ant colony optimization on crowdsourced delivery trip consolidation

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ID: 243503
2017
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Abstract
Common practice in crowdsourced delivery services is through direct delivery. That  is by dispatching direct trip to a driver nearby the origin location. The total distance can be reduced through multiple pickup and delivery by increasing the number of requests in a trip. The research implements exact algorithm to solve the consolidation problem with up to 3 requests in a trip. Greedy heuristic is performed to construct initial route based on highest savings. The result is then optimized using Ant Colony Optimization (ACO). Four scenarios are compared. A direct delivery scenarios and three multiple pickup and delivery scenarios. These include 2-consolidated delivery, 3-consolidated delivery, and 3-consolidated delivery optimized with ACO. Four parameters are used to evaluate using Analytical Hierarchical Process (AHP). These include the number of trips, total distance, total duration, and security concerns. The case study is based on Yogyakarta area for a whole day. The final route optimized with ACO shows 178 requests can be completed in 94 trips. Compared to direct delivery, consolidation can provides savings up to 20% in distance and 14% in duration. The evaluation result using AHP shows that ACO scenario is the best scenario.
Reference Key
paskalathis2017ijccsant Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Victor Paskalathis;Azhari SN
Journal current medicinal chemistry
Year 2017
DOI
10.22146/ijccs.16631
URL
https://jurnal.ugm.ac.id/ijccs/article/view/16631
Keywords
ant colony optimization computer science

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