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

作者：Tarim S Armagan; Dogru Mustafa K^*; Oezen Ulas; Rossi Roberto

来源：European Journal of Operational Research, 2011, 215(3): 563-571.

DOI：10.1016/j.ejor.2011.06.034

• Inventory
• Relaxation
• Stochastic non-stationary demand
• Mixed integer programming
• Service level
• Static-dynamic uncertainty

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.

• 出版日期2011-12-16