Browsing by Author "Tunc, Huseyin"

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Citation - WoS: 19
Citation - Scopus: 21
An Extended Mixed-Integer Programming Formulation and Dynamic Cut Generation Approach for the Stochastic Lot-Sizing Problem
(informs, 2018) Tunc, Huseyin; Kilic, Onur A.; Tarim, S. Armagan; Rossi, Roberto; 6641
We present an extended mixed-integer programming formulation of the stochastic lot-sizing problem for the static-dynamic uncertainty strategy. The proposed formulation is significantly more time efficient as compared to existing formulations in the literature and it can handle variants of the stochastic lot-sizing problem characterized by penalty costs and service level constraints, as well as backorders and lost sales. Also, besides being capable of working with a predefined piecewise linear approximation of the cost function-as is the case in earlier formulations-it has the functionality of finding an optimal cost solution with an arbitrary level of precision by means of a novel dynamic cut generation approach.
Citation - WoS: 21
Citation - Scopus: 24
Heuristic policies for the stochastic economic lot sizing problem with remanufacturing under service level constraints
(Elsevier Science Bv, 2018) Kilic, Onur A.; Tunc, Huseyin; Tarim, S. Armagan; 6641
In this paper, we address the stochastic economic lot sizing problem with remanufacturing under service level constraints. The problem emerges in hybrid production systems where demand can be met via two alternative sources: manufacturing new products and remanufacturing returned products. The deterministic counterpart of this problem has been considered in the literature and it is shown to be NP-Hard. We focus on the case where period demands and returns are stochastic. The optimal solution to this problem is not a deterministic production schedule but a control policy, yet its structure has not yet been characterized. We propose two heuristic policies for the problem that make use of simple decision rules to control manufacturing and remanufacturing operations and present mathematical models thereof. (C) 2018 Elsevier B.V. All rights reserved.