to heat generated by a number of room occupants as well as that from the lighting system and external heat transferred through the walls of the rooms, which in turn affect the operation of the air conditioning system that renders the service to the rooms. The purpose of this research was to develop decision making guidelines for solving classroom scheduling problem of the case-study faculty unit via the application of genetic algorithm. The research goal was to reduce the total cooling load of the air conditioners to the minimum possible value. The results illustrated that the proposed scheduling method could help reduce the total cooling load of the air conditioners when compared to the traditional method conducted by personnel, with an average load reduction of 12.28 percent.
References
Pojana, P., 2011, “Electric Energy Crisis ...The Last Solution Remaining,” EGAT Magazine, 5 (5), pp. 12-15. (In Thai)
Willemen, R.B., 1996, School Timetable Construction: Algorithms and Complexity [Online], Available: https://pure.tue.nl/ws/files/1849715/200211248.pdf.
Sittikorn, K., 2009, A Support System for Classroom Scheduling to Minimize Energy Utilization Index, Master of Engineering Thesis, Khon Kaen University. (In Thai)
Jaojaruek, K., 2011, Air Conditioning [handout], Department of Mechanical Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University. (In Thai)
Janjarassuk, U., 2017, “Parallel Genetic Algorithm for the Stochastic Network Interdiction Problem,” KMUTT Research and Development Journal, 40 (3), pp. 405-414. (In Thai)
Sethanan, K., 2015, Metaheuristics and Applications for Industry, Klungnana Vitthaya Press, Khon Kaen. (In Thai)
Pitakaso, R., 2011, Metaheuristic Approach for Solving Production and Logistics Problems, Technology Promotion Association (Thailand-Japan), Bangkok. (In Thai)
Arora, C.P., 2015, Refrigeration and Air Conditioning, McGraw-Hill, New York.
ASHRAE, 2001, ASHRAE Research: Improving the Quality of Life [Online], Available: https://sovathrothsama.files.wordpress.com/2016/03/ashrae-hvac-2001-funda mentals-handbook.pdf.
Kraithong, R., 2014, A Cooperative Coevolution Genetic Algorithm for Generating Trading Strategies, Master of Science Thesis, National Institute of Development Administration. (In Thai)
Sangsawang, C., Sethanan, K., Fujimoto, T. and Gen, M., 2015, “Metaheuristics Optimization Approaches for Two-stage Reentrant Flexible Flow Shop with Blocking Constraint,” Expert Systems with Applications, 42, pp. 2395–2410.
Rahmani Hosseinabadi, A.A., Vahidi, J., Saemi, B., Sangaiah, A.K. and Elhoseny, M., 2019, “Extended Genetic Algorithm for Solving Open-shop Scheduling Problem,” Soft Computing, 23, pp. 5099–5116.
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