Large neighborhood search for the bike request scheduling problem

Abstract

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.

Risk‐averse two‐stage stochastic programming for the inventory rebalancing of bike‐sharing systems

“…Bulhões et al (2018) presented an integer program and branch-and-cut algorithm to study the static bike relocation problem with multiple vehicles and bike station visits. Vergeylen et al (2020) proposed an algorithm to solve the bike request scheduling problem to reposition bikes in a bike-sharing system such that vehicle routes are determined based on requests to add or remove bikes from stations. Moreover, the following studies consider static rebalancing based on stochastic optimization models.…”

“…Vergeylen et al. (2020) proposed an algorithm to solve the bike request scheduling problem to reposition bikes in a bike‐sharing system such that vehicle routes are determined based on requests to add or remove bikes from stations. Moreover, the following studies consider static rebalancing based on stochastic optimization models.…”

“…It has over 700 stations, bikes and the penalty cost caused by unmet demand based on real-world practice. Compared to the existing studies that mainly adopted a risk-neutral approach for their proposed stochastic optimization models (Dell'Amico et al, 2016;Bulhões et al, 2018;Vergeylen et al, 2020), this study adopts the conditional value at risk (CVaR) to implement a risk-averse approach to successfully address the risk associated with unmet demand caused by low inventory levels (both bikes and docks) in response to uncertain and fluctuated demand. As discussed in Filippi et al (2020), the CVaR measure has been used to solve stochastic optimization problems of different application areas and most notably in inventory management problems (Huang et al, 2020;Yuan et al, 2020); therefore, it is reasonably expected that the CVaR can be appropriately applied for inventory rebalancing for bike-sharing systems.…”

“…The main contribution of this study is to propose a risk‐averse two‐stage stochastic programming to minimize both the operational cost to relocate the bikes and the penalty cost caused by unmet demand based on real‐world practice. Compared to the existing studies that mainly adopted a risk‐neutral approach for their proposed stochastic optimization models (Dell'Amico et al., 2016; Bulhões et al., 2018; Vergeylen et al., 2020), this study adopts the conditional value at risk (CVaR) to implement a risk‐averse approach to successfully address the risk associated with unmet demand caused by low inventory levels (both bikes and docks) in response to uncertain and fluctuated demand. As discussed in Filippi et al.…”

The static on‐demand bus routing problem: large neighborhood search for a dial‐a‐ride problem with bus station assignment

“…Over the last few years several alternatives for collective public transport came into existence, mostly initiated by the private sector. Examples are car sharing (Katzev, 2003), bike sharing (Vergeylen et al., 2020), employee commuter programs, taxi and ride sharing (Agatz et al., 2012; Jung et al., 2016; Santos and Xavier, 2015), and on‐demand carpooling. These alternatives, together with DRT systems, fit under the umbrella term flexible urban transport (FUT) (Finn, 2012).…”