Mathematical Programming and Metaheuristics for Solving Continuous-Time Scheduling Optimization Problems in Low-Volume Low-Variety Production Systems

Document Type : Article


The Reliability, Risk, and Maintenance Research Laboratory (RRMR Lab), Mechanical and Industrial Engineering Department, Toronto Metropolitan University, Toronto, Ontario, Canada


Despite prominent scholarly advancements in the field of operations research, limited literature has been reported on mathematical and heuristic approaches for schedule the low-volume low-variety production systems. This paper proposes a new approach for modeling and solving large-scale sequencing and scheduling problems in Low-Volume Low-variety production systems. The proposed non-linear mathematical programming models and genetic algorithms are subject to time and resource constraints, aimed at maximizing the number of activities completed in-station or intended to minimize the positive deviation to the aspiring time and resources budgets, in scenarios where the allocated work package must be completed in-station. The proposed algorithms are compatible with discrete and continuous-time scheduling problems and are found to be effective in modeling characteristics and constraints inherent in Low-Volume, Low-Variety production systems. To validate the proposed models, a real-world case study of a work center in the final assembly line of a private jet aircraft is conducted.


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