scholarly journals A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems

2021 ◽  
Vol 13 (23) ◽  
pp. 13433
Author(s):  
Mohammed Elhenawy ◽  
Hesham A. Rakha ◽  
Youssef Bichiou ◽  
Mahmoud Masoud ◽  
Sebastien Glaser ◽  
...  
Keyword(s):  
Real Time ◽  
Low Income ◽  
Large Scale ◽  
Computational Time ◽  
Solution Quality ◽  
Suggested Approach ◽  
Bike Sharing ◽  
Deferred Acceptance Algorithm ◽  
Deferred Acceptance ◽  
Q 30

City bikes and bike-sharing systems (BSSs) are one solution to the last mile problem. BSSs guarantee equity by presenting affordable alternative transportation means for low-income households. These systems feature a multitude of bike stations scattered around a city. Numerous stations mean users can borrow a bike from one location and return it there or to a different location. However, this may create an unbalanced system, where some stations have excess bikes and others have limited bikes. In this paper, we propose a solution to balance BSS stations to satisfy the expected demand. Moreover, this paper represents a direct extension of the deferred acceptance algorithm-based heuristic previously proposed by the authors. We develop an algorithm that provides a delivery truck with a near-optimal route (i.e., finding the shortest Hamiltonian cycle) as an NP-hard problem. Results provide good solution quality and computational time performance, making the algorithm a viable candidate for real-time use by BSS operators. Our suggested approach is best suited for low-Q problems. Moreover, the mean running times for the largest instance are 143.6, 130.32, and 51.85 s for Q = 30, 20, and 10, respectively, which makes the proposed algorithm a real-time rebalancing algorithm.

scholarly journals Space-time clustering-based method to optimize shareability in real-time ride-sharing

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262499
Author(s):  
Negin Alisoltani ◽  
Mostafa Ameli ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computation Time ◽  
Clustering Method ◽  
Matching Problem ◽  
Solution Quality ◽  
Mobility Service ◽  
Ride Sharing ◽  
Large Scale Problems ◽  
Spatio Temporal

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

scholarly journals Maximum Closeness Centrality k-Clubs: A Study of Dock-Less Bike Sharing

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ali Rahim Taleqani ◽  
Chrysafis Vogiatzis ◽  
Jill Hough
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Closeness Centrality ◽  
Computational Time ◽  
Greedy Heuristic ◽  
New Paradigm ◽  
Coordination Costs ◽  
Bike Sharing

In this work, we investigate a new paradigm for dock-less bike sharing. Recently, it has become essential to accommodate connected and free-floating bicycles in modern bike-sharing operations. This change comes with an increase in the coordination cost, as bicycles are no longer checked in and out from bike-sharing stations that are fully equipped to handle the volume of requests; instead, bicycles can be checked in and out from virtually anywhere. In this paper, we propose a new framework for combining traditional bike stations with locations that can serve as free-floating bike-sharing stations. The framework we propose here focuses on identifying highly centralized k-clubs (i.e., connected subgraphs of restricted diameter). The restricted diameter reduces coordination costs as dock-less bicycles can only be found in specific locations. In addition, we use closeness centrality as this metric allows for quick access to dock-less bike sharing while, at the same time, optimizing the reach of service to bikers/customers. For the proposed problem, we first derive its computational complexity and show that it is NP-hard (by reduction from the 3-SATISFIABILITY problem), and then provide an integer programming formulation. Due to its computational complexity, the problem cannot be solved exactly in a large-scale setting, as is such of an urban area. Hence, we provide a greedy heuristic approach that is shown to run in reasonable computational time. We also provide the presentation and analysis of a case study in two cities of the state of North Dakota: Casselton and Fargo. Our work concludes with the cost-benefit analysis of both models (docked vs. dockless) to suggest the potential advantages of the proposed model.

scholarly journals Solving a Large-Scale Multi-Depot Vehicle Scheduling Problem in Urban Bus Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaomei Xu ◽  
Zhirui Ye ◽  
Jin Li ◽  
Chao Wang
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Set Partitioning ◽  
Vehicle Scheduling ◽  
Computational Time ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Solution Quality ◽  
Time Space ◽  
Improved Model

