Ann Oper Res https://doi.org/10.1007/s10479-018-2911-2 S.I.: SOME A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network 1 2 3 4 Michelle Dunbar · Simon Belieres · Nagesh Shukla · Mehrdad Amirghasemi · 4 5 Pascal Perez · Nishikant Mishra © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solu- tions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimi- sation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subprob- lem is difﬁcult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over
Annals of Operations Research – Springer Journals
Published: Jun 1, 2018
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