Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis

Badejo, Oluwadare; Ierapetritou, Marianthi G.

doi:10.1016/j.compchemeng.2022.107759

Cited by 10 publications

(7 citation statements)

References 57 publications

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Section: Analytical Application Of the Expressionsmentioning

confidence: 99%

“…Hence, analytical surrogates could enable the use of such algorithms based on bilevel optimization in a range of problems. Examples of applications include, but are not limited to, models with blackbox constraints, computationally expensive models, and nonconvex feasible regions, particularly in pharmaceutical applications, 99 planning, scheduling and control, 100,101 or chromatographic systems. 102 Surrogate models can be further combined with an algebraic objective, and material and energy balances to formulate algebraic optimization problems under the framework of hybrid modeling.…”

Section: Potential Applications Of the Bayesian Machine Scientist To ...mentioning

confidence: 99%

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Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

et al. 2022

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Process modeling has become a fundamental tool to guide experimental work. Unfortunately, process models based on first principles can be expensive to develop and evaluate, and hard to use, particularly when convergence issues arise. This work proves that Bayesian symbolic learning can be applied to derive simple closed-form expressions from rigorous process simulations, streamlining the process modeling task and making process models more accessible to experimental groups. Compared to conventional surrogate models, our approach provides analytical expressions that are easier to communicate and manipulate algebraically to get insights into the process. We apply this method to synthetic data obtained from two basic CO 2 capture processes simulated in Aspen HYSYS, identifying accurate simplified interpretable equations for key variables dictating the process economic and environmental performance. We then use these expressions to analyze the process variables' elasticities and benchmark an emerging CO 2 capture process against the business as usual technology.

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Section: Analytical Application Of the Expressionsmentioning

confidence: 99%

Section: Potential Applications Of the Bayesian Machine Scientist To ...mentioning

confidence: 99%

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

et al. 2022

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“…These threats are disruptive or operational events. Works of literature have addressed operational uncertainties; − such uncertainties are due to supply–demand coordination events and may result from inadequate coordination between SC entities, thus leading to imperfect information and failed processes. Disruption uncertainties result from man-made/natural disasters, pandemics, or strikes.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Programming Approach to Optimize Tactical and Operational Supply Chain Decisions under Disruptions

Badejo

Ierapetritou

2022

Ind. Eng. Chem. Res.

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Supply chain (SC) networks have become more prominent, complex, and challenging to manage, especially considering the multitude of risks and uncertainty that may manifest. Studies have shown two basic approaches to hedge against the negative impact of SC disruptions: proactive and reactive. While the former methods suggest different approaches to generating robust and resilient structures, the latter approach ensures that the SC recovers effectively. A general shortcoming of existing work is not considering SC dynamics. Consequently, disruptions are considered static events without including the durations and recovery policies. In this work, we develop a SC model that aids decision-making in addressing disruptions by considering proactive and reactive strategies. We adopted a discrete time-expanded model to solve the SC problem and consider the disruption dynamics using the rolling horizon framework. In the proposed SC model, a graph network represents the SC, where the nodes consisting of suppliers, manufacturing sites, warehouses, and customers interact using the arcs. The arcs determine the flow of materials between nodes. Independent disruptions can occur at the nodes and/or arcs, and the time of disruption is quantified using the geometric distribution. In the advent of disruption, we have adopted adjusting routing plans, inventory levels, capacity flexibility, and other tactical and operational decisions to hedge against disruption. To illustrate the proposed approach, we used a small problem to illustrate the effect of arcs and node disruption in decision-making and a realistic case study to demonstrate the proposed framework’s computational complexity. The results suggested that the effect of node disruption is more predominant because the initial network configuration limits the flexibility at the nodes. Furthermore, it was shown that the SC operated efficiently, as the solution offers a balance between the service level and the total cost of operating the SC.

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“…The nature of the global market has been forcing enterprises to expand their supply chain network consequently making the structure more complex and more susceptible to threats in the form of risks and uncertainties [3][4][5] . These risks are categorized into two: operational or disruptive 6 .…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A Mathematical Modeling approach for Supply Chain Management under Disruption and Operational Uncertainty

Ierapetritou

Badejo

2022

Preprint

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In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.

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Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis

Cited by 10 publications

References 57 publications

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

Mathematical Programming Approach to Optimize Tactical and Operational Supply Chain Decisions under Disruptions

A Mathematical Modeling approach for Supply Chain Management under Disruption and Operational Uncertainty

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Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis

Cited by 10 publications

References 57 publications

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies

Mathematical Programming Approach to Optimize Tactical and Operational Supply Chain Decisions under Disruptions

A Mathematical Modeling approach for Supply Chain Management under Disruption and Operational Uncertainty

Contact Info

Product

Resources

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Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies