Flow Balancing with Uncertain Demand for Automated Package Sorting Centers

Luis J. Novoa (), Ahmad I. Jarrah () and David P. Morton ()
Additional contact information
Luis J. Novoa: Department of Decision Sciences, School of Business, George Washington University, Washington, DC 20052
Ahmad I. Jarrah: Department of Decision Sciences, School of Business, George Washington University, Washington, DC 20052
David P. Morton: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

Transportation Science, 2018, vol. 52, issue 1, 210-227

Abstract: Package carriers use sophisticated automated sorting facilities to efficiently process inbound packages and sort them to their down line destinations. During each of several daily processing windows, primary sorters perform high level sortation of the packages and direct them to one of several secondary sorters that are then used to segregate the packages by their outbound loading destinations. We examine the problem of assigning package destinations to the secondary sorters in a way that balances the workload in the facility, while incorporating the day-to-day fluctuation in package volumes and adhering to the outbound loading capacities of the various workcenters in the facility. We present a general stochastic modeling framework using chance constraints to balance the flows, and robust constraints to model the capacity limits. We propose and evaluate the performance of three alternative mixed integer nonlinear formulations for the problem and determine which is most effective. Significant improvement in package flow balance and loading capacity robustness is shown for the test sorting facilities by comparing the solutions from the proposed new model to those obtained when ignoring, partially or completely, the stochasticity in the package volumes.

Keywords: transshipment; package carriers; postal services; automated sortation; cross-docking; stochastic programming; chance constraints; robust optimization; mixed integer nonlinear programming (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1287/trsc.2015.0662 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:52:y:2018:i:1:p:210-227

Access Statistics for this article

More articles in Transportation Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().