(612f) Supply Chain Optimization for Modular Manufacturing with Production Feasibility Analysis Under Uncertainty | AIChE

(612f) Supply Chain Optimization for Modular Manufacturing with Production Feasibility Analysis Under Uncertainty


Badejo, O. - Presenter, University of Delaware
Ierapetritou, M., University of Delaware
Bhosekar, A., Rutgers University
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.


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