An inventory-based simulation model for annual-to-daily temporal freight assignment

• Published in: Transportation research. Part E, Logistics and transportation review Vol. 79; pp. 83 - 101
• Main Authors: Zhao, Miyuan, Chow, Joseph Y.J., Ritchie, Stephen G.
• Format: Journal Article
• Language: English
• Published: Exeter Elsevier India Pvt Ltd 01.07.2015; Elsevier Sequoia S.A
• Subjects: Aggregates; Assignment problem; Computer simulation; Demand model; Economic order quantity; Firm simulation; Freight; Freight forecasting; Inventories; Mathematical models; Order quantity; Purchase orders; Stockpiling; Supply chain management; Supply chain networks; Supply chains; Temporal assignment; Temporal logic; Tradeoff analysis; Transportation; Transportation problem (Operations research); Validation studies; United States > US; California; Temporal assignment; Demand model; Firm simulation; Supply chain networks; Freight forecasting; Economic order quantity
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2015.04.001

[Display omitted] •Introduce the first temporal assignment model for freight forecasting.•Explicitly consider trade-offs between inventory and transportation cost management.•Model outputs are sensitive to logistics policies.•Model is illustrated by a case study conducted with California data.•Provide guidelines for other public agencies to adopt similar add-ons in forecasting.•Outperforms the traditional fixed factor approaches in mean value accuracy by 15–31%. In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an Economic Order Quantity (EOQ) model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecasting models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF3 data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results showed the model outperforms fixed factor approaches in mean value accuracy by 15–31%.