Competitive ratios for online multi-capacity ridesharing
In multi-capacity ridesharing, multiple requests (e.g., customers, food items, parcels) with different origin and destination pairs travel in one resource. In recent years, online multi-capacity ridesharing services (i.e., where assignments are made online) like Uber-pool, foodpanda, and on-demand s...
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sg-smu-ink.sis_research-71202021-09-29T12:26:13Z Competitive ratios for online multi-capacity ridesharing LOWALEKAR, Meghna VARAKANTHAM, Pradeep JAILLET, Patrick In multi-capacity ridesharing, multiple requests (e.g., customers, food items, parcels) with different origin and destination pairs travel in one resource. In recent years, online multi-capacity ridesharing services (i.e., where assignments are made online) like Uber-pool, foodpanda, and on-demand shuttles have become hugely popular in transportation, food delivery, logistics and other domains. This is because multi-capacity ridesharing services benefit all parties involved – the customers (due to lower costs), the drivers (due to higher revenues) and the matching platforms (due to higher revenues per vehicle/resource). Most importantly these services can also help reduce carbon emissions (due to fewer vehicles on roads). Online multi-capacity ridesharing is extremely challenging as the underlying matching graph is no longer bipartite (as in the unit-capacity case) but a tripartite graph with resources (e.g., taxis, cars), requests and request groups (combinations of requests that can travel together). The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i.e., a request can be part of at most one request group in the final assignment). While there have been myopic heuristic approaches employed for solving the online multi-capacity ridesharing problem, they do not provide any guarantees on the solution quality. To that end, this paper presents the first approach with bounds on the competitive ratio for online multi-capacity ridesharing (when resources rejoin the system at their initial location/depot after serving a group of requests). The competitive ratio is : (i) 0.31767 for capacity 2; and (ii) γ for any general capacity κ, where γ is a solution to the equation γ = (1 −γ ) κ+1 . 2020-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6117 https://ink.library.smu.edu.sg/context/sis_research/article/7120/viewcontent/Competitive_Ratios_for_Online_Multi_capacity_Ridesharing.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Competitive ratio Food delivery Carbon emissions Urban Studies and Planning |
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Competitive ratio Food delivery Carbon emissions Urban Studies and Planning LOWALEKAR, Meghna VARAKANTHAM, Pradeep JAILLET, Patrick Competitive ratios for online multi-capacity ridesharing |
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In multi-capacity ridesharing, multiple requests (e.g., customers, food items, parcels) with different origin and destination pairs travel in one resource. In recent years, online multi-capacity ridesharing services (i.e., where assignments are made online) like Uber-pool, foodpanda, and on-demand shuttles have become hugely popular in transportation, food delivery, logistics and other domains. This is because multi-capacity ridesharing services benefit all parties involved – the customers (due to lower costs), the drivers (due to higher revenues) and the matching platforms (due to higher revenues per vehicle/resource). Most importantly these services can also help reduce carbon emissions (due to fewer vehicles on roads). Online multi-capacity ridesharing is extremely challenging as the underlying matching graph is no longer bipartite (as in the unit-capacity case) but a tripartite graph with resources (e.g., taxis, cars), requests and request groups (combinations of requests that can travel together). The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i.e., a request can be part of at most one request group in the final assignment). While there have been myopic heuristic approaches employed for solving the online multi-capacity ridesharing problem, they do not provide any guarantees on the solution quality. To that end, this paper presents the first approach with bounds on the competitive ratio for online multi-capacity ridesharing (when resources rejoin the system at their initial location/depot after serving a group of requests). The competitive ratio is : (i) 0.31767 for capacity 2; and (ii) γ for any general capacity κ, where γ is a solution to the equation γ = (1 −γ ) κ+1 . |
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LOWALEKAR, Meghna VARAKANTHAM, Pradeep JAILLET, Patrick |
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LOWALEKAR, Meghna VARAKANTHAM, Pradeep JAILLET, Patrick |
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LOWALEKAR, Meghna |
title |
Competitive ratios for online multi-capacity ridesharing |
title_short |
Competitive ratios for online multi-capacity ridesharing |
title_full |
Competitive ratios for online multi-capacity ridesharing |
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Competitive ratios for online multi-capacity ridesharing |
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Competitive ratios for online multi-capacity ridesharing |
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competitive ratios for online multi-capacity ridesharing |
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Institutional Knowledge at Singapore Management University |
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2020 |
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https://ink.library.smu.edu.sg/sis_research/6117 https://ink.library.smu.edu.sg/context/sis_research/article/7120/viewcontent/Competitive_Ratios_for_Online_Multi_capacity_Ridesharing.pdf |
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