{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/40115"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/40115","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Managing risk in premium fruit and vegetable supply chains","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_html":"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_has_math":false,"creators":["Merrill, Joshua Matthew"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Engineering Systems Division.","school":null,"contributors":[],"advisors":["Edmund W. Schuster and Chris Caplice."],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007","date_published":"2007","updated_at":"2026-07-22T22:21:11Z","subjects":["Engineering Systems Division."],"languages":["eng"],"rights":["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. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng.in Logistics"]},{"key":"dc:title","label":"Title","values":["Managing risk in premium fruit and vegetable supply chains"]}]}],"canonical_facts":{"dc:contributor.advisor":["Edmund W. Schuster and Chris Caplice."],"dc:contributor.department":["Massachusetts Institute of Technology. Engineering Systems Division."],"dc:contributor.other":["Massachusetts Institute of Technology. Engineering Systems Division."],"dc:creator":["Merrill, Joshua Matthew"],"dc:date.accessioned":["2008-02-04T20:47:47Z"],"dc:date.available":["2008-02-04T20:47:47Z"],"dc:date.issued":["2007"],"dc:description":["Thesis (M. 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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."],"dc:description.degree":["M.Eng.in Logistics"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/40115"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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. 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