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

Russel, Arya; Taghipour, Sharareh; Sharifi, Mani

doi:10.24200/sci.2023.59998.6542

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

Articles in Press

Document Type : Research Article

Authors

Arya Russel

Sharareh Taghipour

Mani Sharifi

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

10.24200/sci.2023.59998.6542

Abstract

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.

Keywords

Scheduling Optimization

Goal Programming

Genetic Algorithms

Time and Resource Constrained

Aerospace Scheduling Problem

Subjects