Advertisement

Instance Generation

  • Alf Kimms
Part of the Production and Logistics book series (3197)

Abstract

As we have learned, the problem that we are concerned about has not been treated elsewhere. Hence, there is neither an established multi—level test—bed nor an instance generator available. Section 4.1 therefore introduces a parameter controlled instance generator (APCIG) which allows to create multi—level lot sizing (and scheduling) instances systematically. Due to preliminary computational experience, we know that certain parameters of the PLSP have an impact on the performance of solution methods. This is not a very surprising result, of course. But, to gain a better understanding in what makes instances hard to solve and what method should be chosen for what instance, a (full) factorial design is used rather than a randomized design. For a guideline for designing test—beds, performing computational studies, and reporting the results1 we refer to [BaGoKeReSt95, Hoo95]. Section 4.2 presents an experimental design for the PLSP-MM. Section 4.3 relates the speed of different computers to allow fair comparisons with other platforms than those used for our tests.

Keywords

Setup Cost Capacity Utilization Instance Generation Adjacency Matrice External Demand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Physica-Verlag Heidelberg 1997

Authors and Affiliations

  • Alf Kimms
    • 1
  1. 1.Lehrstuhl für Produktion und Logistik Institut für BetriebswirtschaftslehreChristian-Albrechts-Universität zu KielKielGermany

Personalised recommendations