Agile manufacturing is concerned with thriving in prevailing market conditions by quickly introducing new or modified products. This research deals with the scheduling of an agile manufacturing system (AMS), which performs both machining and assembly, with the objective of minimizing the makespan. The AMS allows the production of high varieties of modular products in small batches and at low costs. This problem is difficult to solve optimally and was solved in literature by heuristic algorithms. In the current research, four novel, genetic algorithms and simulated annealing-based, heuristics – General Genetic Algorithm, General Simulated Annealing, Heuristic Assisted Genetic Algorithm, and Heuristic Assisted Simulated Annealing – are developed to address this scheduling problem. A 23 factorial experiment, replicated twice, is conducted to compare the performance of the proposed and existing heuristics and identify the significant factors that affect the resulting percentage deviation from the lower bound and the frequency of resulting in the best solution. The results show the superiority of the developed heuristics to those existing in literature in addition to identifying the significant factors and interactions.


School of Sciences and Engineering


Mechanical Engineering Department

Degree Name

MS in Mechanical Engineering

Date of Award


Online Submission Date


First Advisor

Lotfi K. Gaafar

Committee Member 1

Hamdy S. Elwany

Committee Member 2

Adel Shalaby

Committee Member 3

Maher Y.A. Younan

Document Type



143 p


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