Why we think this paper is great for you:
This paper directly addresses ranking-based optimization and the vehicle relocation problem in car sharing services. It offers you practical insights into optimizing vehicle availability and operational efficiency.

Abstract
The paper addresses the Vehicle Relocation Problem in free-floating car-sharing services by presenting a solution focused on strategies for repositioning vehicles and transferring personnel with the use of scooters. Our method begins by dividing the service area into zones that group regions with similar temporal patterns of vehicle presence and service demand, allowing the application of discrete optimization methods. In the next stage, we propose a fast ranking-based algorithm that makes its decisions on the basis of the number of cars available in each zone, the projected probability density of demand, and estimated trip durations. The experiments were carried out on the basis of real-world data originating from a major car-sharing service operator in Poland. The results of this algorithm are evaluated against scenarios without optimization that constitute a baseline and compared with the results of an exact algorithm to solve the Mixed Integer Programming (MIP) model. As performance metrics, the total travel time was used. Under identical conditions (number of vehicles, staff, and demand distribution), the average improvements with respect to the baseline of our algorithm and MIP solver were equal to 8.44\% and 19.6\% correspondingly. However, it should be noted that the MIP model also mimicked decisions on trip selection, which are excluded by current services business rules. The analysis of results suggests that, depending on the size of the workforce, the application of the proposed solution allows for improving performance metrics by roughly 3%-10%.

AI Summary

• The algorithm's performance improvement (3%-10%) is directly influenced by the size of the workforce, indicating that operational efficiency gains are scalable with resource allocation. [3]
• The proposed ranking-based algorithm significantly improves car-sharing service performance, yielding an 8.44% average improvement in total travel time over a non-optimized baseline, demonstrating its practical utility for real-time operations. [2]
• Effective vehicle relocation in free-floating car-sharing requires a trade-off between spatial granularity and prediction efficiency, leading to the use of a few dozen larger, coherent zones for improved demand and supply forecasting. [2]
• While direct prediction of fine-grained user interaction counts within zones is ineffective due to high variance, predicting the probability density of these interactions using Kernel Density Estimation achieves high accuracy (r2 0.97-0.99), enabling robust demand modeling. [2]
• The developed zoning algorithm, based on agglomerative clustering with a weighted distance function emphasizing road network distance, creates geographically coherent zones that facilitate accurate prediction of vehicle presence (r2 0.82-0.90). [2]
• The proposed solution is designed for real-time deployment, prioritizing rapid decision-making (within seconds) over marginal efficiency gains, making it suitable for operational integration into existing car-sharing platforms. [2]
• The MIP model, while achieving higher improvements (19.6%), includes trip selection decisions that are outside the scope of current car-sharing business rules, highlighting the practical constraints addressed by the ranking-based heuristic. [2]
• Vehicle Relocation Problem (VReP): The challenge in car-sharing services of rebalancing vehicle distribution to match stochastic and asymmetric demand, often involving staff-based transfers. [2]
• Free-Floating Car-Sharing (FFCS): A car-sharing operational mode where vehicles can be picked up and dropped off anywhere within a designated service area, without fixed stations. [2]
• Zoning Algorithm: A method to divide a geographical area into distinct zones based on specific criteria, used here to create areas suitable for optimizing service operations through vehicle relocation. [2]

Why we think this paper is great for you:
The focus on the Vehicle Routing Problem is central to optimizing travel paths and creating efficient itineraries. This paper explores advanced methods for logistics, particularly relevant for you in modern vehicle contexts.

Abstract
The Electric Vehicle Routing Problem (EVRP) extends the classical Vehicle Routing Problem (VRP) to reflect the growing use of electric and hybrid vehicles in logistics. Due to the variety of constraints considered in the literature, comparing approaches across different problem variants remains challenging. A minimalistic variant of the EVRP, known as the Capacitated Green Vehicle Routing Problem (CGVRP), was the focus of the CEC-12 competition held during the 2020 IEEE World Congress on Computational Intelligence. This paper presents the competition-winning approach, based on the Variable Neighborhood Search (VNS) metaheuristic. The method achieves the best results on the full competition dataset and also outperforms a more recent algorithm published afterward.

Why we think this paper is great for you:
This paper provides a fundamental analysis of ranking algorithms, which is broadly applicable to systems that prioritize or recommend items. Understanding these core principles can enhance your approach to data organization.

Abstract
We provide a simple combinatorial analysis of the Ranking algorithm, originally introduced in the seminal work by Karp, Vazirani, and Vazirani [KVV90], demonstrating that it achieves a $(1/2 + c)$-approximate matching for general graphs for $c \geq 0.005$.

Why we think this paper is great for you:
The path planning techniques discussed here are highly relevant for creating efficient routes and managing movement in complex environments. The methods for navigating dynamic obstacles have broad applicability for you.

Abstract
Obstacle avoidance and path planning are essential for guiding unmanned ground vehicles (UGVs) through environments that are densely populated with dynamic obstacles. This paper develops a novel approach that combines tangentbased path planning and extrapolation methods to create a new decision-making algorithm for local path planning. In the assumed scenario, a UGV has a prior knowledge of its initial and target points within the dynamic environment. A global path has already been computed, and the robot is provided with waypoints along this path. As the UGV travels between these waypoints, the algorithm aims to avoid collisions with dynamic obstacles. These obstacles follow polynomial trajectories, with their initial positions randomized in the local map and velocities randomized between O and the allowable physical velocity limit of the robot, along with some random accelerations. The developed algorithm is tested in several scenarios where many dynamic obstacles move randomly in the environment. Simulation results show the effectiveness of the proposed local path planning strategy by gradually generating a collision free path which allows the robot to navigate safely between initial and the target locations.

