1 Ocak 2011 Dergi makalesi Açık Erişim

Tarim, S. Armagan; Dogru, Mustafa K.; Oezen, Ulas; Rossi, Roberto

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    <subfield code="a">We provide an efficient computational approach to solve the mixed integer programming (MIP) model developed by Tarim and Kingsman [8] for solving a stochastic lot-sizing problem with service level constraints under the static-dynamic uncertainty strategy. The effectiveness of the proposed method hinges on three novelties: (i) the proposed relaxation is computationally efficient and provides an optimal solution most of the time, (ii) if the relaxation produces an infeasible solution, then this solution yields a tight lower bound for the optimal cost, and (iii) it can be modified easily to obtain a feasible solution, which yields an upper bound. In case of infeasibility, the relaxation approach is implemented at each node of the search tree in a branch-and-bound procedure to efficiently search for an optimal solution. Extensive numerical tests show that our method dominates the MIP solution approach and can handle real-life size problems in trivial time. (C) 2011 Elsevier B.V. All rights reserved.</subfield>
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    <subfield code="a">10.1016/j.ejor.2011.06.034</subfield>
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    <subfield code="a">An efficient computational method for a stochastic dynamic lot-sizing problem under service-level constraints</subfield>
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