Management of empty containers by consignees in the hinterland
Benjamin Legros1, Jan Fransoo2, Oualid Jouini3
1EM Normandie Business School, France; 2Tilburg School of Economics and Managemnt; 3CentraleSupélec
This study analyses street-turn strategies for empty container repositioning in the hinterland using a double-ended queue model for matching operations. Containers arrive over time at the consignee and the demand for containers arises from the shipper. We prove that the matching time impacts matching proportion, while it marginally influences the consignee's inventory policy and cost per container. Also, the consignee's withholding level is mainly determined by the shipper's production rate.
Vehicle routing optimization with relay: an arc-based column generation approach
Alexandre Jacquillat1, Alexandria Schmid2, Kai Wang3
1MIT Sloan School of Management, United States of America; 2MIT Operations Research Center, United States of America; 3Heinz College, Carnegie Mellon University, United States of America
In relay-based logistics, orders are routed through pit-stops with a different driver assigned to each segment. This paper formulates an integer optimization model to coordinate driver, truck and driver movements. We develop an arc-based column generation algorithm which expands time-space networks iteratively until convergence. Results show that relay operations, combined with our algorithm, can lead to faster deliveries, better driver lifestyles, and a lower environmental footprint.
Optimizing order fulfillment via genetic programming generated policies
Sérgio Vasconcelos Castro1,2, Gonçalo Figueira1,2, Bernardo Almada-Lobo1,2
1INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Portugal; 2Faculty of Engineering, University of Porto
In online retail, fulfillment optimization is the problem of dynamically determining, for every new order, the fulfillment node that will fulfill every item within the order. To solve the problem, we propose a novel policy function approximation approach based on genetic programming to generate interpretable fulfillment policies. Results show that policies more simple than mathematical programming based ones are able to significantly improve over a myopic assignment.
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