Optimization of Scheduling in Manufacturing Systems
Summary
Optimization of scheduling in manufacturing systems involves the allocation of jobs, machines and resources over time to meet objectives such as minimising completion time, maximising throughput and reducing energy consumption. These scheduling problems—ranging from classical flowshop and job shop configurations to flexible and dynamic environments—are combinatorial and generally NP-hard, requiring advanced methods to obtain high-quality solutions within reasonable time frames. Traditional exact algorithms struggle with large-scale instances, prompting widespread adoption of heuristics, metaheuristics and, more recently, data-driven approaches. Developments in reinforcement learning, simulation optimisation and hybrid frameworks have enhanced real-time adaptability under stochastic disturbances, while multi-objective models now explicitly balance service levels and sustainability. Practical applications span automotive assembly, electronics production and chemical processing, where efficient scheduling directly impacts operational cost, environmental footprint and global supply-chain resilience.
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Optimization of Scheduling in Manufacturing Systems publication trend
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Technical terms
Makespan: Total elapsed time from the start of the first job to the completion of the last job in a schedule.
Flexible job shop scheduling: Allocation of jobs to machines when multiple routing options exist for each job.
Dynamic scheduling: Real-time adjustment of schedules in response to unforeseen changes such as machine breakdowns or new job arrivals.
Simulation optimisation: Integration of simulation models with optimisation techniques to evaluate and improve decision variables under realistic operational scenarios.
Pareto frontier: Set of non-dominated solutions in a multi-objective context, representing optimal trade-offs between conflicting objectives.
References
- Dynamic Scheduling for Large-Scale Flexible Job Shop Based on Noisy DDQN. International Journal of Network Dynamics and Intelligence (2023).
- Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap. Journal of Industrial Information Integration (2024).
- Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research (2016).
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