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Sequencing and Scheduling Optimization in Low-Volume Low-Variety Production Systems

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posted on 25.05.2022, 17:09 by Arya Russell

Despite prominent scholarly advancements in scheduling optimization approaches for a wide range of production systems, limited research has been reported on sequencing and scheduling optimization strategies in Low-Volume Low-Variety production systems. This dissertation fills the gap in the current literature through the formulation and the proposal of a suite of mathematical programming models and heuristic algorithms, capturing the unique characteristics and constraints inherent in such production systems. In the first section of this dissertation a suite of mixed-integer multi-objective linear mathematical programming models are proposed for solving discrete-time single work center scheduling problems, distinguished by a key decision criterion of permitting or prohibiting the traveling of incomplete activities. It was evident through personal observations however, that there exist scenarios where resources are shared between parallel work centers, which yielded to further research in the use of shared resource pools in multi-parallel work center scheduling problems. A novel suite of mathematical programming models are proposed for solving single and multi-parallel work center scheduling problems with shared or dedicated resources. The mathematical programming models formulated in this section are modular, signifying that constraints can be added or removed without jeopardizing the integrity of the mathematical models. The proposed optimization models were validated and verified through a real-world case study where significant cost savings in form of resource requirements are realized through the integration of shared resource pools. It is often the case however, that activity processing times and planning horizon are not discrete. To tackle continuous-time work center scheduling problems a novel suite of mathematical programming models is formulated and proposed in the final section of this dissertation, as well as two new genetic algorithms for solving large-scale scheduling problems. The proposed mathematical programming models and metaheuristics are aimed at optimizing the production schedule as well as activity execution sequence to minimize overall cost and resource requirements. The optimization models proposed through this dissertation are validated and verified through a real-world case study of the final assembly line of a narrow body private aircraft, where the problems were solved to optimality.

History

Language

English

Degree

Doctor of Philosophy

Program

Mechanical and Industrial Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Dissertation

Thesis Advisor

Sharareh Taghipour