An efficient computational method for a stochastic dynamic lot-sizing problem under service-level constraints

S.A. Tarim, U. Ozen, M.K. Dogru, R. Rossi

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Scopus)

Abstract

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. --------------------------------------------------------------------------------
Original languageEnglish
Pages (from-to)563-571
JournalEuropean Journal of Operational Research
Volume215
Issue number3
DOIs
Publication statusPublished - 2011

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