Optimization-based manufacturing scheduling: Deterministic and uncertain cases

Scheduling is a key factor for manufacturing profitability. Effective schedules can improve on-time delivery, reduce inventory, cut lead time, and improve utilization of resources. The dissertation addresses the optimization-based scheduling at a deterministic or uncertain manufacturing environment. The first part of the work is motivated by the design and implementation of a scheduling system for J. M. Products Inc. whose manufacturing is characterized by the need to simultaneously consider machines and operators, machines requiring significant setup times, and lots dividable into smaller and transferable sub-lots. The second part of this work is to provide a new problem formulation and a new methodology by considering key probabilistic processing requirements within a job-shop context that is one of the most prevalent manufacturing environments. The third part is to address the balance of on time delivery of parts and the comfort levels of operators in the scheduling problem by using fuzzy optimization.