Planning and Scheduling of Process operations under Uncertainty
14th March 2018, Wednesday, 04:30‐05:00 pm (25+5 mins), ChE Seminar hall
In the process industry continuous and batch production systems can be distinguished. The process industries are widely considering planning and scheduling for their optimal operations with the objective of increasing the overall effectiveness of the manufacturing system. Typically steady-state mixed integer linear/nonlinear programming models are formulated and solved for optimal process operations. Most of the process and operational parameters have uncertainty associated with them due to their random disturbance, data collection errors, natural disasters and/or other causes. These uncertainties are categorized as environmental (exogenous) uncertainty and system (endogenous) uncertainty based on the sources of uncertainty. This work focuses on scheduling of integrated production and preventive maintenance in multi-purpose batch plants. The malfunctioning of a unit operation in the absence of adequate maintenance could result in an un planned shutdown leading to down time and a reduction in plant productivity. Since batch plants can be used for multiproduct/multipurpose operations, failure of an equipment can have a serious impact on the sequencing of numerous products and plant profit. Preventive maintenance gives overall reduced costs, and reliable production can be reached as desired. Preventive maintenance policies are very common because of their simplicity and ease of implementation. In this work, we consider the problem of scheduling of integrated production and maintenance activities. The approach is based on unit-specific event-based time representation and using state task network process representation leading to formulation of a mixed integer linear programming model. The proposed model is validated on examples drawn from the literature.