Optimal periodic flexible policies for two-stage serial supply chains
In a two-stage serial supply chain, a periodic flexible policy (PF policy) allows the retailer to receive fixed orders that may depend on demand history in one period of the ordering cycle and order freely in other periods. Existing literature has shown that certain PF policies can significantly red...
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sg-ntu-dr.10356-901022023-05-19T06:44:43Z Optimal periodic flexible policies for two-stage serial supply chains Liu, Fang Gavirneni, Srinagesh Nanyang Business School Inventory DRNTU::Business::Management Periodic Flexibility In a two-stage serial supply chain, a periodic flexible policy (PF policy) allows the retailer to receive fixed orders that may depend on demand history in one period of the ordering cycle and order freely in other periods. Existing literature has shown that certain PF policies can significantly reduce the inefficiency in a decentralized supply chain. However, these works have mostly defined and implemented ad-hoc periodic flexible policies and have not attempted to identify the optimal periodic flexible policies. In this paper, we characterize the structure of the optimal PF policy using calculus of variations. In particular, we show that under the optimal PF policy, the retailer receives shipments either according to a state dependent capacitated policy or a state dependent order up to policy. Furthermore, we can approximate the retailer’s optimal restricted ordering function by a piecewise linear function and show numerically that this approximation is near optimal. Accepted version 2019-05-27T05:24:35Z 2019-12-06T17:40:42Z 2019-05-27T05:24:35Z 2019-12-06T17:40:42Z 2018 Journal Article Liu, F., & Gavirneni, S. (2018). Optimal periodic flexible policies for two-stage serial supply chains. European Journal of Operational Research, 268(1), 148-160. doi:10.1016/j.ejor.2018.01.004 0377-2217 https://hdl.handle.net/10356/90102 http://hdl.handle.net/10220/48377 10.1016/j.ejor.2018.01.004 en European Journal of Operational Research © 2018 Elsevier B.V. All rights reserved. This paper was published in European Journal of Operational Research and is made available with permission of Elsevier B.V. 13 p. application/pdf |
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Inventory DRNTU::Business::Management Periodic Flexibility Liu, Fang Gavirneni, Srinagesh Optimal periodic flexible policies for two-stage serial supply chains |
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In a two-stage serial supply chain, a periodic flexible policy (PF policy) allows the retailer to receive fixed orders that may depend on demand history in one period of the ordering cycle and order freely in other periods. Existing literature has shown that certain PF policies can significantly reduce the inefficiency in a decentralized supply chain. However, these works have mostly defined and implemented ad-hoc periodic flexible policies and have not attempted to identify the optimal periodic flexible policies. In this paper, we characterize the structure of the optimal PF policy using calculus of variations. In particular, we show that under the optimal PF policy, the retailer receives shipments either according to a state dependent capacitated policy or a state dependent order up to policy. Furthermore, we can approximate the retailer’s optimal restricted ordering function by a piecewise linear function and show numerically that this approximation is near optimal. |
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Nanyang Business School |
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Nanyang Business School Liu, Fang Gavirneni, Srinagesh |
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Article |
author |
Liu, Fang Gavirneni, Srinagesh |
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Liu, Fang |
title |
Optimal periodic flexible policies for two-stage serial supply chains |
title_short |
Optimal periodic flexible policies for two-stage serial supply chains |
title_full |
Optimal periodic flexible policies for two-stage serial supply chains |
title_fullStr |
Optimal periodic flexible policies for two-stage serial supply chains |
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Optimal periodic flexible policies for two-stage serial supply chains |
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optimal periodic flexible policies for two-stage serial supply chains |
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2019 |
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https://hdl.handle.net/10356/90102 http://hdl.handle.net/10220/48377 |
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1770566323368099840 |