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

Abstract

Despite prominent scholarly advancements in operations research, limited literature has been reported on mathematical and heuristic approaches for scheduling 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.