A hybrid algorithm for the static bike-sharing re-positioning problem based on an effective clustering strategy

Lv, Chang; Zhang, Chaoyong; Lian, Kunlei; Ren, Yaping; Meng, Leilei

doi:10.1016/j.trb.2020.07.004

Cited by 32 publications

(8 citation statements)

References 25 publications

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“…Another difference is that DBRP considers the temporal variation of the demand throughout the day and adopts different rebalancing strategies based on usage forecasting [13,14]. Rebalancing problems are variants of the classical vehicle routing problem (VRP) and are NP-hard problems [15]. Since there are no exact algorithms for large-scale rebalancing problems [16], researchers usually adopt heuristic algorithms [17][18][19].…”

Section: Studies Related To the Maintenance Operation Of Sharedmentioning

confidence: 99%

“…Several studies adopt the cluster-based algorithm to solve the city-scale rebalancing problem. Lv et al [15] the adaptive variable neighborhood search algorithm for routing optimization. Lv et al [20] proposed a fuzzy clustering strategy considering distance and inventory factors.…”

Section: Studies Related To the Maintenance Operation Of Sharedmentioning

confidence: 99%

“…(12) while max (Adjustment k ) >0 do ( 13) for each k ∈ K and Adjustment k ≥ 0 (14) Calculate the distance from each point to the center of the k-th cluster and sort it in descending order. (15) Take the top Adjustment k in the distance order. ( 16)…”

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confidence: 99%

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Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Zheng

Yang

Jiang

et al. 2023

Journal of Advanced Transportation

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Shared bikes can help cities achieve carbon neutrality goals. Cleaning and disinfection are vital procedures of the maintenance of shared bikes, especially during the COVID-19 pandemic because shared bikes could be a transmission intermediary of viruses. This study proposes an optimization model of the cleaning and disinfection scheme of the dockless shared bikes. The disinfection is assumed to be performed at night, when the usage is lowest. By regarding the disinfection staff as traveling salesmen, the model is formulated as an extension of the Multidepot Multiple Traveling Salesman Problem (MDMTSP). The objective function is to minimize the total cost; which consists of the cost associated with the working time and per-capita cost of the disinfection staff. A heuristic algorithm combining k -means clustering and genetic algorithm (K-GA) is adopted to find the lower bound solution. Then, the K-GA-adjustment algorithm has been adopted to find the solutions that satisfy the constraints. To reduce the computing time needed, an approximate function for the lower bound of the optimal number of disinfection staff is obtained by constructing a Continuous Approximation (CA) model. A case study based on real location data of shared bikes in Chengdu, China, is performed to show how the maintenance department could adopt the optimization framework to design an efficient scheme to clean and disinfect the shared bikes.

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Section: Studies Related To the Maintenance Operation Of Sharedmentioning

confidence: 99%

Section: Studies Related To the Maintenance Operation Of Sharedmentioning

confidence: 99%

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Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Zheng

Yang

Jiang

et al. 2023

Journal of Advanced Transportation

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“…The service level requirements of bike-docking stations have been frequently explored in the literature. The general allocation of bikes to dock stations is the most common theme in existing studies ( [2], [21], [37], [41], [44], [59], [60], [66]). In a typical BSS service-level problem, there is a trade-off between attaining a high customer level (availability of bikes at any time), and the costs of allocating bikes to stations and purchasing a large inventory of bikes.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Bike Usage and Optimizing Operations at Repair Shops in Bike Sharing Systems

Alzaman

Aljuneidi

2023

IEEE Access

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Supply chain responsiveness and big data analytics (BDA) have garnered considerable interest in academia and among practitioners. BDA helps researchers understand the current challenges in data management, including the high volume, velocity, and variety of data. This study is concerned with improving the responsiveness of supply chain networks to bike-sharing systems (BSS), which exhibit BDA characteristics. To address the challenges of forecasting bike usage and accordingly optimizing repair shop operations, we analyze multi-factor BSS data (Data from Washington D.C. BSS available to public), wherein attributes, such as weather conditions, registration, humidity, date, and time, are present. We use machine learning algorithms, such as neural networks, decision-tree-based regression, K-nearest neighbor, support vectors, and ensemble random forest, to predict bike usage and repair. This work contests the results and demonstrates the effectiveness of combining machine learning with supply chain network design. Supply chain networks model bike repairs by means of capacity extensions, which entails a nonlinear problem. In this study, we utilize a gradient search to solve a nonlinear supply chain network model. By enabling capacity extension, bike repair shops within the BSS exhibit a promising 50 % reduction in lead repair time. Furthermore, a 25 % overall throughput increase in BSS is achieved. Ultimately, this study demonstrates the importance of operational flexibility in responding to big data challenges.INDEX TERMS Big data, bike sharing, flexibility, machine leaning, supply chain network design.

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“…Forma et al [38] adapt a cluster-first route-second approach to subdivide the problem by vehicle-associated zones. With the same idea of dividing the network into different zones, Lv et al [39] propose a hybrid algorithm for BRP. A destroy and repair algorithm is developed to improve the clusters, and an adaptive variable neighborhood search algorithm is designed to conduct intracluster and intercluster vehicle routing optimization.…”

Section: Static Bicycle Rebalancing Problemmentioning

confidence: 99%

Modelling of User Behaviour for Static Rebalancing of Bike Sharing System: Transfer of Demand from Bike-Shortage Stations to Neighbouring Stations

Affonso

Couffin

Leclaire

2021

Journal of Advanced Transportation

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Bike sharing systems are becoming more and more common around the world. One of the main difficulties is to ensure the availability of bicycles in order to satisfy users. To achieve this objective, managers of these systems set up rebalancing vehicles that displace bicycles to stations that are likely to be in a situation of bike shortage. In order to determine which stations must be supplied on a priority basis and the number of bicycles to be supplied (named in this paper as rebalancing plan), the aim is generally to reduce the lost demand for each station, i.e., the gap between the demand for bicycles and the number of bicycles at a station. On the one hand, this paper proposes an algorithm that evaluates the lost demand in a more realistic way, by describing the behaviour of users faced with a bike-shortage station. It takes into account the possibility that a proportion of users who cannot find bicycles will move to a neighbouring station that is not empty. This proportion depends on the distance between stations and corresponds to the number of users willing to walk a given distance to a neighbouring station. On the other hand, this algorithm provides the value of the objective function to be minimized to a static rebalancing plan algorithm based on a Random Search metaheuristic. The quantities of bicycles to be picked up and dropped off at each station are calculated in a static rebalancing context. The calculation of lost demand based on this algorithm, which simulates user behaviour, was compared with that one obtained by the classical method on a real numerical example obtained from the open data of Parisian Vélibʼ (more than 1200 stations). In addition, the efficiency of the rebalancing algorithm coupled with the user behaviour simulation algorithm was evaluated on this numerical example and allowed to obtain very good results compared to the rebalancing performed by the system operator.

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A hybrid algorithm for the static bike-sharing re-positioning problem based on an effective clustering strategy

Cited by 32 publications

References 25 publications

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Predicting Bike Usage and Optimizing Operations at Repair Shops in Bike Sharing Systems

Modelling of User Behaviour for Static Rebalancing of Bike Sharing System: Transfer of Demand from Bike-Shortage Stations to Neighbouring Stations

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