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Robust Optimization for Smart Manufacturing Planning and Supply Chain Design in Chemical Industry
Abstract
The depletion of natural resource, the complexity of economic markets and the increased requirement for environment protection have increased the uncertainty of chemical supply and manufacturing. The consequence of short-time material shortage or emergent demand under extreme conditions, may cause local areas to suffer from delayed product deliveries and manufacturing disorder, which will both cause tremendous economic losses. In such urgent events, robust optimization for manufacturing planning and supply chain design in chemical industry, targeting the smart manufacturing, should be a top priority. In this chapter, a novel methodology is developed for robust optimization of manufacturing planning and supply chain design in chemical industry, which includes four stages of work. First, the network of the chemical supply chain needs to be characterized, where the capacity, quantity, and availability of various chemical sources is determined. Second, the initial situation under steady conditions needs to be identified. Then, the optimization is conducted based on a developed MILP (mixed-integer linear programming) model in the third stage. Finally, the sensitivity of the manufacturing and transportation planning with respect to uncertainty parameters is characterized by partitioning the entire space of uncertainty parameters into multiple subspaces. The efficacy of the developed methodology is demonstrated via a case study with in-depth discussions.
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