A Novel Approach to Optimize the Ridesharing Problem Using Genetic Algorithm

Rangriz, S.; Davoodi, M.; Saberian, J

doi:10.5194/isprs-archives-xlii-4-w18-875-2019

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Genetic Algorithm (GA) is a popular meta-heuristic approach since it can provide solutions for large problems in a shorter amount of time than LP. They also provide good performance for multi-criteria objectives (Rangriz, Davoodi, and Saberian 2019). In the context of ridesharing, (Herbawi and Weber 2011) has compared the solution efficiency of a GA and several variants of Ant Colony Optimization (ACO).…”

Section: Related Workmentioning

confidence: 99%

SKATE : Successive Rank-based Task Assignment for Proactive Online Planning

Nedelmann,

Lacan,

Chanel

2024

ICAPS

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The development of online applications for services such as package delivery, crowdsourcing, or taxi dispatching has caught the attention of the research community to the domain of online multi-agent multi-task allocation. In online service applications, tasks (or requests) to be performed arrive over time and need to be dynamically assigned to agents. Such planning problems are challenging because: (i) few or almost no information about future tasks is available for long-term reasoning; (ii) agent number, as well as, task number can be impressively high; and (iii) an efficient solution has to be reached in a limited amount of time. In this paper, we propose SKATE, a successive rank-based task assignment algorithm for online multi-agent planning. SKATE can be seen as a meta-heuristic approach which successively assigns a task to the best-ranked agent until all tasks have been assigned. We assessed the complexity of SKATE and showed it is cubic in the number of agents and tasks. To investigate how multi-agent multi-task assignment algorithms perform under a high number of agents and tasks, we compare three multi-task assignment methods in synthetic and real data benchmark environments: Integer Linear Programming (ILP), Genetic Algorithm (GA), and SKATE. In addition, a proactive approach is nested to all methods to determine near-future available agents (resources) using a receding-horizon. Based on the results obtained, we can argue that the classical ILP offers the better quality solutions when treating a low number of agents and tasks, i.e. low load despite the receding-horizon size, while it struggles to respect the time constraint for high load. SKATE performs better than the other methods in high load conditions, and even better when a variable receding-horizon is used.

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Section: Related Workmentioning

confidence: 99%

SKATE : Successive Rank-based Task Assignment for Proactive Online Planning

Nedelmann,

Lacan,

Chanel

2024

ICAPS

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“…A common one is the Linear Programming [25] [21]. Evolutionary algorithm have also been used due to their strong performance for multi-criteria objective [23]. Given the nature of the problem we are treating in this paper, this solving approach interests us as we will detail on Section 4.…”

Section: Related Workmentioning

confidence: 99%

“…A way to treat this problem online is to use metaheuristic approaches, such as Genetic Algorithm, as we will present in next section. Moreover, metaheuristic approaches allows to use multi-objective criteria costs to evaluate (multi-)task assignments solutions [23].…”

Section: Introductionmentioning

confidence: 99%

Online Proactive Multi-Task Assignment with Resource Availability Anticipation

Conforto Nedelmann,

Lacan,

Chanel

2023

Electron. Proc. Theor. Comput. Sci.

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With the emergence of services and online applications as taxi dispatching, crowdsourcing, package or food delivery, industrials and researchers are paying attention to the online multi-task assignment optimization field to quickly and efficiently met demands. In this context, this paper is interested in the multi-task assignment problem where multiple requests (e.g. tasks) arrive over time and must be dynamically matched to (mobile) agents. This optimization problem is known to be NP-hard. In order to treat this problem with a proactive mindset, we propose to use a receding-horizon approach to determine which resources (e.g. taxis, mobile agents, drones, robots) would be available within this (possibly dynamic) receding-horizon to meet the current set of requests (i.e. tasks) as good as possible. Contrarily to several works in this domain, we have chosen to make no assumption concerning future locations of requests. To achieve fast optimized online solutions in terms of costs and amount of allocated tasks, we have designed a genetic algorithm based on a fitness function integrating the traveled distance and the age of the requests. We compared our proactive multi-task assignment with resource availability anticipation approach with a classical reactive approach. The results obtained in two benchmark problems, one synthetic and another based on real data, show that our resource availability anticipation method can achieve better results in terms of costs (e.g. traveled distance) and amount of allocated tasks than reactive approaches while decreasing resources idle time.

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Impacts of ride-hailing on energy and the environment: a systematic review

Sheldon,

Dua

2024

Environ. Res. Lett.

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Ride-hailing has expanded substantially around the globe over the last decade and is likely to be an integral part of future transportation systems. We perform a systematic review of the literature on energy and environmental impacts of ride-hailing. In general, empirical papers find that ride-hailing has increased congestion, vehicle miles traveled, and emissions. However, theoretical papers overwhelmingly point to the potential for energy and emissions reductions in a future with increased electrification and pooling. Future research addressing the gap between observed and predicted impacts is warranted.

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A Novel Approach to Optimize the Ridesharing Problem Using Genetic Algorithm

Cited by 4 publications

References 10 publications

SKATE : Successive Rank-based Task Assignment for Proactive Online Planning

SKATE : Successive Rank-based Task Assignment for Proactive Online Planning

Online Proactive Multi-Task Assignment with Resource Availability Anticipation

Impacts of ride-hailing on energy and the environment: a systematic review

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