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Performance Evaluation of Stochastic Multi-Echelon Inventory Systems: A Survey

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TLDR
This paper compares and contrast three generic methods for controlling inventory in general supply networks under uncertainty and shows how to apply them systematically to characterize and evaluate various supply networks with different supply processes, inventory policies, and demand processes.
Abstract
Globalization, product proliferation, and fast product innovation have significantly increased the complexities of supply chains in many industries. One of the most important advancements of supply chain management in recent years is the development of models and methodologies for controlling inventory in general supply networks under uncertainty and their widefspread applications to industry. These developments are based on three generic methods: the queueing-inventory method, the lead-time demand method and the flow-unit method. In this paper, we compare and contrast these methods by discussing their strengths and weaknesses, their differences and connections, and showing how to apply them systematically to characterize and evaluate various supply networks with different supply processes, inventory policies, and demand processes. Our objective is to forge links among research strands on different methods and various network topologies so as to develop unified methodologies.

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Journal ArticleDOI

A typology and literature review on stochastic multi-echelon inventory models

TL;DR: The typology is used to classify and review the extensive research of multi-echelon inventory management under uncertain demand and clusters of model assumptions, research goals and applied methodologies are identified.
Journal ArticleDOI

A comprehensive survey of guaranteed-service models for multi-echelon inventory optimization

TL;DR: A comprehensive review of the GSM literature is conducted and classify the literature along three axes: extensions of the original GSM through the relaxation of certain modeling assumptions, solution methods developed for different supply chain structures, and industrial applications and the results obtained on real-world problems.
Posted Content

OR-Gym: A Reinforcement Learning Library for Operations Research Problem.

TL;DR: Reinforcement learning is applied to the knapsack, multi-dimensional bin packing,Multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models.
Journal ArticleDOI

Project-driven supply chains: integrating safety-stock and crashing decisions for recurrent projects

TL;DR: The model of project-driven supply chain (PDSC) is presented to jointly optimize the safety-stock decisions in material supply chains and the crashing decisions in projects, and certain convexity properties are proved which allow the optimal crashing policy to be characterized.
Journal ArticleDOI

Positioning Inventory in Clinical Trial Supply Chains

TL;DR: In this paper, a multi-echelon inventory model for clinical trial supply chains is proposed, which is a nonlinear integer programming problem with chance constraints, and a solution method is developed to transform the original formulation into a linear integer equivalent.
References
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Book

Foundations of Inventory Management

Paul Zipkin
TL;DR: In this article, one item with a constant demand rate and time-varying demands is described. But, the model is based on a single item with constant lead times.
Journal ArticleDOI

Optimal Policies for a Multi-Echelon Inventory Problem

TL;DR: The problem of determining optimal purchasing quantities in a multi-installation model of this type, which arises when there are several installations, is considered.
Journal ArticleDOI

Metric: A Multi-Echelon Technique for Recoverable Item Control

TL;DR: A practical and efficient computer program has been designed to show the cost-effectiveness tradeoff for a large group of recoverable items and can evaluate any distribution of stock and it can compute the optimal redistribution of stock.
Book

Modeling and Analysis of Stochastic Systems

TL;DR: In this paper, the authors define a discrete-time Markov chain to describe the dynamics of a stochastic process, and investigate the effect of different types of discontinuities.