Endüstri Mühendisliği Bölümü Koleksiyonu

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

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  • 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.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Generation of Feasible Integer Solutions on a Massively Parallel Computer Using the Feasibility Pump
    (Elsevier Science bv, 2017-11-01) Mehrotra, Sanjay; Koç, Utku
    We present an approach to parallelize generation of feasible mixed integer solutions of mixed integer linear programs in distributed memory high performance computing environments. This approach combines a parallel framework with feasibility pump (FP) as the rounding heuristic. It runs multiple FP instances with different starting solutions concurrently, while allowing them to share information. Our computational results suggest that the improvement resulting from parallelization using our approach is statistically significant. (C) 2017 Elsevier B.V. All rights reserved.