Production planning is a critical component in supply chain management. The goal of production planning is to meet market demand while minimizing operational costs. There is inherent uncertainty in manufacturing systems due to unscheduled shutdowns and variable production rates. Additionally, actual demand levels cannot be predicted accurately. As a result, there is value in creating a production plan that considers these uncertainties.
Scheduling is also a critical component in supply chain management, but at a smaller level of time granularity. Industrially sized scheduling problems are often on such a large scale that the problem is computationally difficult to solve. Consequently, there is value in creating a mathematical model and selecting a solution algorithm that minimizes this burden.
This work aims to determine the benefit of a stochastic production planning model over its deterministic counterpart. The problem utilizes a multi-period, multi-product aggregated planning model with a finite horizon in a steel manufacturing environment. The production and demand uncertainty is modelled as a two-stage stochastic mixed integer linear program. The problem utilizes a Monte Carlo simulation technique to create the scenarios used in the optimization. The objective of the optimization is to determine the production volume and inventory levels for each discrete time interval while minimizing the weighted cost of production and surplus. The production decisions must be non-anticipative, immediately implementable, and are subjected to production capacity and inventory holding constraints. This work also investigates the advantages a cost-based model has over its goal-programming counterpart. Finally, this thesis develops several mathematical batch scheduling models that use different modelling paradigms in an effort to compare their computational complexity. With the selection of an appropriate model, model extensions are added to replicate an industrially relevant steel mill scheduling problem for a finishing line using data from a facility located in Ontario, Canada. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/17467 |
| Date | January 2015 |
| Creators | Carter, Patrick Alexander Philippe |
| Contributors | Swartz, Christopher L. E., Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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