Over the last decades, energy efficiency has received numerous attentions from manufacturing companies since energy cost has been climbing up along with environmental awareness. Therefore, energy-efficient scheduling of production systems and renewable energy receive more focuses from enterprises, especially energy-intensive manufacturing companies, such as glass, paper, iron, steel, or basic chemicals production companies with aims to enhance energy efficiency, reduce energy cost, and preserve the environment. Inspired by a practical glass production case study, this dissertation investigates stochastic programming for two-stage hybrid flow shop scheduling problem satisfying two objectives. In the first phase, the optimal schedules are provided to minimize the make-span while the next phase decides the energy supply and trading decisions to minimize average total cost of energy consumption under different on-site renewable generation scenarios. In order to fulfill the power demand from production, supplied electricity can be acquired from the main grid or discharged from energy storage system (i.e., batteries). To solve this problem, the tailor-made approach a tabu search algorithm (TS) and an ant colony optimization (ACO) algorithm are integrated with sample average approximation (SAA) method in order to come up with sets of Pareto optimal solutions. Lastly, the real-world case study about glass production in Taiwan is conducted. Our results prove the effectiveness of the proposed method.

Over the last decades, energy efficiency has received numerous attentions from manufacturing companies since energy cost has been climbing up along with environmental awareness. Therefore, energy-efficient scheduling of production systems and renewable energy receive more focuses from enterprises, especially energy-intensive manufacturing companies, such as glass, paper, iron, steel, or basic chemicals production companies with aims to enhance energy efficiency, reduce energy cost, and preserve the environment. Inspired by a practical glass production case study, this dissertation investigates stochastic programming for two-stage hybrid flow shop scheduling problem satisfying two objectives. In the first phase, the optimal schedules are provided to minimize the make-span while the next phase decides the energy supply and trading decisions to minimize average total cost of energy consumption under different on-site renewable generation scenarios. In order to fulfill the power demand from production, supplied electricity can be acquired from the main grid or discharged from energy storage system (i.e., batteries). To solve this problem, the tailor-made approach a tabu search algorithm (TS) and an ant colony optimization (ACO) algorithm are integrated with sample average approximation (SAA) method in order to come up with sets of Pareto optimal solutions. Lastly, the real-world case study about glass production in Taiwan is conducted. Our results prove the effectiveness of the proposed method.

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