The Multi-Location Transshipment Problem with Positive Replenishment Lead Times

Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm

Stochastic Modelling and Analysis of Warehouse Operations

This thesis has studied stochastic models and analysis of warehouse operations. After an overview of stochastic research in warehouse operations, we explore the following topics. Firstly, we search optimal batch sizes in a parallel-aisle warehouse with online order arrivals. We employ a sample path optimization and perturbation analysis algorithm to search the optimal batch size for a warehousing service provider, and a central finite difference algorithm to search the optimal batch sizes from the perspectives of customers and total systems. Secondly, we research a polling-based dynamic order picking system for online retailers. We build models to describe and analyze such systems via stochastic polling theory, find closed-form expressions for the order line waiting times, and apply polling-based picking to online retailers. We then present closed-form analytic expressions for pick rates of order picking bucket brigades systems in differe

Evaluating battery charging and swapping strategies in a robotic mobile fulfilment system

Robotic mobile fulfillment systems (RMFS) have seen many implementations in recent years, due to their high flexibility and low operational cost. Such a system stores goods in movable shelves and uses movable robots to transport the shelves. The robot is battery powered and the battery depletes during operations, which can seriously affect the performance of the system. This study focuses on battery management problem in an RMFS, considering a battery swapping and a battery charging strategy with plug-in or inductive charging. We build a semi-open queueing network (SOQN) to estimate system performance, modeling the battery charging process as a single queue and the battery swapping process as a nested SOQN. We develop a decomposition method to solve the analytical models and validate them through simulation. Our models can be used to optimize battery recovery strategies and compare their cost and throughput time performance. The results show that throughput time performance can be significantly affected by the battery recovery policy, that inductive charging performs best, and that battery swapping outperforms plug-in charging by as large as 4.88%, in terms of retrieval transaction throughput time. However, the annual cost of the RMFS using the battery swapping strategy is generally higher than that of the RMFS using the plug-in charging strategy. In the RMFS that uses the inductive charging strategy, a critical price of a robot can be found, for a lower robot price and a small required retrieval transaction throughput time, inductive charging outperforms both plug-in charging and battery swapping strategies in terms of annual cost. We also find that ignoring the battery recovery will underestimate the number of robots required and the system cost for more than 15%

A polling based dynamic order picking system for online retailers

One of the challenging questions that online retailers are currently facing is how to organize the logistic fulfillment processes both during and after a transaction has taken place. As new information technologies become available that allow picking information to be conveyed in real time and with the ongoing need to create greater responsiveness to customers, there is increasing interest in applying dynamic picking in the warehouses of online retailers. In a Dynamic Picking System (DPS), a worker picks orders that arrive in real time during the picking operations and the picking information can dynamically change in a picking cycle. Models to describe and analyze such systems via stochastic polling theory are presented and closed-form expressions for the order line waiting times in a DPS are derived. These analytical results are verified by simulation. It is shown that the application of polling-based picking can generally lead to shorter order throughput times and higher on-time service completion ratios than traditional batch picking systems using optimal batch sizes. It is demonstrated that the proposed analysis method can be applied to minimize warehouse cost and improve service

Performance Evaluation of Automated Medicine Delivery Systems

The daily medicine delivery is one of the most important activities in hospitals. The traditional medicine delivery, from hospital warehouses to the patients, typically involves a human delivery team supplying patients by handcarts. The multiple steps in the medicine delivery process impact the efficiency and increase the risk of contamination. Many hospitals are therefore on their way to automate this process. The Telelift-based automated medicine delivery system provides high health safety, low operational cost, and high system efficiency. This paper develops a stochastic model to evaluate and analyze the medicine delivery process by such an automated medicine delivery system. We adopt a two-moment approximation method and an aggregation approximation algorithm to solve the nested queuing model, considering regular and peak demand. We use simulation to validate the analytical model. The numerical experiments show that our analytical model is sufficiently accurate to evaluate the automated medicine delivery process. Our model can help decision makers of hospitals to reduce the patient waiting time and medicine response time. Our method can also be extended to other automated overhead material handling systems

Optimal travel time models for split-platform automated storage and retrieval systems

In traditional automated storage and retrieval (AS/R) systems, the storage and retrieval machine travels simultaneously in the horizontal and vertical directions. The so-called split-platform AS/R system consists of platforms (or shuttles and lifts), that can move independently in horizontal (shuttles) and vertical (lifts) directions. Previous literature on such systems is limited to single command cycles (i.e. storages or retrievals). This paper studies two optimal dual command travel time models. We formulate a continuous optimal travel time model for a split-platform AS/R system with a dedicated lift per rack and another travel time model for a split-platform AS/R system with a dedicated lift per job type. Then we analyse the performance of these two models. The two models are validated by computer simulation and appear to give quite accurate results. We show that the optimal travel time gap with an upper bound from existing literature can be as large as 26%. When the shape factor of the rack is less than about 1, the policy to use a dedicated lift per rack is better; when the shape factor of the rack is more than about 1, the policy to use a dedicated lift per job type is better