VMI versus information sharing: an analysis under static uncertainty strategy with fill rate constraints

Choudhary, Devendra Kumar; Shankar, Ravi; Tiwari, Manoj Kumar; Purohit, Arun Kr.

doi:10.1080/00207543.2016.1168943

Cited by 10 publications

(1 citation statement)

References 22 publications

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“…Apart from the above mentioned studies, there is a sizeable literature adopting static uncertainty strategy to control inventory/production decisions in various production/manufacturing systems (see e.g. [18], [19], [20], [21], [22], [23], and [24]). Among those, an extended MIP formulation for a singleitem, stochastic CLSP under α service level constraints is proposed by Tunc [23].…”

Section: *Corresponding Authormentioning

confidence: 99%

A fix-and-optimize heuristic for the capacitated multi-item stochastic lot-sizing problem

Gurkan

Tunç²

2020

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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This study addresses the stochastic multi-item capacitated lot-sizing problem. Here, it is assumed that all items are produced on a single production resource and unmet demands are backlogged. The literature shows that the deterministic version of this problem is NP-Hard. We consider the case where period demands are time-varying random variables. The objective is to determine the minimum expected cost production plan so as to meet stochastic period demands over the planning horizon. We extend the mixed integer programming formulation introduced in the literature to capture the problem under consideration. Further, we propose a fix-and-optimize heuristic building on an item-period oriented decomposition scheme. We then conduct a numerical study to evaluate the performance of the proposed heuristic as compared to the heuristic introduced by Tempelmeier and Hilger [16]. The results clearly show that the proposed fix-and-optimize heuristic arises as both cost-efficient and time-efficient solution approach as compared to the benchmark heuristic.

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