Managing risk in premium fruit and vegetable supply chains
Name
185056840-MIT.pdf
Description
Full printable version
Size
5.72 MB
Format
Adobe PDF
Checksum (MD5)
8245805915d182094beebfd99d8ce7e9
Author(s)
Merrill, Joshua Matthew
Advisor(s)
Edmund W. Schuster and Chris Caplice.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
Abstract
Production planning in premium fresh produce supply chains is challenging due to the uncertainty of both supply and demand. A two-stage planning algorithm using mixed integer linear programming and Monte Carlo simulation is developed for production planning in the case of a premium branded tomato. Output from the optimization model is sequentially input into the simulation to provide management with information on expected profit and customer service levels at the grocery retail distribution center. The models are formulated to incorporate uncertainty in demand, yield, and harvest failure. The outcome of the algorithm is an annual production plan that meets minimum customer service requirements, while optimizing profit. The resulting timing, location, and quantity of acres suggested by the algorithm are evaluated against the current industry heuristic of performing deterministic calculations, based on average yield and demand, and then planting double the required acreage. The suggested two-stage planning algorithm achieves 90 percent customer service with 20 percent less planted acres and almost three times as much profit than the industry heuristic of doubling the acreage.
Description
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007.
"June 2007."
Includes bibliographical references (leaves 69-71).
Subjects
Engineering Systems Division.
MIT Department
Massachusetts Institute of Technology. Engineering Systems Division
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