Title page for ETD etd-0207103-072636


Type of Document Master's Thesis
Author Rahman, Mohammad Anwar Ashek
Author's Email Address mrahma2@lsu.edu
URN etd-0207103-072636
Title Supply Chain Models for an Assembly System with Preprocessing of Raw Materials
Degree Master of Science in Industrial Engineering (M.S.I.E.)
Department Industrial and Manufacturing Systems Engineering
Advisory Committee
Advisor Name Title
Bhaba R. Sarker Committee Chair
Dennis Webster Committee Member
Lawerence Mann Committee Member
Keywords
  • supply chain system
  • assembly system
  • raw materials
  • batch production
Date of Defense 2002-12-09
Availability unrestricted
Abstract
An assembly line that procures raw materials from outside suppliers and processes the materials into finished products is considered in this research. An ordering policy is proposed for raw materials to meet the requirement of a production facility, which, in turn, must deliver finish products in a fixed quantity at a fixed time interval to the outside buyers. Two different types of raw materials, ‘unfinished’ and ‘ready-to-use’, are procured for the manufacturing system. The ‘unfinished raw materials’ are turned into ‘processed raw materials’ after preprocessing. In the assembly line, the ‘processed raw materials’ and the ‘ready raw materials’ are assembled to convert into the final products. A cost model is developed to aggregate the total costs of raw materials, Work-in-process, and finished goods inventory. Based on the product design and manufacturing requirement a relationship is established between the raw materials and the finished products at different stages of production. A non-linear integer-programming model is developed to determine the optimal ordering policies for procurement of raw materials, and shipment of assembly product, which ultimately minimize the total costs of the model. Numerical examples are presented to demonstrate the solution technique. Sensitivity analysis is performed to show the effects of the parameters on the total cost model. Future research direction is suggested for further improvement of the existing results.
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