Optimal supply chain design and operations under multi-scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

In today’s global economy, the oil industry plays a vital role and has an effect on most of countries within leading business environments, particularly in oil producing countries. To deal with the complexity of the crude oil supply network, a mathematical programming model is developed to formulate a crude oil supply chain. A robust optimization model is developed to maximize the profitability of the entire chain and take uncertainties of price and demand into account. The results show that according to the real case study data the robust optimization technique will increase the profitability of the crude oil supply chain.

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Resilient supply chain design and operations with decision‐dependent uncertainty using a data‐driven robust optimization approach

AIChE Journal ◽

10.1002/aic.16513 ◽

2019 ◽

Vol 65 (3) ◽

pp. 1006-1021 ◽

Cited By ~ 15

Author(s):

Shipu Zhao ◽

Fengqi You

Keyword(s):

Supply Chain ◽

Robust Optimization ◽

Supply Chain Design ◽

Data Driven ◽

Optimization Approach ◽

Decision Dependent Uncertainty ◽

Resilient Supply Chain

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Data-driven robust optimization for wastewater sludge-to-biodiesel supply chain design

Computers & Industrial Engineering ◽

10.1016/j.cie.2019.07.001 ◽

2020 ◽

Vol 139 ◽

pp. 105944 ◽

Cited By ~ 10

Author(s):

Shayan Mohseni ◽

Mir Saman Pishvaee

Keyword(s):

Supply Chain ◽

Robust Optimization ◽

Wastewater Sludge ◽

Supply Chain Design ◽

Data Driven

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Robust Optimization for Smart Manufacturing Planning and Supply Chain Design in Chemical Industry

Smart Manufacturing Innovation and Transformation - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-4666-5836-3.ch002 ◽

2014 ◽

pp. 21-37 ◽

Cited By ~ 1

Author(s):

Tianxing Cai

Keyword(s):

Supply Chain ◽

Robust Optimization ◽

Chemical Industry ◽

Programming Model ◽

Supply Chain Design ◽

Mixed Integer ◽

Smart Manufacturing ◽

Manufacturing Planning ◽

Economic Markets ◽

Uncertainty Parameters

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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