This study proposes an improved model and algorithm for the large-scale multi-depot vehicle scheduling problem (MDVSP) with departure-duration restrictions. In this study, the time-space network is applied to model the large-scale MDVSP. Considering that crews usually change shifts in the depot, departure-duration restrictions are added to the classic set-partitioning model to ensure that buses return to the depot when crews reach their working time limits. By embedding a preliminary exploring tactic to the shortest path faster algorithm (SPFA), researchers developed an improved large neighborhood search (LNS) algorithm to solve large-scale instances of MDVSP with departure-duration restrictions. The proposed methodology is applied to a real-life case in China and several test instances. The results show that the improved LNS algorithm can achieve very good performance in computational efficiency without deteriorating solution quality, which is important for large-scale systems. More specifically, the total cost of the improved LNS algorithm is approximately equal to branch-and-price, but the computational time is much shorter in the case study. For test instances with different number of timetabled trips (500, 1000, 1500, and 2000), the Quality Gap (QG) is very small, approximately 0.35%, 0.38%, 0.63%, and 0.93%, while the Efficiency Ratio (ER) reaches up to 2.89, 2.98, 3.65, and 3.79, respectively.

scholarly journals A Space-Time POD Basis Interpolation on Grassmann Manifolds for Parametric Simulations of Rigid-Viscoplastic FEM

2020 ◽  
Vol 318 ◽  
pp. 01043
Author(s):  
Orestis Friderikos ◽  
Marc Olive ◽  
Emmanuel Baranger ◽  
Dimitrios Sagris ◽  
Constantine N. David
Keyword(s):  
Real Time ◽  
Metal Forming ◽  
Large Scale ◽  
Space Time ◽  
Computational Time ◽  
High Fidelity ◽  
Grassmann Manifolds ◽  
Real Time Analysis ◽  
Time Basis ◽  
Proper Orthogonal

Parametric simulations of thermomechanical metal forming processes still remain computational costly and difficult due to inherent strong non-linearities. To this end, Reduced Order Models (ROMs) are introduced to decrease the computational time in large scale models and provide near-optimal solutions in acceptable times. ROMs based on the Proper Orthogonal Decomposition (POD) are usually capable of accurately reproducing the dynamics of high-fidelity FEM simulations and offer the potential for near real-time analysis. However, ROMs are not robust with respect to parameter changes and must often be rebuilt for each parameter variation. This work aims to interpolate ROM POD basis associated with a limited number of training points on Grassmann manifolds, so as to obtain a robust ROM corresponding to a target parameter. A novel Space-Time (ST) POD basis interpolation, where the reduced spatial and time basis are separately interpolated on Grassmann manifolds, is proposed. Good correlations of the ROM ST models with respect to their associated high-fidelity FEM counterpart simulations are found. Hence, application of the ROM adaptation method for near real-time metal forming simulations using off-line computed ROM POD databases can be possible.

scholarly journals An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

2017 ◽  
Vol 7 (6) ◽  
pp. 2260-2265 ◽  
Author(s):  
H. Jafarzadeh ◽  
N. Moradinasab ◽  
M. Elyasi
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Nearest Neighbor Search ◽  
Traveling Salesman ◽  
Computational Time ◽  
Test Problems ◽  
Solution Quality ◽  
Generalized Traveling Salesman Problem

The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

Real-Time Dynamic Origin-Destination Matrix Adjustment with Simulated and Actual Link Flows in Urban Networks

2003 ◽  
Vol 1857 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Theodore Tsekeris ◽  
Antony Stathopoulos
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Dynamic Traffic Assignment ◽  
Computational Time ◽  
Urban Networks ◽  
Time Dynamic ◽  
Assignment Model ◽  
Network Problems ◽  
Diverse Application ◽  
Scale Network

