Scheduling optimization of cellular flowshop with sequence dependent setup times

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dc.contributor.authorIbrahem, Al-mehdi Mohamed M.
dc.contributor.examiningcommitteeRichards, Norman (Mechanical Engineering) Appadoo, Srimantoorao S. (Management) Abdul-Kader, Walid (Mechanical, Automotive and Materials Engineering, University of Windsor)en_US
dc.contributor.supervisorElMekkawy, Tarek (Mechanical Engineering) Peng, Qingjin (Mechanical Engineering)en_US
dc.date.accessioned2015-09-08T20:09:15Z
dc.date.available2015-09-08T20:09:15Z
dc.date.issued2014-04-30en_US
dc.degree.disciplineMechanical Engineeringen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractIn cellular manufacturing systems, minimization of the completion time has a great impact on the production time, material flow, and productivity. An effective scheduling is crucial to attaining the advantages of cellular manufacturing systems. This dissertation attempts to solve the Flowshop Manufacturing Cell (cellular flowshop) Scheduling Problem with Sequence Dependent Setup Times (FMCSP with SDSTs) considering two performance measures: the total flow time as a mono objective, and the makespan and total flow time combined as a bi-criteria scheduling problem. The proposed problem is known to be the NP-hard problem because of its complexity. Several metaheuristic algorithms based on Genetic Algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) are developed for scheduling part families as well as jobs within each part family for FMCSP with SDSTs to minimize the total flow time. A local search method based on SA combined with PSO (named as PSO-SA) is proposed to enhance the intensification and improve the quality of the solution obtained by pure PSO. The effectiveness and efficiency of the proposed metaheuristics are evaluated based on the Relative Percentage Deviation (RPD) from its lower bound, and the robustness. Results indicate PSO-SA is performed similar to best available algorithms for small and medium size test problems. Yet, there is a very small deviation from best results for large problems. A Multi-objective Particle Swarm Optimization (MPSO) and a Multi-objective Simulated Annealing (MOSA) Algorithm are further proposed to solve the bi-criteria optimization problem to minimize the total flow time and makespan simultaneously. An improved PSO is combined with Threshold Acceptance (TA) algorithm to improve effectiveness of the proposed MPSO (named as IMPSO-TA) for the convergence of the obtained Pareto Front. The proposed algorithms are evaluated using several Quality Indicators (QI) measures for multiobjective optimization problems. The proposed algorithms can generate approximated Pareto Fronts in a reasonable CPU time. The proposed IMPSO-SA outperforms MOSA algorithm in terms of CPU time and minimizing the objective functions.en_US
dc.description.noteOctober 2015en_US
dc.identifier.citationA.-m. Ibrahem, T. Elmekkawy, and Q. Peng, “Robust Metaheuristics for Scheduling Cellular Flowshop with Family Sequence-Dependent Setup Times,” Procedia CIRP, vol. 17, pp. 428–433, 2014.en_US
dc.identifier.urihttp://hdl.handle.net/1993/30730
dc.language.isoengen_US
dc.publisherProceedings of the 47th CIRP Conference on Manufacturing Systemsen_US
dc.rightsopen accessen_US
dc.subjectCellular manufacturing flowshop, Sequencing-dependent setup, Total flow time, Makespan, Genetic algorithm, Particle Swarm Optimization, Bi-criteria scheduling problemen_US
dc.titleScheduling optimization of cellular flowshop with sequence dependent setup timesen_US
dc.typedoctoral thesisen_US
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