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Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

機譯：使用粒子群算法的聯(lián)合能源管理和生產(chǎn)計劃成本最小化

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摘要

Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning.;Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model.;Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach.;Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities.;The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of $ 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of $ 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.
機譯：生產(chǎn)成本占制造設(shè)備總成本的最大份額。隨著美國工業(yè)領(lǐng)域的競爭越來越激烈，制造商正在尋找更具成本和資源效率的工作方式。運營管理和生產(chǎn)計劃已顯示出顯著降低制造成本和提高系統(tǒng)穩(wěn)定性的能力。在實施與運營相關(guān)的決策和計劃時，最有效的兩個領(lǐng)域是維護和能源。不幸的是，目前結(jié)合兩者的研究是有限的。此外，這些研究沒有考慮聯(lián)合能源和維護驅(qū)動的生產(chǎn)計劃的參數(shù)域和優(yōu)化。因此，研究了考慮維護和能源的生產(chǎn)計劃方法。提出了兩種模型來實現(xiàn)全面的運營策略。第一個是聯(lián)合能源和維護生產(chǎn)計劃模型。第二個是考慮維護，能源和生產(chǎn)的單件成本模型。擬議的方法將涉及使用時間的電力需求響應(yīng)程序，緩沖和保持容量，電站可靠性，生產(chǎn)率，電站額定功率等。實際上，調(diào)度問題可用于確定聯(lián)合能源，維護和生產(chǎn)調(diào)度。同時，零件成本模型可用于：（1）通過更改關(guān)鍵生產(chǎn)系統(tǒng)參數(shù)來測試獲得的最佳生產(chǎn)計劃的敏感性及其相應(yīng)的節(jié)?。?（2）使用聯(lián)合能源，維護和生產(chǎn)計劃模型確定最佳的系統(tǒng)參數(shù)組合。此外，對系統(tǒng)參數(shù)進行因素分析，并評估在可變參數(shù)條件下生產(chǎn)計劃的相應(yīng)性能。 。此外，還將討論在生產(chǎn)計劃框架中納入維護和能源參數(shù)決策的參數(shù)優(yōu)化準(zhǔn)則。采用改進的粒子群優(yōu)化求解技術(shù)來解決所提出的調(diào)度問題。詳細(xì)描述了該算法，并將其與遺傳算法進行了比較。通過案例研究來說明使用所提出的模型的好處和粒子群優(yōu)化方法的有效性。進行了數(shù)值實驗并進行了分析，以測試所提出模型的有效性。與基線調(diào)度方案相比，所提出的調(diào)度策略可以節(jié)省約19％至27％的零件成本。通過根據(jù)每零件成本模型優(yōu)化關(guān)鍵生產(chǎn)系統(tǒng)參數(shù)，基準(zhǔn)線方案可以節(jié)省約20％至35％的零件成本。當(dāng)系統(tǒng)參數(shù)優(yōu)化與建議的調(diào)度問題集成在一起時，這些節(jié)省將進一步增加42％到55％。使用這種方法，對零件成本最有影響的參數(shù)是生產(chǎn)的額定功率，生產(chǎn)率和初始機器可靠性。;與遺傳算法相比，所采用的改進的粒子群優(yōu)化算法具有更大的多樣性和探索性聯(lián)合模型，從而可以在確定最佳調(diào)度方面提高計算效率。而遺傳算法可以在2,300秒的計算工作量上實現(xiàn)2,279.63美元的解決方案質(zhì)量。相比之下，所提出的粒子群優(yōu)化算法在不到遺傳算法所需計算工作量一半的情況下，實現(xiàn)了2,167.26美元的解決方案質(zhì)量。

著錄項

  • 作者

    Shah, Rahul H.;

  • 作者單位

    University of Illinois at Chicago.;

  • 授予單位 University of Illinois at Chicago.;
  • 學(xué)科 Industrial engineering.;Energy.
  • 學(xué)位 M.S.
  • 年度 2017
  • 頁碼 71 p.
  • 總頁數(shù) 71
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類 遙感技術(shù);
  • 關(guān)鍵詞

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