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Erland Hejn Nielsen

Optimising Job-Shop Functions Utilising the Score-Function Method

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearch

  • Department of Business Studies
  • CORAL - Centre for Operations Research Applications in Logistics
During the last 1-2 decades, simulation optimisation of discrete event dynamic systems (DEDS) has made considerable theoretical progress with respect to computational efficiency. The score-function (SF) method and the infinitesimal perturbation analysis (IPA) are two candidates belonging to this new class of methods, where one single simulation run in principle is sufficient for the estimation of any desired number of partial gradients. Embedded in an iterative set-up both the SF and the IPA methods belong to the class of Stochastic Approximation (SA) algorithms and furthermore if the gradients are unbiased, the SA-algorithm will be known as a Robbins-Monro-algorithm. The present work will focus on the SF method and show how to migrate it to general types of discrete event simulation systems, in this case represented by SIMNET II, and discuss how the optimisation of the functioning of a Job-Shop can be handled by the SF method.
Original languageEnglish
Title of host publicationLogistics Changes in the New Century
Number of pages16
PublisherAarhus School of Business, Department of Management Science and Logistics
Publication year2000
Publication statusPublished - 2000
EventXIIth NOFOMA conference - NOFOMA 2000 -
Duration: 13 Jun 200015 Jun 2000


ConferenceXIIth NOFOMA conference - NOFOMA 2000

    Research areas

  • Simulation, Optimisation, Score Function, Stochastic Approximation, Robbins-Monro, Job-Shop, SIMNET II

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