Scheduling process operations under uncertainty and integration with long term planning

Abstract

This thesis centers upon the application of mathematical modelling, optimization theory and uncertainty analysis to the problem of scheduling batch operations for large scale industries. Over the years, decision making strategies such as scheduling, that deals with allocation of plant resources, has been widely adopted by industries to efficiently carry out their operations and achieve the desired targets. In this thesis, the focus is on planning and scheduling under endogenous uncertainty in the context of multijob, multitasking batch plants. This class of scheduling problems are of practical importance, specially in the analytical services sector, where effective scheduling models could increase the efficiency in carrying out the plant operations and may lead to increased throughput, or reduced makespan, resulting in greater profits or customer satisfaction.