Scheduled service network design with resource acquisition and management

Teodor Gabriel Crainic (), Mike Hewitt (), Michel Toulouse and Duc Minh Vu
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Teodor Gabriel Crainic: Université du Québec à Montréal
Mike Hewitt: Quinlan School of Business, Loyola University Chicago
Michel Toulouse: Vietnamese-German University
Duc Minh Vu: Loyola University Chicago

EURO Journal on Transportation and Logistics, 2018, vol. 7, issue 3, No 4, 277-309

Abstract: Abstract We present a new planning model for freight consolidation carriers, one that links strategic, resource acquisition, and allocation decisions with tactical, service network design-related decisions. Specifically, such as service network design models that recognize resource constraints, the model selects services and routes both commodities and the resources needed to support the services that transport them. In addition, the model recognizes that resources can be grouped into types that differ from one another with respect to capabilities, e.g., speeds, capacities, scheduling rules, etc. Ultimately, along with recognizing resource constraints, the model also makes strategic decisions such as how many resources of each type should be acquired, to what terminal new resources should be assigned, and which existing terminal-based resources should be reassigned. As such, the model can be used from a strategic planning, resource acquisition, mixing, and allocation perspective as it provides an estimate of the impact of such decisions on transportation costs. We extend a matheuristic for a service network design problem with a fixed set (both in number and allocation) of resources of a single type to one that can also make these acquisition and allocation decisions for multiple types of resource. Then, with an extensive computational study, we demonstrate the efficacy of the matheuristic and benchmark its performance against both a leading commercial solver and a column generation-based heuristic. Finally, we perform an extensive computational study to understand how the resource-related and service network design-related components of the model interact, including how freight volumes and cost structures impact how many resources should beacquired.

Keywords: Service network design; Fleet sizing and management; Matheuristics; Slope scaling; Column generation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s13676-017-0103-x

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