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Farm machinery selection using simulation and genetic algorithms

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dc.contributor.author Parmar, RS en
dc.contributor.author McClendon, RW en
dc.contributor.author Potter, WD en
dc.date.accessioned 2014-06-06T06:43:02Z
dc.date.available 2014-06-06T06:43:02Z
dc.date.issued 1996 en
dc.identifier.issn 00012351 en
dc.identifier.uri http://62.217.125.90/xmlui/handle/123456789/964
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-0030239187&partnerID=40&md5=59cfc72964f44ebbb18da9df68a9a750 en
dc.subject Genetic algorithms en
dc.subject Machinery en
dc.subject Optimization en
dc.subject Peanuts en
dc.subject Simulation en
dc.subject.other Net returns en
dc.subject.other Peanut farm machinery en
dc.subject.other Artificial intelligence en
dc.subject.other Computer simulation en
dc.subject.other Costs en
dc.subject.other Genetic algorithms en
dc.subject.other Mathematical models en
dc.subject.other Mathematical programming en
dc.subject.other Agricultural machinery en
dc.subject.other Arachis hypogaea en
dc.title Farm machinery selection using simulation and genetic algorithms en
heal.type journalArticle en
heal.publicationDate 1996 en
heal.abstract Computer simulation and genetic algorithms were used to optimize peanut farm machinery selection. The objective of optimization was to maximize net returns above machinery costs. A computer simulation model was used to determine net returns above machinery costs. The simulation model determined net returns above machinery costs for a given machinery set, but did not find an optimum machinery set. The optimum machinery set was determined using two search schemes-an exhaustive search and an artificially intelligent search. The exhaustive search scheme involved running the simulation model with all possible machinery sets, and then selecting the machinery set that produced the highest returns. Alternatively, genetic algorithms were used as an intelligent search scheme to generate machinery sets for the simulation model. A genetic algorithm found a near-optimal solution in 10% of the total time required by the exhaustive search. Modifications in the genetic algorithm not only reduced the search time by half, but also improved the quality of the solutions.Computer simulation and genetic algorithms were used to optimize peanut farm machinery selection. The objective of optimization was to maximize net returns above machinery costs. A computer simulation model was used to determine net returns above machinery costs. The simulation model determined net returns above machinery costs for a given machinery set, but did not find an optimum machinery set. The optimum machinery set was determined using two search schemes - an exhaustive search and an artificially intelligent search. The exhaustive search scheme involved running the simulation model with all possible machinery sets, and then selecting the machinery set that produced the highest returns. Alternatively, genetic algorithms were used as an intelligent search scheme to generate machinery sets for the simulation model. A genetic algorithm found a near-optimal solution in 10% of the total time required by the exhaustive search. Modifications in the genetic algorithm not only reduced the search time by half, but also improved the quality of the solutions. en
heal.journalName Transactions of the American Society of Agricultural Engineers en
dc.identifier.issue 5 en
dc.identifier.volume 39 en
dc.identifier.spage 1905 en
dc.identifier.epage 1909 en


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