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Supply Chain Optimization for Modular Manufacturing with Production Feasibility Analysis Under Uncertainty

  • Type:
    Conference Presentation
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    AIChE Member Credits 0.5
    AIChE Members $19.00
    AIChE Graduate Student Members Free
    AIChE Undergraduate Student Members Free
    Non-Members $29.00
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 11, 2021
  • Duration:
    19 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.50

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In the current market conditions, the need to systematically address uncertainty is re-emphasized with increased global competition, volatility in market conditions, and shift in economic sentiments resulting from abrupt changes such as COVID-19 1–3. Consequently, enterprises are forced to re-assess their strategies to balance responsiveness (customers' satisfaction) and efficiency (profitability). It is worth noting that responsiveness and efficiency are conflicting and need to be carefully balanced using a multi-objective strategy 4,5. Coupling modular manufacturing with a multi-objective model offers a promising direction to solving the problem. Modular manufacturing adds flexibility in planning decisions by offering standardized designs, which lower capital cost per unit of equipment due to the economy of mass production and reduce construction time 6,7. Data-driven methods can be used to approximate the feasible operating regions of each module, which can be incorporated as constraints into a supply chain model 8,9. To incorporate risk in decision making model, the objective should simultaneously maximize returns and maintain high customer service level.

In this work, simultaneous strategic and tactical decisions are considered under demand uncertainty, using a risk averse model. The problem is formulated as a multiperiod planning model, which optimizes supply chain cross functional drivers – production facilities (location and capacity), inventory, transportation - as well as production amount. Flexibility of facilities capacity was increased by using modular strategy. A mixed-integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables representing the supply chain network's material flow. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote risk-averse decisions, a downside risk measure is incorporated in the model4,5. The results demonstrate the several advantages of modular designs in meeting product demands. Finally, a Pareto optimal curve for minimizing the objectives of expected cost and downside risk is obtained to guide the decision making.




Bibliography

  1. Queiroz MM, Ivanov D, Dolgui A, Fosso Wamba S. Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann Oper Res. Published online June 16, 2020. doi:10.1007/s10479-020-03685-7
  2. Dias LS, Ierapetritou MG. From process control to supply chain management: An overview of integrated decision making strategies. Comput Chem Eng. 2017;106:826-835. doi:10.1016/j.compchemeng.2017.02.006
  3. Ivanov D, Dolgui A, Sokolov B, Ivanova M. Literature review on disruption recovery in the supply chain. Int J Prod Res. 2017;55(20):6158-6174. doi:10.1080/00207543.2017.1330572
  4. You F, Grossmann IE. Integrated multi-echelon supply chain design with inventories under uncertainty: MINLP models, computational strategies. AIChE J. Published online 2009:NA-NA. doi:10.1002/aic.12010
  5. Sahay N, Ierapetritou M. Flexibility assessment and risk management in supply chains. AIChE J. 2015;61(12):4166-4178. doi:10.1002/aic.14971
  6. Arora A, Li J, Zantye MS, Hasan MMF. Design standardization of unit operations for reducing the capital intensity and cost of small-scale chemical processes. AIChE J. 2020;66(2):e16802. doi:https://doi.org/10.1002/aic.16802
  7. Allen RC, Allaire D, El-Halwagi MM. Capacity Planning for Modular and Transportable Infrastructure for Shale Gas Production and Processing. Ind Eng Chem Res. 2019;58(15):5887-5897. doi:10.1021/acs.iecr.8b04255
  8. Bhosekar A, Ierapetritou M. Modular Design Optimization using Machine Learning-based Flexibility Analysis. J Process Control. 2020;90:18-34. doi:10.1016/j.jprocont.2020.03.014
  9. Bhosekar A, Ierapetritou M. A framework for supply chain optimization for modular manufacturing with production feasibility analysis. Comput Chem Eng. Published online November 16, 2020:107175. doi:10.1016/j.compchemeng.2020.107175
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Checkout

Checkout

Do you already own this?

Pricing


Individuals

AIChE Member Credits 0.5
AIChE Members $19.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $29.00
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