Inventory placement in acyclic supply chain networks

Magnanti, Thomas L.; Shen, Zuo‐Jun Max; Shu, Jia; Simchi‐Levi, David; Teo, Chung‐Piaw

doi:10.1016/j.orl.2005.04.004

Cited by 76 publications

(57 citation statements)

References 10 publications

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“…Graves and Willems (2000) extend Simpson's work to supply chains with spanning tree topology, and formulate an efficient dynamic programming algorithm. Optimizing general networks is an NP-hard problem (Lesnaia et al 2005); nevertheless, Willems (2006, 2011) and Magnanti et al (2006) have developed algorithms for optimizing the safety stocks in large-scale real-world supply chains. Sitompul et al (2008) is the only paper we found that includes a capacity constraint; the paper empirically estimates an approximate correction factor to account for the impact on the required safety stock due to the capacity constraint.…”

Section: Literature Reviewmentioning

confidence: 99%

Strategic Safety-Stock Placement in Supply Chains with Capacity Constraints

Graves

Schoenmeyr

2016

M&SOM

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We generalize the guaranteed-service (GS) model for safety stock placement in supply chains to include capacity constraints. We first examine the guaranteed-service model for a capacitated single-stage system with bounded demand. We characterize the optimal inventory policy, which depends on the entire demand history. Due to this complexity, we develop a heuristic, namely a constant base-stock policy with censored ordering. This is an order-up-to policy but with its replenishment orders censored by the capacity constraint. We refer to this heuristic as the modified constant base-stock policy (MCBS). We use a numerical experiment to compare the performance of the heuristic policy to the optimal policy. We find that the performance of the heuristic relative to the optimal policy improves with a tighter capacity constraint. We also observe that the performance of the heuristic itself can sometimes be improved by tightening the capacity constraint. We then use the results for a single-stage system to model a multi-stage serial system that operates with a constant base-stock policy with censored ordering, i.e., a MCBS policy. We show how to adapt the existing dynamic programming algorithm for the unconstrained case to solve for the safety stock locations and base-stock levels in a capacitated serial system. We describe how to extend this model to other supply chain topologies. We report on numerical tests for serial systems, and find that the best MCBS policy in a capacitated system can outperform the best constant base-stock policy in an identical but uncapacitated system.

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Section: Literature Reviewmentioning

confidence: 99%

Strategic Safety-Stock Placement in Supply Chains with Capacity Constraints

Graves

Schoenmeyr

2016

M&SOM

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“…The GSM ILP follows the original work in [3], except for the integrality of the order-points, which is mandatory in spare-part systems with occasionally large, expensive parts at very small stock-levels.…”

Section: The Guaranteed-service-modelmentioning

confidence: 99%

“…With standard piecewise-linear modelling techniques with additional binary variables, this model can approximately be transformed into an ILP (see [3]). …”

Section: The Guaranteed-service-modelmentioning

confidence: 99%

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The Stochastic Guaranteed Service Model with Recourse for Multi-Echelon Warehouse Management

Rambau

Schade

2010

Electronic Notes in Discrete Mathematics

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The Guaranteed Service Model (GSM) computes optimal order-points in multiechelon inventory control under the assumptions that delivery times can be guaranteed and the demand is bounded. Our new Stochastic Guaranteed Service Model (SGSM) with Recourse covers also scenarios that violate these assumptions. Simulation experiments on real-world data of a large German car manufacturer show that policies based on the SGSM dominate GSM-policies.

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“…This is a four-stage network with one final product, two subassemblies For acyclic supply chains with random demand, so far only guaranteed service-time models are considered in the literature. We refer to Lesnaia (2004), Minner (2000), Magnanti, et al (2006) and Humair and Willems (2007) for various solution methods, and to Lesnaia (2004) and Graves and Willems (2003) for recent reviews. Under the guaranteed service-time assumption, one does not model what happens when demand exceeds on-hand stock, but rather assumes that the supply chain responds with an extraordinary measure which always ensures fulfillment of the demand at the service time.…”

Section: Introductionmentioning

confidence: 99%