Optimal and Asymptotically Optimal Policies for Assemble-to-Order N- and W-Systems

Naval Research Logistics, Volume 62, Issue 8, December 2015

Columbia Business School Research Paper No. 14-55

Posted: 11 Oct 2014 Last revised: 15 Sep 2016

See all articles by Lijian Lu

Lijian Lu

Columbia University - Columbia Business School

Jing-Sheng Jeannette Song

Duke University - Fuqua School of Business

Hanqin Zhang

National University of Singapore (NUS) - NUS Business School

Date Written: August 23, 2015

Abstract

We consider two specially structured assemble-to-order (ATO) systems -- the N- and W-systems -- under continuous review, stochastic demand, and non-identical component replenishment leadtimes. Using a hybrid approach that combines sample-path analysis, linear programming, and the tower property of conditional expectation, we characterize the optimal component replenishment policy and common-component allocation rule, present comparative statics of the optimal policy parameters, and show that some commonly used heuristic policies can lead to significant optimality loss. The optimality results require a certain symmetry in the cost parameters. In the absence of this symmetry, we show that, for systems with high demand volume, the asymptotically optimal policy has essentially the same structure; otherwise, the optimal policies have no clear structure. For these latter systems, we develop heuristic policies and show their effectiveness.

Keywords: Inventory Control, Assemble-to-order system, asymptotical analysis

Suggested Citation

Lu, Lijian and Song, Jing-Sheng Jeannette and Zhang, Hanqin, Optimal and Asymptotically Optimal Policies for Assemble-to-Order N- and W-Systems (August 23, 2015). Naval Research Logistics, Volume 62, Issue 8, December 2015; Columbia Business School Research Paper No. 14-55. Available at SSRN: https://ssrn.com/abstract=2507854 or http://dx.doi.org/10.2139/ssrn.2507854

Lijian Lu (Contact Author)

Columbia University - Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

Jing-Sheng Jeannette Song

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States

Hanqin Zhang

National University of Singapore (NUS) - NUS Business School ( email )

1 Business Link
Singapore, 117592
Singapore

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