A framework for fair and sustainable process supply chain planning under uncertainty

Project ID: 2531ad1499

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Research Theme: Manufacturing The Future

UCL Lead department: Chemical Engineering

Project Summary:

Fair, sustainable, stable and efficient process supply chains are critical to the UK (and worldwide) economy and society, by boosting the economic growth, benefiting the business, improving the living standard, safeguarding jobs, as well as reducing pollution and energy use. The proposed project will focus on the investigation of the novel modelling and solution approaches to address the fairness in supply chain planning problems for the process industry in the presence of uncertainties by integrating game theory and computational models and tools. In this project, we will exploit recent advances in mathematics, operational research, computer science and process systems engineering to develop effective and efficient optimisation-based models, frameworks and solution methodologies for fair decision-making of supply chain planning subject to uncertainty, such as variations of the demands, prices, process performances, fluctuations in socio-economics, climate changes, and natural disasters. Potential advances have a vast realm of applicability in both academia and industry, especially in the areas vital to the economic success, environment, and social wellbeing of the UK in face of the challenges of uncertainties in energy resources, water demand and availability, food supply/demand and climate changes. The novel supply chain planning frameworks and tools developed in this project will be tested on literature examples as well as real world industrial case studies. The primary analyses tools will be high-level modelling systems (for example, GAMS) and/or Python-based for the development of relevant mathematical programming techniques for optimisation components of the project coupled with supervised and unsupervised machine learning. The project is ideally suited to a quantitative individual with chemical/biochemical engineering and/or applied operational research background, who is motivated to work on complex and combinatorial optimisation problems with application domains from the process industry including energy systems and environment.