The efficiency and robustness of different real-time dynamic origin–destination (O-D) matrix adjustment algorithms were investigated when implemented in large-scale transportation networks. The proposed algorithms produce time-dependent O-D trip matrices based on the maximum-entropy trip departure times with simulated and actual observed link flows. Implementation of the algorithms, which are coupled with a quasi-dynamic traffic assignment model, indicated their convergent behavior and their potential for handling realistic urban-scale network problems in terms of both accuracy and computational time. The main factors influencing the numerical performance of each algorithm were identified and analyzed. Their relative efficiency was found to be particularly dependent on the level at which the assigned flows approximate the observed link flows. These results may provide insights into the suitability of each algorithm for diverse application domains, including freeways, small networks, and large-scale urban networks, where a different quality of O-D information is usually available.

scholarly journals Real-Time Simulation of a Cerebellar Scaffold Model on Graphics Processing Units

2021 ◽  
Vol 15 ◽  
Author(s):  
Rin Kuriyama ◽  
Claudia Casellato ◽  
Egidio D'Angelo ◽  
Tadashi Yamazaki
Keyword(s):  
Computer Simulation ◽  
Real Time ◽  
Graphics Processing Units ◽  
Large Scale ◽  
Computational Models ◽  
Computational Time ◽  
Time Signal ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Graphics Processing

Large-scale simulation of detailed computational models of neuronal microcircuits plays a prominent role in reproducing and predicting the dynamics of the microcircuits. To reconstruct a microcircuit, one must choose neuron and synapse models, placements, connectivity, and numerical simulation methods according to anatomical and physiological constraints. For reconstruction and refinement, it is useful to be able to replace one module easily while leaving the others as they are. One way to achieve this is via a scaffolding approach, in which a simulation code is built on independent modules for placements, connections, and network simulations. Owing to the modularity of functions, this approach enables researchers to improve the performance of the entire simulation by simply replacing a problematic module with an improved one. Casali et al. (2019) developed a spiking network model of the cerebellar microcircuit using this approach, and while it reproduces electrophysiological properties of cerebellar neurons, it takes too much computational time. Here, we followed this scaffolding approach and replaced the simulation module with an accelerated version on graphics processing units (GPUs). Our cerebellar scaffold model ran roughly 100 times faster than the original version. In fact, our model is able to run faster than real time, with good weak and strong scaling properties. To demonstrate an application of real-time simulation, we implemented synaptic plasticity mechanisms at parallel fiber–Purkinje cell synapses, and carried out simulation of behavioral experiments known as gain adaptation of optokinetic response. We confirmed that the computer simulation reproduced experimental findings while being completed in real time. Actually, a computer simulation for 2 s of the biological time completed within 750 ms. These results suggest that the scaffolding approach is a promising concept for gradual development and refactoring of simulation codes for large-scale elaborate microcircuits. Moreover, a real-time version of the cerebellar scaffold model, which is enabled by parallel computing technology owing to GPUs, may be useful for large-scale simulations and engineering applications that require real-time signal processing and motor control.

Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems

2013 ◽  
Vol 4 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Ziauddin Ursani ◽  
Daryl Essam ◽  
David Cornforth ◽  
Robert Stocker
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Large Scale ◽  
Computational Time ◽  
Small Scale ◽  
Problem Decomposition ◽  
Vehicle Routing Problems ◽  
Solution Quality ◽  
Routing Problems ◽  
Capacitated Vehicle

This paper is a continuation of two previous papers where the authors used Genetic Algorithm with automated problem decomposition strategy for small scale capacitated vehicle routing problems (CVRP) and vehicle routing problem with time windows (VRPTW). In this paper they have extended their scheme to large scale capacitated vehicle routing problems by introducing selective search version of the automated problem decomposition strategy, a faster genotype to phenotype translation scheme, and various search reduction techniques. The authors have shown that genetic algorithm used with automated problem decomposition strategy outperforms the GAs applied on the problem as a whole not only in terms of solution quality but also in terms of computational time on the large scale problems.

Dynamic Ride-Matching for Large-Scale Transportation Systems

2021 ◽  
pp. 036119812110494
Author(s):  
Taner Cokyasar ◽  
Felipe de Souza ◽  
Joshua Auld ◽  
Omer Verbas
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Learning Algorithm ◽  
Transportation Systems ◽  
Computational Time ◽  
System Optimum ◽  
Solution Quality ◽  
Optimum Approach ◽  
The Cost

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

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