کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
480272 | 1446090 | 2011 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: An efficient computational method for a stochastic dynamic lot-sizing problem under service-level constraints An efficient computational method for a stochastic dynamic lot-sizing problem under service-level constraints](/preview/png/480272.png)
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.
► Efficient computational approach to solve the mixed integer programming (MIP) model of Tarim and Kingsman [8].
► Stochastic lot-sizing problem with service level constraints under the static–dynamic uncertainty strategy.
► Computational procedure: a relaxation step where a shortest-path problem is solved, and a branch-and-bound step.
► Numerical tests show that our method dominates the MIP solution approach and can handle real-life size problems.
Journal: European Journal of Operational Research - Volume 215, Issue 3, 16 December 2011, Pages 563–571