000			04338ntm a2200337 i 4500
999			_c103111 _d103117
003			MY-KuUP
005			20251125111114.0
006			t||||fr|||| 000 0
007			ta
008			250814t20252025my a|||fr|||| 000 0 eng d
020			_qhardback _aTHE0010149 (Local)
040			_beng _cUMPSA _erda _aUMPSA
090			_aFTKPM .H33 2025 r Thesis
100	0		_aMohd Abdul Hadi Bin Osman, _eauthor.
245	1	0	_aOptimization of hybrid flowshop scheduling problem with energy consideration using moth flame - sine cosine based algorithm / _cMohd Abdul Hadi Bin Osman
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axvii, 203 pages : _billustration ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing & Mechatronics Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang Al Sultan Abdullah – 2025
504			_aIncludes bibliographical references
520	3		_aThis thesis investigates the Hybrid Flowshop Scheduling Problem with Energy Consideration (HFSP-EC), which merges permutation and parallel flowshop scheduling across multiple machines in production. While traditional methods often prioritize makespan minimization, the essential aspect of energy efficiency is frequently neglected in manufacturing. Although some studies have begun addressing energy consumption, this area remains inadequately examined. In addition, existing optimization algorithms face difficulties in solving scheduling problems to find the optimal makespan and energy consumption simultaneously. It leads to solutions that are not the best and fail to optimize these critical factors effectively. This drawback also hinders the development of a robust scheduling strategy to adapt to the complexities of a real manufacturing industry environment, such as customer demand and fluctuating energy consumption tariff rates. This thesis identifies these gaps and proposes a novel hybrid optimization algorithm, combining the Moth Flame Optimizer with the Sine Cosine Algorithm (MFO-SCA), to achieve optimal makespan and energy consumption within the HFSP-EC framework. By utilizing the exploratory strengths of MFO and the intensification capabilities of SCA, MFO-SCA successfully optimizes both makespan and energy use. The performance of MFO-SCA was rigorously tested using 12 benchmark problems and compared to Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Arithmetic Optimization Algorithm (AOA), Genetic Algorithm (GA), MFO, Particle Swamp Optimization (PSO), SCA, Whale Optimization Algorithm (WOA), and MFO-AOA establishing a robust foundation for comparison with existing methods. Results indicate that MFO-SCA consistently outperforms competing algorithms, with an average improvement of 5.42% in makespan and 7.13% in energy consumption. A comprehensive case study demonstrated the practical applicability of the HFSP-EC model and MFO-SCA in real manufacturing settings, yielding an 8.06% reduction in makespan and a 5.63% decrease in energy consumption. These findings affirm the robustness and adaptability of MFO-SCA in energy-sensitive contexts. This thesis emphasizes the necessity of integrating energy considerations into scheduling methodologies and advocates for sustainable manufacturing practices. The results promote the adoption of advanced optimization techniques that enhance operational performance while minimizing energy use. By linking enhanced scheduling strategies with environmental sustainability, this thesis sets the stage for future research investigating the relationship between manufacturing efficiency and ecological responsibility. In conclusion, this study represents a significant step toward fostering sustainable scheduling practices. It provides valuable insights for scholars and industry professionals seeking to improve manufacturing sustainability. Future research must build upon these findings to advance energy-efficient practices within the industry.
610	2	0	_aFaculty of Manufacturing & Mechatronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aThesis _xDissertations
942			_2lcc _cTHESIS