Abstract
We consider the problem of matching multiple shippers and transporters participating in an online B2B last-mile logistics platform in an emerging market. Each shipper places a bid that is made up of multiple jobs, where each job comprises key information like the weight, volume, pickup and delivery locations, and time windows. Each transporter specifies its vehicle capacity, available time periods, and a cost structure. We formulate the mathematical model and provide a Branch-and-Cut approach to solve small-scale problem instances exactly and larger scale instances heuristically using an Adaptive Large Neighbourhood Search approach. To increase the win percentage of both shippers and transporters, we propose an extension of the single round auction to a second-round for failed bids. Using our models and the results obtained, we present interesting managerial insights that are helpful to platform participants. For example, due to the cost structure, there is little impact on profitability with time varying speeds, but time windows play a significant role in the profitability of the system. And there are tangible benefits that all platform participants (shippers, transporters and operator) can gain by extending single-round auction to two rounds.
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