celdi

The purpose of this project is to evaluate the preferred size of tanks to allocate to customer sites (and also at production facilities and intermediate distribution centers) with the objective of minimizing distribution costs and investment costs in tanks.

The focus of this project is dynamically determining routes of shipments for tactical level decision support for railroads. The objective is to determine an approach to effectively route shipments on trains such that total distance and travel time is minimum while honoring capacity constraints on trains.

Forecasting and optimization have traditionally been approached as two sequential components of inventory management: the random demand is first estimated based on historical data, and the forecast is then used as input to the optimization module. This method suffers from several limitations: (i) the best forecasting method might not be known a priori, and may vary depending on the product considered, (ii) the forecasting module does not take into account the over and underage costs of the inventory model, and instead penalizes over and under-predicting demand equally, although backorder costs are typically much higher than holding costs. This provides motivation for devising an operating strategy that builds upon the information revealed over time and remains flexible to integrate forecasting and inventory control in a framework that can be applied to large numbers of products. In preliminary work, the PI and Co-PI have demonstrated the potential of adaptive forecasting, and achieved cost reductions of up to 5% on test problems. To establish the relevance of the Adaptive and data-driven forecasting and inventory control allows companies to recognize changing demand conditions and mitigate the risk of costly demand estimation errors by (i) tracking the performance of forecasting methods available to the manager and (ii) relying on historical data as much as possible without making assumptions on the underlying probability distributions. It has emerged as an important tool to help companies satisfy customer demand in fast-changing environments. This technical report discusses the challenges related to traditional demand management techniques, motivates the relevance of the data-driven approach to address these issues, and provides a high-level description of the solution tools implemented in this project.