AIOLOS: Artificial Intelligence powered framework for OnLine prOduction Scheduling

The chemical industry in the UK plays a vital role in the nation's economy with a total annual turnover of £50 billion. To remain competitive both regionally and globally, optimisation-based scheduling methods are often applied to achieve a significant increase in process profit, reduction in energy cost, improvement in the efficiency of inventory management, and enhanced customer satisfaction. However, frequent disruptions such as demand fluctuation, rush order arrivals, due date changes, and equipment malfunction are unavoidable in chemical manufacturing. When these disruptions are present, a pre-determined optimal schedule can become suboptimal or even infeasible. With the use of heuristic-based reactive scheduling methods in response to frequent disruptions, the UK chemical industry loses an estimated profit in the order of hundreds of millions of pounds every year. The existing optimisation-based scheduling methods either require high computational expense to generate a schedule, thus rendering them incapable of managing unexpected disruptions in online scheduling; or directly use poor heuristics or knowledge for fast decision-making which usually leads to a conservative schedule resulting in significant financial losses. More importantly, these methods cannot effectively accommodate certain disruptions such as equipment malfunction and rush order arrivals that often occur in online scheduling, restricting their potential application.
This research will deliver a next generation autonomous online scheduling framework in response to different types of disruptions in the chemical manufacturing industry. The framework will generate high-quality dispatching rules to provide optimal or near-optimal online scheduling solutions for emerging uncertainties in a timely manner (e.g., < 5 minutes) through integration of novel machine learning techniques and robust mathematical programming approaches. This will also allow for the identification of a solution to minimise energy consumption. The research will be addressed via a seamless collaboration between The University of Manchester and University College London with expertise in process systems engineering and machine learning. The proposed framework will be tested in close interactions with industrial partners in the UK and China. The improvement in profit is expected to be at least 3% and potentially up to 15%, corresponding to an estimated annual increase in profit between £70 million and £320 million for the UK chemical industry.