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USING SIMULATION TO EXAMINE CUTTING POLICIES FOR A STEEL FIRM

Minimizing the cost of filling demand is a problem that reaches back to the foundation of operations research. Here we use simulation to investigate various heuristic policies for a one-dimensional, guillotine cutting stock problem with stochastic demand and multiple supply and demand locations. The policies investigated range from a random selection of feasible pieces, to a more strategic search of pieces of a specific type, to a new policy using dual values from a linear program that models a static, deterministic demand environment. We focus on an application in the steel industry and we use real data in our model. We show that simulation can effectively model such a system, and further we exhibit the relative performance of each policy. Our results demonstrate that this new policy provides statistically significant savings over the other policies investigated.

Identiferoai:union.ndltd.org:PITT/oai:PITTETD:etd-11302003-233527
Date02 February 2004
CreatorsOlsen, Susan Marie
ContributorsLarry Shuman, PhD, Jayant Rajgopal, PhD, Andrew Schaefer, PhD
PublisherUniversity of Pittsburgh
Source SetsUniversity of Pittsburgh
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.library.pitt.edu/ETD/available/etd-11302003-233527/
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