Clickstream Big Data and “Delivery before Order Making” Mode for Online Retailers

Our research is inspired by a leading online retailer using clickstream big data to estimate customer demand and then ship items to customers or hubs near customers by a mode of “delivery before order making” (DBOM) mode. Using clickstream data to obtain advance demand information in order quantities, we integrate the forecasting with a singleitem uncapacitated dynamic lot sizing problem in a rolling-horizon environment. Using the simulated clickstream data, we evaluate the performance of DBOM mode

Robust Design of Public Storage Warehouses

We apply robust optimization and revenue management in public storage warehouses. We optimize the expected revenue of public storage warehouses against the worst cases with a max-min revenue objective, and the decision variables are mainly the number of storage units for each storage type. With the robust design, we can observe worst-case revenue improvement

Sorting with Robots: where to drop off the parcel?

This paper presents a method for assigning destinations to drop off points in robotic sorting systems, taking into account robot congestion

Improving the Speed Delivery for Robotic Warehouses

International audienceThis paper studied a new order fulfilling system using mobile robots instead of manual picking at online retailers' distribution centers. This new generation of sustainable green warehouse systems can improve productivity and flexibility. We measured the performance of system, and provided design rules for velocity of robots. We built open queue models for the new order fulfilling system and calculated throughput time of this system given the number of robots. This is one of earliest papers to introduce this new material handling system

Bot-In-Time Delivery for Robotic Mobile Fulfillment Systems

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Designing public storage warehouses with high demand for revenue maximisation

International audienceThe design of public storage warehouses needs to fit market segments to increase the average revenue in an environment of high demand. This paper presents a revenue model integrated with queuing and price-demand theories to solve the design and pricing problem for public storage warehouses. We consider two demand cases in the model, which are exponential demand and piecewise linear demand. We also develop a solution based on dynamic programming techniques to solve the problem. Using data from a warehouse, we conduct numerical experiments. Results show that our approach can improve the expected revenue of public storage warehouses with high demand by 16.6% on average. We further conduct sensitivity analysis on price, and investigate the relation between revenue and price

Dynamic lot-sizing models for retailers with online channels

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Inventory Replenishment Models with Advance Demand Information for Agricultural Online Retailers

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