A Non-Parametric Learning Algorithm for a Stochastic Multi-Echelon Inventory Problem PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download A Non-Parametric Learning Algorithm for a Stochastic Multi-Echelon Inventory Problem PDF full book. Access full book title A Non-Parametric Learning Algorithm for a Stochastic Multi-Echelon Inventory Problem by Cong Yang. Download full books in PDF and EPUB format.
Author: Cong Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
We consider a periodic-review single-product multi-echelon inventory problem with instantaneous replenishment. In each period, the decision-maker makes ordering decisions for all echelons. Any unsatisfied demand is backordered, and any excess inventory is carried to the next period. In contrast to the classic inventory literature, we assume that the information of the demand distribution is not known a priori, and the decision-maker observes demand realizations over the planning horizon. We propose a non-parametric algorithm that generates a sequence of adaptive ordering decisions based on the stochastic gradient descent method. We compare the T-period cost of our algorithm to the clairvoyant, who knows the underlying demand distribution in advance, and we prove that the expected T-period regret is at most O( √ T), matching a lower bound for this problem.
Author: Cong Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
We consider a periodic-review single-product multi-echelon inventory problem with instantaneous replenishment. In each period, the decision-maker makes ordering decisions for all echelons. Any unsatisfied demand is backordered, and any excess inventory is carried to the next period. In contrast to the classic inventory literature, we assume that the information of the demand distribution is not known a priori, and the decision-maker observes demand realizations over the planning horizon. We propose a non-parametric algorithm that generates a sequence of adaptive ordering decisions based on the stochastic gradient descent method. We compare the T-period cost of our algorithm to the clairvoyant, who knows the underlying demand distribution in advance, and we prove that the expected T-period regret is at most O( √ T), matching a lower bound for this problem.
Author: Marc De Bodt
Publisher:
ISBN:
Category : Inventory control
Languages : en
Pages : 48
Get Book Here
Book Description
Author: Marc De Bodt
Publisher: Palala Press
ISBN: 9781378920930
Category : History
Languages : en
Pages : 60
Get Book Here
Book Description
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Author: Marc de Bodt
Publisher:
ISBN: 9781332256280
Category : Business & Economics
Languages : en
Pages : 58
Get Book Here
Book Description
Excerpt from Continuous-Review Policies for a Multi-Echelon Inventory Problem With Stochastic Demand Continuous-Review Policies for a Multi-Echelon Inventory Problem With Stochastic Demand was written by Marc de Bodt and Stephen C. Graves in 1982. This is a 58 page book, containing 5607 words and 5 pictures. Search Inside is enabled for this title. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Author: Zhanyue Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
In this paper, we consider a class of inventory management applications where the distribution of the underlying demand is unknown and the manager must make an inventory-ordering decision in each period based only on the past demand data. The standard performance measure is regret, which is the cost difference between a learning algorithm and the clairvoyant (full-information) benchmark. When the benchmark is chosen to be the (full-information) optimal policy, we propose a new nonparametric learning algorithm that combines the Sample Average Approximation (SAA) approach and the Stochastic Gradient Descent (SGD) method to admit a regret bound of $ mathcal{O}( sqrt{K})$ ($K$ is the planning horizon), which matches the theoretical lower bound. We demonstrate the usefulness of the algorithm by applying it to two classic inventory problems: dual-sourcing and two-echelon inventory problems, under the assumption that the manager does not know the demand distributions and has access only to historical data. We also conduct numerical experiments to demonstrate the effectiveness of our proposed algorithms.
Author: Hao Yuan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
We consider a periodic-review single-product inventory system with fixed cost under censored demand. Under full demand distributional information, it is well-known that the celebrated $(s,S)$ policy is optimal. In this paper, we assume the firm does not know the demand distribution a priori, and makes adaptive inventory ordering decision in each period based only on the past sales (a.k.a. censored demand) data. The standard performance measure is regret, which is the cost difference between a feasible learning algorithm and the clairvoyant (full-information) benchmark. Compared with prior literature, the key difficulty of this problem lies in the loss of joint convexity of the objective function, due to the presence of fixed cost. We develop a nonparametric learning algorithm termed the $( delta, S)$ policy that combines the powers of stochastic gradient descent, bandit controls, and simulation-based methods in a seamless and non-trivial fashion. We prove that the cumulative regret is $O( log T sqrt{T})$, which is provably tight up to a logarithmic factor. We also develop several technical results that are of independent interest. We believe that the framework developed could be widely applied to learning other important stochastic systems with partial convexity in the objectives.
Author: Michael A. Völkel
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832551077
Category : Mathematics
Languages : en
Pages : 119
Get Book Here
Book Description
This book deals with inventory systems in supply chains that face risks that could render products unsalable. These risks include possible cooling system failures, transportation risks, packaging errors, handling errors, or natural quality deterioration over time like spoilage of food or blood products. Classical supply chain inventory models do not regard these risks. This thesis introduces novel cost models that consider these risks. It also analyzes how real-time tracking with RFID sensors and smart containers can contribute to decision making. To solve these cost models, this work presents new solution methods based on dynamic programming. In extensive computational studies both with experimental as well as real-life data from large players in the retailer industry, the solution methods prove to lead to substantially lower costs than existing solution methods and heuristics.
Author: Bodt Marc de
Publisher:
ISBN: 9780243780754
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Charles Edward Pinkus
Publisher:
ISBN:
Category : Branch and bound algorithms
Languages : en
Pages : 151
Get Book Here
Book Description
Large-scale distribution systems require a hierarchy of retail stores and warehouses to satisfy the demand of their customers. Given the maximum number of installations and their possible locations, the problem is to determine which installations to include in the design of the system and which products to stock at these installations. Demand for the products is assumed known and may be deterministic or stochastic. The objective is to find a solution to this design problem which minimizes the total (expected) discounted cost for the lifetime of the system. This problem has been formulated as a combinatorial optimization problem and solved by a branch-and-bound algorithm. The subproblems of the algorithm are tractable integer linear programs. Applications of this model to the design of other multi-use, multi-facility systems are briefly described. (Author).
Author: Alp Muharremogl̆u
Publisher:
ISBN:
Category :
Languages : en
Pages : 126
Get Book Here
Book Description
(cont.) In addition to providing a simple proof technique, the new approach gives rise to efficient algorithms for the calculation of the policy parameters, for all the systems described above. Finally we analyze an assembly system with stochastic leadtimes. We show that the problem can be decomposed into a series of subproblems, each with a single kit of parts. This enhances our understanding about optimal policies in this setting and we develop a relatively efficient algorithm for the computation of optimal policies.
