This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: IE 495 – Final Exam Due Date: May 1, 2003 Do the following problems. You must work alone on this exam. The only refer- ence materials you are allowed to use on the exam are the textbook by Birge and Louveaux, the Stochastic Programming book by Kall and Wallace: http://www.unizh.ch/ior/Pages/Deutsch/Mitglieder/Kall/bib/ka-wal-94.pdf , the class notes and materials on the course web page, and references to help you with AMPL syntax. You may use whatever software tools you find necessary (e.g. AMPL, Excel, Maple, NEOS). If you feel you need other reference materials to correctly answer any problem, contact Prof. Linderoth. Please show your work. You may attach printouts of AMPL files, Maple work- sheets, and Excel worksheets, or you may email them to Prof. Linderoth. 1 You Say To-MAY-To, I Say To-MAH-To Linear Programming 1 Teresa’s Terrific Tomatoes ( ttt ) is in the business of selling a set P of tomato-related products (e.g salsa, ketchup, tomato paste). In order to create these products, resources from a set R are required (e.g. tomatoes, sugar, labor, spices). To be specific, the parameter a pr is the amount of resource r ∈ R that is required to produce one unit of product p ∈ P . There is a limit b r on the amount of each resource r ∈ R that can be used in a production period. The company can pay for extra resources at a cost of γ r ,r ∈ R . ttt management is trying to determine the optimal way to meet an estimate of its demand d pt for each of its products p ∈ P in each planning period t over a horizon T . The regular production costs for each product are c pt ,p ∈ P,t ∈ T . Any surplus production of a product p ∈ P must be stored at a cost of α p per unit. ttt management also considers unmet demand important, so it imposes a penalty cost of β p for each unit of unmet demand for product p ∈ P . ttt carries no initial inventory of products....
View Full Document
- Spring '08
- Probability distribution, probability density function, Prof. Jeﬀ Linderoth, Tomato-Paste Ketchup Salsa