Asymptotically Optimal Lagrangian Policies for Multi-Warehouse, Multi-Store Systems with Lost Sales

Simple Algorithms for Complex Multiwarehouse, Multistore Inventory Control Problems Retailers (both brick-and-mortar and e-commerce) have always faced the problem of allocating inventories in their warehouses (or central distribution centers) to the stores (or smaller local warehouses) in order to minimize total costs. The problem is particularly challenging when the network structure is large and complex, the selling season is long, and the replenishment is frequent. For example, giant retail chains such as Macy’s typically have many warehouses and hundreds of stores across the United States, and online retailers such as Amazon have many distribution centers and over one hundred fulfillment centers. The authors develop algorithms to solve this multiwarehouse, multistore (MWMS) inventory control problem. Their algorithms are computationally efficient and asymptotically optimal as the problem becomes large and complex. This feature is very appealing to today’s fast-moving retail industry with rapidly expanding business scale.

Transportation modal choice for a hybrid inventory control problem

In this report I tried to develop a model to control remanufacturing transportation cost for a reverse supply chain considering various serviceable and remanufacturable inventory stock points. While consumers traditionally dump the products at the end of their life cycle, there is an option to return them to the manufacturers for renew and reuse. As a result, manufacturers must collect products at the end of their life and control their recovery or disposal situation. Product recovery, which includes reusing, remanufacturing and recycling, requires a reverse logistic network in order to increase efficiently. This report presents the results of a simulation model for a reverse logistics network for collecting the returned cartridge toners from customers. Real data from an Australian case company was utilized to design test experiments in order to validate and evaluate the model.

Transportation modal choice for a hybrid inventory control problem

In this report I tried to develop a model to control remanufacturing transportation cost for a reverse supply chain considering various serviceable and remanufacturable inventory stock points. While consumers traditionally dump the products at the end of their life cycle, there is an option to return them to the manufacturers for renew and reuse. As a result, manufacturers must collect products at the end of their life and control their recovery or disposal situation. Product recovery, which includes reusing, remanufacturing and recycling, requires a reverse logistic network in order to increase efficiently. This report presents the results of a simulation model for a reverse logistics network for collecting the returned cartridge toners from customers. Real data from an Australian case company was utilized to design test experiments in order to validate and evaluate the model.

A FUZZY PROGRAMMING APPROACH TO INVENTORY CONTROL PROBLEM

Purpose of study: Main aim of this study is to deals with the problem of inventories. Their holding cost, set-up cost, and many more related to that. All the problems are flexible and having fuzzy nature. Methodology: The model takes the form of a Geometric Programming problem. Hence geometric programming algorithm is used here. Main Finding: The developed models may be used for a single item with a single constraint of limitation on storage area and multi-item inventory problems. Application of this study: This study is useful in the area of inventories. There holding cost and set-up cost etc. The originality of this study: This study may help the stockholders for storing goods and minimizing the cost of holding.

Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning

We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. When there is no fixed ordering cost involved, we design a deep reinforcement learning algorithm to obtain a near-optimal ordering policy and show that there are some monotonicity properties in the learned policy. We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. When a fixed ordering cost is involved, we show that our deep reinforcement learning algorithm is effective and efficient, under which the problem of “curse of dimension” is circumvented.

Simulation-based lateral transshipment policy optimization for s, S inventory control problem in a single-echelon supply chain network

Since it affects the performance of whole supply chain significantly, definition of correct inventory control policy in a supply chain is critical. Recent technological development enabled real time visibility of a supply network by horizontal integration of each node in a supply network. By this opportunity, inventory sharing among stocking locations is also possible in the effort of cost minimization in supply chain management. Hence, lateral transshipment gained popularity and studies seeking the best lateral-transshipment policy is still under research. In this study, we aim to compare different lateral-transshipment policies for an s, S inventory control problem for a single-echelon supply chain network system. In this work, we consider a supply network with three stocking locations which may perform lateral transshipment among them when backorder takes place. We develop the simulation models of the systems in ARENA 14.5 commercial software and compare the performance of the policies by minimizing the total cost under a pre-defined fill rate constraint by using an optimization tool, OptQuest, integrated in that software. The results show that lateral transshipment works well compared to the scenario when there is no lateral transshipment policy in the network.