Optimizing Bioethanol Supply Chains for Sustainability
A robust possibilistic programming model can simultaneously optimize economic, environmental, and social objectives in bioethanol supply chain design under uncertainty.
Industrial & Engineering Chemistry Research · 2015
Key Findings
- 01The proposed multiobjective robust possibilistic programming approach effectively integrates economic, environmental, and social objectives.
- 02The model can handle multiple uncertainties inherent in supply chain data.
- 03Robust solutions outperform deterministic solutions in terms of key performance measures.
Application
Design takeaway
When designing complex resource-based supply chains, employ robust optimization techniques that explicitly model and manage uncertainties to achieve superior economic, environmental, and social outcomes.
How to apply
Use this approach to model and optimize the design of any complex supply chain where multiple objectives and significant data uncertainty exist, such as renewable energy, food production, or waste management systems.
Project actions
- 01Clearly define your objectives (e.g., cost, emissions, social impact).
- 02Identify and quantify potential uncertainties in your design parameters.
- 03Consider using optimization software to solve complex models.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Comprehensive consideration of multiple objectives.
- +Robust handling of uncertainty.
- +Integration of Life Cycle Assessment for environmental evaluation.
Limitations
The complexity of the model might be challenging to implement fully; data collection for uncertainties can be time-consuming.
Reliability & validity
The model's reliability is based on the mathematical rigor of the optimization framework. Validity is supported by a case study demonstrating its application and the superiority of its solutions over deterministic approaches.
Think critically
How might the choice of uncertainty modelling (e.g., possibilistic vs. stochastic) impact the final design decisions and the overall sustainability of the bioethanol supply chain?
Design Principles
"Embrace uncertainty in design by using robust optimization to achieve multi-objective sustainability goals."
Designing complex supply chains, especially those involving renewable resources like bioethanol, requires balancing multiple competing goals. This approach provides a framework for making strategic decisions that enhance sustainability while mitigating risks associated with uncertain data.
What This Means for Your Design
This research shows how to use a smart computer program to design a bioethanol factory and delivery system that is good for money, the planet, and people, even when you're not sure about all the numbers.
How to use in your project
- 1.Reference this study when discussing the optimization of complex systems, the integration of sustainability metrics, or the handling of uncertainty in design projects.
Add to My Project
Quick Cite
(2015). Multiobjective Robust Possibilistic Programming Approach to Sustainable Bioethanol Supply Chain Design under Multiple Uncertainties. Industrial & Engineering Chemistry Research. https://doi.org/10.1021/acs.iecr.5b02875 Retrieved from https://designdex.org/study/e4345fd9-c83c-49ba-81f5-c532d41aeb35/optimizing-bioethanol-supply-chains-for-sustainability
Paragraph starter
The study by Bairamzadeh et al. (2015) provides a robust framework for designing sustainable bioethanol supply chains by employing a multiobjective robust possibilistic programming approach. This method effectively balances economic, environmental, and social objectives while accounting for inherent data uncertainties, demonstrating that robust solutions offer superior performance compared to deterministic ones.
Source
Industrial & Engineering Chemistry Research
Multiobjective Robust Possibilistic Programming Approach to Sustainable Bioethanol Supply Chain Design under Multiple Uncertainties
journal · 2015
View sourceQuestions about this research
- What does the research say about optimizing bioethanol supply chains for sustainability?
- When designing complex resource-based supply chains, employ robust optimization techniques that explicitly model and manage uncertainties to achieve superior economic, environmental, and social outcomes. Evidence: Industrial & Engineering Chemistry Research (2015).
- Why does "Optimizing Bioethanol Supply Chains for Sustainability" matter for design?
- Designing complex supply chains, especially those involving renewable resources like bioethanol, requires balancing multiple competing goals. This approach provides a framework for making strategic decisions that enhance sustainability while mitigating risks associated with uncertain data.
- How can designers apply this research?
- When designing complex resource-based supply chains, employ robust optimization techniques that explicitly model and manage uncertainties to achieve superior economic, environmental, and social outcomes.
- What were the main findings?
- The proposed multiobjective robust possibilistic programming approach effectively integrates economic, environmental, and social objectives.. The model can handle multiple uncertainties inherent in supply chain data.. Robust solutions outperform deterministic solutions in terms of key performance measures.
- What research method was used?
- Mathematical Modelling (Multiobjective Robust Possibilistic Programming).
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2015 journal from Industrial & Engineering Chemistry Research.
- What should I do differently in my next project?
- Use this approach to model and optimize the design of any complex supply chain where multiple objectives and significant data uncertainty exist, such as renewable energy, food production, or waste management systems.
- What are the limitations?
- The model's complexity may require significant computational resources; the accuracy of the results depends on the quality of the input data and the chosen uncertainty distributions.
- Is there evidence that supply chains affects design outcomes?
- Using a sophisticated mathematical model that accounts for uncertainties leads to better overall performance in bioethanol supply chains compared to simpler, deterministic approaches. Designing complex supply chains, especially those involving renewable resources like bioethanol, requires balancing multiple competing g Source: Industrial & Engineering Chemistry Research (2015).
- Where does this bioethanol supply research apply?
- Bioethanol Supply Chain Design It sits within resource management research on designdex.org.
Related research topics
supply chains design research · evidence on supply chains · does supply chains improve design outcomes · bioethanol supply studies for designers · supply chains and bioethanol supply findings · resource management research evidence