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

S. Armagan Tarim, Mustafa K. Dogru, Ulas Özen and Roberto Rossi

European Journal of Operational Research, 2011, vol. 215, issue 3, 563-571

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.

Keywords: Inventory; Relaxation; Stochastic; non-stationary; demand; Mixed; integer; programming; Service; level; Static-dynamic; uncertainty (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:215:y:2011:i:3:p:563-571

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