A Hybrid Metaheuristic to Solve the Resource Allocation Problem in Bike Sharing Systems

Vogel, Patrick; Neumann-Saavedra, Bruno Albert; Mattfeld, Dirk C.

doi:10.1007/978-3-319-07644-7_2

Cited by 30 publications

(19 citation statements)

References 23 publications

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“…All of these studies considered models based on a single station, meaning that each station's inventory target level was calculated independently of the others and the interactions among stations were neglected. In Vogel et al (2014), the inventory levels of all stations were set simultaneously, but these authors also ignored the influence of interaction on the system because they treated shortages of bicycles and lockers as lost sales. They determined the stations' inventory levels so as to minimize the total expected operation costs of the system due to relocation while satisfying a minimal level of service.…”

Section: Introductionmentioning

confidence: 99%

Setting Inventory Levels in a Bike Sharing Network

Datner

Raviv

Tzur

et al. 2019

Transportation Science

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Bike Sharing Systems (BSSs) allow customers to rent bicycles at automatic rental stations distributed throughout a city, use them for a short period of time, and return them to any station. One of the major issues that BSS operators must address is non-homogeneous asymmetric demand processes. These demand processes create an inherent imbalance, thus leading to shortages either of bicycles when users are attempting to rent them and of vacant lockers when users are attempting to return them. The predominant approach taken by operators to cope with this difficulty is to reposition bicycles to rebalance the inventory levels at the different stations. Most repositioning studies assume that a target inventory level or range of inventory levels is known for each station. In this paper, we focus on determining the correct target level for repositioning according to a well-defined objective. This is a challenging task because of the nature of the user behavior that creates the interactions among the inventory levels at different stations. For example, if bicycles are not available at the user's origin, the user may either abandon the system, use other means of transportation, or look for available bicycles at a neighboring station. If in another case, a locker is not available at a user's destination, then that user is obliged to find a station with available space to return the bicycle to the system. Thus, an empty/full station can create a spill-over of demand to nearby stations. In addition, stations are related by origin-destination pairing. In this paper, we take this effect into consideration for the first time when setting target inventory levels and develop a robust guided local search algorithm for that purpose.We show that neglecting the interactions among stations leads to inferior decision-making.

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Section: Introductionmentioning

confidence: 99%

Setting Inventory Levels in a Bike Sharing Network

Datner

Raviv

Tzur

et al. 2019

Transportation Science

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“…An adapted version of the hybrid metaheuristic, which obtains good-quality solutions for the SND-RS in Vogel et al (2014), can be adapted to the SND-ST. In addition, limited number of ST should be considered since the real-world BSS have a determined number of vehicles.…”

Section: Discussionmentioning

confidence: 99%

“…Although the SND-RS formulation is computationally tractable for medium instances in terms of finding suitable solutions in a prudent time (Vogel et al, 2014), it neglects both the sequence of the RS into routes and the empty trips, which a vehicle should make to visit a station with need of a RS. For this reason, the SND-RS leads to a weak anticipation of operational decisions since a vehicle fleet can not be managed efficiently when information about the empty trips is not provided.…”

Section: Service Network Design Of Bike Sharing Systemsmentioning

confidence: 99%

Anticipatory Service Network Design of Bike Sharing Systems

Neumann-Saavedra

Vogel

Mattfeld

2015

Transportation Research Procedia

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Today's conurbations suffer from inefficiency in transportation systems. Bike sharing systems (BSS) combine the advantages of public and private transportation to better exploit the given transportation infrastructure. They provide bikes for short-term trips at automated rental stations. However, spatio-temporal variation of bike rentals leads to imbalances in the distribution of bikes, causing full or empty stations in the course of the day. Ensuring the reliable provision of bike and free bike racks is crucial for the viability of these systems. On the tactical planning level, target fill levels of bikes at stations are determined to provide reliability in service. On the operational planning level, the BSS operator relocates bikes in vehicles among stations based on target fill levels. A recent approach in tactical service network design (SND) anticipates relocation operations of BSS by means of a dynamic transportation model yielding the required demand of relocation services (RS). A RS is described by pickup and return station, time period, and the number of relocated bikes. RS represent the design decision for implementing a service between two stations in each period for each day of the system operation. The output of the SND model are the time-dependent target fill levels at stations and the set of cost-efficient RS to facilitate these target fill levels. However, the existing approach neglects the sequence of RS into tours, thus leading to a weak anticipation of operational decisions. We extend an existing SND approach by including the concept of service tours (ST). RS are sequenced in ST which start and end at the depot. Experiments shows that the ST obtained by the extended SND yield a stronger anticipation of operational decisions.

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“…Vogel et al [49] propose a MIP model for the resource allocation problem arising in PBSs. They aim at minimizing the traveling costs as well as the handling costs for the relocated bikes.…”

Section: (Meta-)heuristics and Hybrid Approachesmentioning

confidence: 99%

Full‐load route planning for balancing bike sharing systems by logic‐based benders decomposition

Kloimüllner

Raidl

2017

Networks

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Public bike sharing systems require some kind of rebalancing to avoid too many rental stations of running empty or entirely full, which would make the system ineffective and annoy customers. Most frequently, a fleet of vehicles with trailers is used for this purpose, moving bikes among the stations. Previous works considered different objectives and modeled the underlying routing problem in different ways, but they all allow an arbitrary number of bikes to be picked up at some stations and delivered to other stations, just limited by the vehicles' capacities. Observations in practice, however, indicate that in larger well-working bike sharing systems drivers almost never pickup or deliver only few bikes, but essentially always approximately full vehicle loads. Many stations even require several visits with full loads. Due to budgetary reasons, typically only just enough drivers and vehicles are employed to achieve a reasonable balance most of the time, but basically never an ideal one where single bikes play a substantial role. Consequently, we investigate here a simplified problem model, in which only full vehicle loads are considered for movement among the rental stations. This restriction appears to have only a minor impact on the achieved quality of the rebalancing in practice but eases the modeling substantially. More specifically, we formulate the rebalancing problem as a selective unit-capacity pickup and delivery problem with time budgets on a bipartite graph and present a compact mixed integer linear programming model, a logic-based Benders decomposition and a variant thereof, namely branch-and-check for it. For the general case, instances with up to 70 stations, and for the single-vehicle case instances with up to 120 stations are solved to proven optimality. A comparison to leading metaheuristic approaches considering flexible vehicle loads indicates that indeed the restriction to full loads has only a very small impact on the finally achieved balance in typical scenarios of Citybike Wien.

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A Hybrid Metaheuristic to Solve the Resource Allocation Problem in Bike Sharing Systems

Cited by 30 publications

References 23 publications

Setting Inventory Levels in a Bike Sharing Network

Setting Inventory Levels in a Bike Sharing Network

Anticipatory Service Network Design of Bike Sharing Systems

Full‐load route planning for balancing bike sharing systems by logic‐based benders decomposition

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