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    <title>Kloprogge, P.</title>
    <link>http://repub.eur.nl/res/aut/4763/</link>
    <description>List of Publications</description>
    <language>en</language>
    <image>
      <url>http://repub.eur.nl/static-eur/img/logo.png</url>
      <title>RePub, Erasmus University Rotterdam</title>
      <link>http://repub.eur.nl</link>
    </image>
    <item>
      <title>An Analytic Model for Capacity Planning of Prisons in the Netherlands (Article)</title>
      <link>http://repub.eur.nl/res/pub/15420/</link>
      <pubDate>2000-11-01T00:00:00Z</pubDate>
      <description>In this paper we describe a decision support system developed to help in assessing the need for various types of prison cells. In particular we predict the probability that a criminal has to be sent home because of a shortage of cells. The problem is modelled through a queuing network with blocking after service. The main objective of our study is to describe our analytical method and an approximate algorithm to solve this network. Through simulation studies we evaluate our method. Both the analytic and the simulation tool are elements of the decision support system.</description>
    </item> <item>
      <title>Capacity planning of prisons in the Netherlands (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/1563/</link>
      <pubDate>1999-03-30T00:00:00Z</pubDate>
      <description>In this paper we describe a decision support system developed to help in assessing the need for various type of prison cells. In particular we predict the probability that a criminal has to be sent home because of a shortage of cells. The problem is modelled through a queueing network with blocking after service. We focus in  particular on the new analytical method to solve
this network.</description>
    </item> <item>
      <title>Media planning by optimizing contact frequencies (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/1522/</link>
      <pubDate>1998-12-31T00:00:00Z</pubDate>
      <description>In this paper we study a model to estimate the probability that a target group of an advertising campaign is reached by a commercial message a given number of times. This contact frequency distribution is known to be computationally difficult to calculate because of dependence between the viewing probabilities of advertisements. Our model calculates good estimates of contact frequencies in a very short time based on data that is often available. A media planning model that optimizes effective reach as a function of contact frequencies demonstrates the usefulness of the model. Several local search procedures such as taboo search, simulated annealing and genetic algorithms are applied to find a good media schedule. The results show that local search methods are flexible, fast and accurate in finding media schedules for media planning models based on contact frequencies. The contact frequency model is a potentially useful new tool for media planners.</description>
    </item>
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