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020			_aTHE0010197 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKPM .W37 2025 r Thesis
100	0		_aWasif Ullah, _eauthor.
245	1	0	_aTeaching learning-based optimization for cost-oriented hybrid flow shop scheduling / _cWasif Ullah
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		1	_c© 2025
300			_axii, 125 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-COM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing and Mechatronic Engineering Technology
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2025
504			_aIncludes bibliographical references
520	3		_aProduction scheduling is a strategic process of organizing the execution of jobs on available resources to optimize certain optimization objectives. One of the important scheduling problem is the cost-oriented hybrid flow shop (CHFS) scheduling problem, which involves optimizing the scheduling of jobs across multiple stages to minimize the scheduling-related costs. Despite the significance of cost optimization in CHFS scheduling, there is a lack of comprehensive studies that address all major cost components using efficient optimization algorithms. For this purpose, a comprehensive cost model was developed that includes four cost elements: labor costs, machine energy consumption costs, preventive maintenance costs, and late penalty costs. Then a Greedy-assisted Teaching Learning-Based Optimization (GTLBO) algorithm was proposed to optimize the developed CHFS model. In GTLBO algorithm the initialization process was hybridized with Greedy algorithm, where 10% of initial population was generated by Greedy algorithm, while 90% randomly generated as usual. Afterward, a computational experiment was conducted to evaluate the performance of GTLBO algorithm by choosing certain comparative algorithms. The experiment utilized a dataset of 12 benchmark test problems defined by Carlier and Neron and was conducted using MATLAB version 2022b. The Wilcoxon rank-sum test confirmed statistically significant improvements (p value < 0.05) in most scenarios. The experiment and Wilcoxon test revealed that GTLBO outperformed other algorithms in optimizing the CHFS problems. For the practical validation of the CHFS model and the GTLBO algorithm, two real-case study problems were analyzed by comparing the optimized schedule costs obtained using GTLBO with the costs of the original schedules. In case study 1, the proposed method reduced the total scheduling cost from RM 53,314.00 to RM 50,854.88, achieving a cost reduction of approximately 4.6%. In case study 2, the cost was reduced from RM 214.35 to RM 208.92, resulting in a 2.5% improvement. This research can help the manufacturers with HFS scheduling setups struggling to minimize their production expenses. Future research directions include establishing multi-objective CHFS scheduling models and developing hybridized optimization algorithms to optimize these models.
610	2	0	_aFaculty of Manufacturing and Mechatronic Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c103816 _d103822