Multi-Hop Ridematching optimization problem: Intelligent chromosome agent-driven approach

Cheikh, Sondes Ben; Hammadi, Slim

doi:10.1016/j.eswa.2016.06.001

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…In [62], an intelligent chromosome agent-driven approach is proposed to enhance the performance of the Evolutionary Algorithm in a solution to the multi-hop ride-matching optimisation problem. It can be defined as Autonomous and Intelligent Agents (E2AIA) where genetic operators will be driven by chromosome agents through a powerful negotiation protocol.…”

Section: Many-to-one Ridesharing Problemmentioning

confidence: 99%

“…On the other hand, WT for the driver and rider can be different in terms of waiting as a target. Minimisation of the driver's WT is to minimise the time used by a driver to wait for the rider at the pickup point while minimisation of the rider's WT is to minimise the time wasted to wait for the driver at the origin point [62]. Minimisation of the NT is for the smallest number of transfers of the rider between multiple vehicles along a trip [111].…”

Section: Descriptive Analysismentioning

confidence: 99%

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Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

et al. 2021

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The Shared Mobility Problems (SMP) with the rideshare concept based on sharing a vehicle are fast becoming a trend in many urban cities around the world. Examples of these problems are like ridesharing, carpooling, taxisharing, buspooling, vanpooling, and multi-modal ridesharing. This is the new way to access transportation services by those who are propelling the sharing economy, where access rather than ownership is the new norm. This paper provides a systematic review of SMP using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method. A total of 110 papers from the last decade are selected from 12 abstracts and citation databases to be reviewed and classified. This is done based on the problem types, variants, characteristics, and solution approaches. The current trends and analysis of the survey findings are also summarised. From this systematic review, it is observed that both the time window and multi-objective problems are popular among the researchers, while the minimisation of the total cost is the main concern in the literature of the SMP. Both static and dynamic cases of the SMP are the most researched where heuristic and metaheuristic approaches are widely adopted by the researchers in the literature. Finally, challenges and suggestions for future work are discussed and highlighted.

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Section: Many-to-one Ridesharing Problemmentioning

confidence: 99%

Section: Descriptive Analysismentioning

confidence: 99%

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

et al. 2021

View full text Add to dashboard Cite

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“…Dynamic ride-matching includes many parameters, and this renders the problem to be non-deterministic polynomial-time hard (NP-hard) [15][16][17]. Therefore, many solutions to the ride-matching problem that have been proposed in the literature use either heuristics or metaheuristics [6,[15][16][17][18][19][20][21][22][23]. Although heuristic and meta-heuristic methods offer feasible processing times, they may not find the best possible matches.…”

Section: Related Studiesmentioning

confidence: 99%

Matching algorithm for improving ride-sharing by incorporating route splits and social factors

2020

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Increasing traffic congestion and the advancements in technology have fostered the growth of alternative transportation modes such as dynamic ride-sharing. Smartphone technologies have enabled dynamic ride-sharing to thrive, as this type of transportation aims to establish ride matches between people with similar routes and schedules on short notice. Many automated matching methods are designed to improve system performance; such methods include minimizing process time, minimizing total system cost or maximizing total distance savings. However, the results may not provide the maximum benefits for the participants. This paper intends to develop an algorithm for optimizing matches when considering participants' gender, age, employment status and social tendencies. The proposed matching algorithm also splits unmatched parts of drivers' routes and creates new travel requests to find additional matches using these unmatched parts. Accordingly, this paper performs an extensive simulation study to assess the performance of the proposed algorithm. The simulation results indicate that route splits may increase the number of matches significantly when there is a shortage of drivers. Furthermore, the paper demonstrates the effects and potential benefits of utilizing a social compatibility score in the objective function.

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“…On the basis of the above analysis, in order to explore the optimal distribution of trajectory points, the intelligent optimization algorithms are used to optimize the positions to obtain the best trajectory reducibility. Among many intelligent optimization algorithms [38][39][40], the PSO [41] and GA [42] are chosen due to their fast convergence and robustness. e primary purpose of optimization is to make the generated trajectory closer to the original design trajectory.…”

Section: Interpolation Points Optimizationmentioning

confidence: 99%

Analysis and Optimization of Interpolation Points for Quadruped Robots Joint Trajectory

et al. 2020

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Trajectory planning is the foundation of locomotion control for quadruped robots. This paper proposes a bionic foot-end trajectory which can adapt to many kinds of terrains and gaits based on the idea of trajectory planning combining Cartesian space with joint space. Trajectory points are picked for inverse kinematics solution, and then quintic polynomials are used to plan joint space trajectories. In order to ensure that the foot-end trajectory generated by the joint trajectory planning is closer to the original Cartesian trajectory, the distributions of the interpolation point are analyzed from the spatial domain to temporal domain. An evaluation function was established to assess the closeness degree between the actual trajectory and the original curve. Subsequently, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) for the points selection are used to obtain a more precise trajectory. Simulation and physical prototype experiments were included to support the correctness and effectiveness of the algorithms and the conclusions.

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Multi-Hop Ridematching optimization problem: Intelligent chromosome agent-driven approach

Cited by 7 publications

References 26 publications

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

Matching algorithm for improving ride-sharing by incorporating route splits and social factors

Analysis and Optimization of Interpolation Points for Quadruped Robots Joint Trajectory

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