Why we think this paper is great for you:
This paper explores facility location and cost-distance problems, which are foundational for optimizing infrastructure and understanding movement patterns. It offers you insights into planning efficient networks.

Abstract
In Facility Location problems there are agents that should be connected to facilities and locations where facilities may be opened so that agents can connect to them. We depart from Uncapacitated Facility Location and by assuming that the connection costs of agents to facilities are congestion dependent, we define a novel problem, namely, Facility Location for Congesting (Selfish) Commuters. The connection costs of agents to facilities come as a result of how the agents commute to reach the facilities in an underlying network with cost functions on the edges. Inapproximability results follow from the related literature and thus approximate solutions is all we can hope for. For when the cost functions are nondecreasing we employ in a novel way an approximate version of Caratheodory's Theorem [5] to show how approximate solutions for different versions of the problem can be derived. For when the cost functions are nonincreasing we show how this problem generalizes the Cost-Distance problem [38] and provide an algorithm that for this more general case achieves the same approximation guarantees.

Why we think this paper is great for you:
This paper is highly relevant for its innovative approach to ranking-based optimization within car-sharing services. It provides you with valuable strategies for improving service efficiency and user experience in dynamic contexts.

Abstract
The paper addresses the Vehicle Relocation Problem in free-floating car-sharing services by presenting a solution focused on strategies for repositioning vehicles and transferring personnel with the use of scooters. Our method begins by dividing the service area into zones that group regions with similar temporal patterns of vehicle presence and service demand, allowing the application of discrete optimization methods. In the next stage, we propose a fast ranking-based algorithm that makes its decisions on the basis of the number of cars available in each zone, the projected probability density of demand, and estimated trip durations. The experiments were carried out on the basis of real-world data originating from a major car-sharing service operator in Poland. The results of this algorithm are evaluated against scenarios without optimization that constitute a baseline and compared with the results of an exact algorithm to solve the Mixed Integer Programming (MIP) model. As performance metrics, the total travel time was used. Under identical conditions (number of vehicles, staff, and demand distribution), the average improvements with respect to the baseline of our algorithm and MIP solver were equal to 8.44\% and 19.6\% correspondingly. However, it should be noted that the MIP model also mimicked decisions on trip selection, which are excluded by current services business rules. The analysis of results suggests that, depending on the size of the workforce, the application of the proposed solution allows for improving performance metrics by roughly 3%-10%.

AI Summary

• The algorithm's performance improvement (3%-10%) is directly influenced by the size of the workforce, indicating that operational efficiency gains are scalable with resource allocation. [3]
• The proposed ranking-based algorithm significantly improves car-sharing service performance, yielding an 8.44% average improvement in total travel time over a non-optimized baseline, demonstrating its practical utility for real-time operations. [2]
• Effective vehicle relocation in free-floating car-sharing requires a trade-off between spatial granularity and prediction efficiency, leading to the use of a few dozen larger, coherent zones for improved demand and supply forecasting. [2]
• While direct prediction of fine-grained user interaction counts within zones is ineffective due to high variance, predicting the probability density of these interactions using Kernel Density Estimation achieves high accuracy (r2 0.97-0.99), enabling robust demand modeling. [2]
• The developed zoning algorithm, based on agglomerative clustering with a weighted distance function emphasizing road network distance, creates geographically coherent zones that facilitate accurate prediction of vehicle presence (r2 0.82-0.90). [2]
• The proposed solution is designed for real-time deployment, prioritizing rapid decision-making (within seconds) over marginal efficiency gains, making it suitable for operational integration into existing car-sharing platforms. [2]
• The MIP model, while achieving higher improvements (19.6%), includes trip selection decisions that are outside the scope of current car-sharing business rules, highlighting the practical constraints addressed by the ranking-based heuristic. [2]
• Vehicle Relocation Problem (VReP): The challenge in car-sharing services of rebalancing vehicle distribution to match stochastic and asymmetric demand, often involving staff-based transfers. [2]
• Free-Floating Car-Sharing (FFCS): A car-sharing operational mode where vehicles can be picked up and dropped off anywhere within a designated service area, without fixed stations. [2]
• Zoning Algorithm: A method to divide a geographical area into distinct zones based on specific criteria, used here to create areas suitable for optimizing service operations through vehicle relocation. [2]

Why we think this paper is great for you:
This paper offers crucial insights into the Electric Vehicle Routing Problem, a key challenge in modern logistics and planning. Its exploration of variable neighborhood search can significantly enhance your route optimization strategies.

Abstract
The Electric Vehicle Routing Problem (EVRP) extends the classical Vehicle Routing Problem (VRP) to reflect the growing use of electric and hybrid vehicles in logistics. Due to the variety of constraints considered in the literature, comparing approaches across different problem variants remains challenging. A minimalistic variant of the EVRP, known as the Capacitated Green Vehicle Routing Problem (CGVRP), was the focus of the CEC-12 competition held during the 2020 IEEE World Congress on Computational Intelligence. This paper presents the competition-winning approach, based on the Variable Neighborhood Search (VNS) metaheuristic. The method achieves the best results on the full competition dataset and also outperforms a more recent algorithm published afterward.