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Edmund W. Schuster and Chris Caplice. Merrill, Joshua Matthew Massachusetts Institute of Technology. Engineering Systems Division. Massachusetts Institute of Technology. Engineering Systems Division 2008-02-04T20:47:47Z 2008-02-04T20:47:47Z 2007 http://hdl.handle.net/1721.1/40115 185056840 Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007. "June 2007." Includes bibliographical references (leaves 69-71). 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. by Joshua Matthew Merrill. M.Eng. in Logistics 71 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Engineering Systems Division. Managing risk in premium fruit and vegetable supply chains Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3f449a4b-b135-4c6a-bea2-3870a9ef2279"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Managing risk in premium fruit and vegetable supply chains</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2007</PublicationDate> <Authors> <Author> <DisplayName>Merrill, Joshua Matthew</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Engineering Systems Division.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>