In this paper, we develop a dynamic control algorithm for production scheduling that considers machine capacity and idle time controls and aims at satisfying time related production demand and reducing energy consumption in a unified manner. A mixed integer nonlinear programming (MINLP) model is developed to determine job arrival sequence for a machine and machine capacity while minimizing resulting costs of just-in-time production, machine repair, and energy consumption during machine idle time and nominal processing. A dynamic control algorithm based on feedback control of continuous variables is also developed to determine an energy-efficient production schedule with proper machine capacity and turn-off schedules. Energy, JIT, and maintenance costs of the proposed approach are examined using real energy and machining parameters of a HAAS VF0 milling machine. Algorithmic performance of the proposed dynamic control approach is compared to other heuristics, adaptive large neighborhood search (ALNS), and genetic algorithm (GA) with a speed optimization (SO) component. Experimental results show that the proposed algorithm improved performance by an average 10.0 ~ 93.8% and 0.52 ~ 22.9% compared to GA and ALNS with the SO module, respectively.
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All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
- Management of Technology and Innovation