Degree Type

Thesis

Date of Award

2017

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

Major

Industrial and Manufacturing Systems Engineering

First Advisor

Guiping Hu

Abstract

This thesis focuses on designing stochastic programming models for production planning at different stages in a manufacturing supply chain under multiple sources of uncertainties. Various decision makers along the manufacturing supply chain often have to make planning decisions with embedded risks and uncertainties. In an effort to reduce risks and to ensure that the customer demand is met in the most efficient and cost effective way, the production plans at each stage need to be strategically planned. To assist production planning decisions, a two-stage stochastic programming model is developed with the objective of minimizing the total cost including production, inventory, and backorder costs. The proposed framework is validated with case studies in an automobile part manufacturer with real data based on literature. The results demonstrate the robustness of the stochastic model compared with various deterministic models. Sensitivity analysis is performed for the production capacity parameter to derive managerial insights regarding lot-sizing and scheduling decisions under different scenarios.

Copyright Owner

Goutham Ramaraj

Language

en

File Format

application/pdf

File Size

93 pages

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