Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942

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Subject: search.filters.subject.Sample average approximationWoS Q: search.filters.wosQuartile.Q1

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  • Article
    An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks (vol 503, Pg 589, 2019)
    (Elsevier, 2020-02-01) Güney, Evren
    The influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program that approximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently, we propose a linear programming relaxation based method with a provable worst case bound that converges to the current state-of-the-art 1-1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in terms of solution quality and this is one of the few studies that provides approximate optimal solutions for certain real life social networks.
  • Article
    Citation - WoS: 20
    Citation - Scopus: 23
    A Capacitated Lot Sizing Problem With Stochastic Setup Times and Overtime
    (Elsevier Science bv, 2019-02-01) Jabali, Ola; Gendreau, Michel; Jans, Raf; Taş, Duygu
    In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times and Overtime (CLSPSSTO). We describe a mathematical model that considers both regular costs (including production, setup and inventory holding costs) and expected overtime costs (related to the excess usage of capacity). The CLSP-SSTO is formulated as a two-stage stochastic programming problem. A procedure is proposed to exactly compute the expected overtime for a given setup and production plan when the setup times follow a Gamma distribution. A sample average approximation procedure is applied to obtain upper bounds and a statistical lower bound. This is then used to benchmark the performance of two additional heuristics. A first heuristic is based on changing the capacity in the deterministic counterpart, while the second heuristic artificially modifies the setup time. We conduct our computational experiments on well-known problem instances and provide comprehensive analyses to evaluate the performance of each heuristic. (C) 2018 Elsevier B.V. All rights reserved.