Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two‐stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. 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 model. The results demonstrate the several advantages of modular designs in meeting product demands. A pareto‐optimal curve for minimizing the objectives of expected cost and downside risk is obtained.
Identifying sustainable chemical processes often depends on the choice of enabling materials that directly influence the overall performance. Matching property targets while incorporating adequate process knowledge is essential for optimal material selection. Multi‐scale decisions need to be taken simultaneously to determine the optimal process configurations, operating conditions, and material structures. Integrating molecular to process scale decisions within an equation‐oriented optimization framework leads to large‐scale mixed‐integer nonlinear programs (MINLP). Over the years, several solution approaches have been suggested to tackle this issue. Here, the current state‐of‐the‐art in the field of computer‐aided molecular and process design (CAMPD) is discussed and key challenges and open questions are highlighted that may stimulate future research.
more » « less- Award ID(s):
- 1943479
- NSF-PAR ID:
- 10442532
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Chemie Ingenieur Technik
- Volume:
- 95
- Issue:
- 3
- ISSN:
- 0009-286X
- Page Range / eLocation ID:
- p. 315-333
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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