A survey of dial-a-ride problems: Literature review and recent developments

Ho, Sin C.; Szeto, W.Y.; Kuo, Yong-Hong; Leung, Janny; Petering, Matthew E. H.; Tou, Terence W.H.

doi:10.1016/j.trb.2018.02.001

Cited by 332 publications

(150 citation statements)

References 103 publications

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“…Ribeiro et al [40] and Ropke et al [41] modified three large neighborhood removal heuristics and two large neighborhood insertion heuristics from Shaw P. [42], [43] and Potvin et al [44] for OPDP. Additionally, studies of Grimault et al [45] and Ho et al [7] show that solution feasibility of the OPDP is an important issue to the neighborhoods efficiency for the algorithm.…”

Section: Opdp Opdpst and Opdpstrpmentioning

confidence: 99%

“…A Iterated Local Search (ILS) is proposed by Li et al [47]. Ho et al [7] classified the solution methods for the DARP (an important category of the OPDP). Factorovich et al [48] propose a combination of cutting planes to find feasible solutions for the pickup and delivery problem with incompatibility constraints.…”

Section: Neighborhood and Algorithm For Opdpmentioning

confidence: 99%

“…Constraints (2), (3), (4), (5) and (6) determine the order between pd-pairs/vehicle i and j. Constraints (7) ensure that each pd-pair i is transported no more than once. Constraint (8) ensures that each vehicle is not over-loading.…”

Section: Mathematical Modelmentioning

confidence: 99%

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Multi-start heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths

Zhuo

Xiong

et al. 2020

PLoS ONE

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The One-to-one Pickup and Delivery Problem with Shortest-path Transport along Real-life Paths (OPDPSTRP) is presented in this opaper. It is a variation of the One-to-one Pickup and Delivery Problem (OPDP), which is common in daily life, such as the Passenger Train Operation Plans (PTOP) and partial Taxi-sharing Problem. Unlike the classical OPDP, in the OPDPSTRP, (1) each demand must be transported along the shortest path according to passengers/shippers requirements, and (2) each vehicle should travel along a real-life path. First, six route structure rules are proposed for the OPDPSTRP, and a kind of Mixed-Integer Programming (MIP) models is formulated for it. Second, A Variable Neighborhood Descent (VND), a Variable Neighborhood Research (VNS), a Multi-Start VND (MS_VND) and a Multi-Start VNS (MS_VNS) with five neighborhood operators has been developed to solve the problem. Finally, The Gurobi solver, the VND, the VNS, the MS_VND and the MS_VNS have been compared with each other by 84 random instances partitioned in small size connected graphs, medium size connected graphs and large size connected graphs. From the test results we found that solutions generated by these approaches are often comparable with those found by the Gurobi solver, and the solutions found by these approaches are better than the solutions found by the Gurobi solver when solving instances with larger numbers of demands. In almost all instances, the MS_VND significantly outperforms the VND and the VNS in terms of solution quality, and outperforms the MS_VNS both in terms of solution quality and CPU time. In the instances with large numbers of demands, the MS_VND is still able to generate good feasible solutions in a reasonable CPU time, which is of vital practical significance for real-life instances. OPEN ACCESSCitation: Qi X, Fu Z, Xiong J, Zha W (2020) Multistart heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths. PLoS ONE 15(2): e0227702.

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Section: Opdp Opdpst and Opdpstrpmentioning

confidence: 99%

Section: Neighborhood and Algorithm For Opdpmentioning

confidence: 99%

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Multi-start heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths

Zhuo

Xiong

et al. 2020

PLoS ONE

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“…The DRT service was initially provided to extend the coverage of the traditional fixed-route network by providing door-to-door services and enhancing the mobility for certain groups of customers (e.g., the elderly and people with special needs), especially in remote areas with low demand densities. The practice the on-demand transit services emerged in the 1970s, i.e., the Dial-A-Ride service offered in the United States (US) [12]. With the help of interactive and intelligent information platforms (via smartphone applications or websites), various types of similar on-demand transit services gained increasing popularity worldwide, such as customized/subscription buses [4], dial-a-ride [13], personalized rapid transit [14], and shuttle buses [15].…”

Section: Literature Reviewmentioning

confidence: 99%

An Analytical Model for the Many-to-One Demand Responsive Transit Systems

et al. 2019

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The demand-responsive transit (DRT) service is an emerging and flexible transit mode to enhance the mobility of the urban transit system by providing personalized services. Passengers can make advanced appointments through smartphone applications. In this paper, an analytical model is proposed for the many-to-one DRT system. The agency and user costs are approximated by closed-form expressions. The agency cost, which is also the operation cost, is approximated by the continuum approximation technique. A nearest-neighbor routing strategy is applied, whereby the vehicle always collects the nearest passenger waiting in the system. The Vickrey queueing theory is adopted as the basis for approximating each component of the user cost, which is composed of the out-of-vehicle and in-vehicle waiting times and schedule deviations, which also depend on the service quality of the DRT system. The results of the numerical experiment show that (1) the agency and user costs are influenced significantly by the demand density, and (2) the DRT operator cannot further decrease the operating cost by solely deploying larger vehicles.

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“…As a possible attempt to mitigate this, ridesharing, also referred as carpooling or vanpooling, facilitates shared rides between drivers and passengers with similar origin-destination pairings, and allows travelers going from one destination to the other to pick up other passengers on the way (Coulombel et al 2019, Shaheen et al 2015. Scheduling routes that are matching customers' preferences and needs requires to consider a specific class of vehicle routing problems, namely the dial-a-ride problems (Fu 2002b, Ho et al 2018. In dial-a-ride services, the provider aims to minimize operations costs or level of service, while satisfying demand and customers' constraints (Cordeau & Laporte 2003, Parragh et al 2008.…”

Section: Introductionmentioning

confidence: 99%

On the needs for MaaS platforms to handle competition in ridesharing mobility

Pandey

Monteil

Gambella

et al. 2019

Transportation Research Part C: Emerging Technologies

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Ridesharing has been emerging as a new type of mobility. However, the early promises of ridesharing for alleviating congestion in cities may be undermined by a number of challenges, including the growing number of proposed services and the subsequent increasing number of vehicles, as a natural consequence of competition. In this work, we present optimization-based approaches to model cooperation and competition between multiple ridesharing companies, in a real-time on-demand setting. A recent trend relies on solving the integrated combination of Dial-A-Ride Problems (DARP), which compute the cost of assigning incoming requests to vehicle routes, plus Linear Assignment Problems (LAP), which assign vehicles to requests. While the DARPs, are solved at the level of the vehicles of each company, we introduce cooperative and competitive approaches to solve the LAP. The cooperative model, which could make use of Mobility as a Service platforms, is shown to solve the LAP to optimality following closely results from the literature, and limiting the amount of information the companies are required to share. We investigate how a realistic model of competition deviates from this optimality and provide worst case bounds. We evaluate these models with respect to a centralized model on one-week instances of the New York City taxi dataset. Model variants coping with noise in the travel time estimations, bias in the assignment costs, and preferences in the competitive case are also presented and validated. The computational results suggest that cooperation among ridesharing companies can be conducted in such a way to limit the degradation of the level of service with respect to a centralized model. Finally, we argue that the competition can lower the quality of the ridesharing service, especially in the case customer preferences are accommodated.

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A survey of dial-a-ride problems: Literature review and recent developments

Cited by 332 publications

References 103 publications

Multi-start heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths

Multi-start heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths

An Analytical Model for the Many-to-One Demand Responsive Transit Systems

On the needs for MaaS platforms to handle competition in ridesharing mobility

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