The pickup and delivery problem with time windows and occasional drivers

Dahle, Lars; Andersson, Henrik; Christiansen, Marielle; Speranza, Maria

doi:10.1016/j.cor.2019.04.023

Cited by 74 publications

(34 citation statements)

References 13 publications

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“…Some literatures on PTWS were addressed from the viewpoint of the operation mechanism. Dahle et al [1] considered the compensation mechanism in pickup and delivery operation with time window. Urrutia and De Werra [2] considered both limited and unlimited capacity stacks and allowed some transformations on the delivery route.…”

Section: Overview Of Previousmentioning

confidence: 99%

“…is the travel time from railway station o to the rst freight operation site p (1). And then, the start operation time point of the next freight operation site p(2) can be written as…”

Section: Selecting Operation Sites Sequence Based On Minimizing Absolmentioning

confidence: 99%

“…Instead of this regulation, we will provide another four di erent shunting strategies which generate taking-out freight operation site sequence with nearest location and completion time. us, we will obtain four new shunting strategies, i.e., placing-in and taking-out site sequence based on minimizing absolute travel time and nearest location and completion time (ATT-NL&CT), placing-in and taking-out site sequence based on maximizing absolute detention time and nearest location (1) t op (2) t op (A′) ... and completion time (ADT-NL&CT), placing-in and takingout site sequence based on minimizing relative travel time and nearest location and completion time (RTT-NL&CT), and placing-in and taking-out site sequence based on maximizing relative detention time and nearest location and completion time (RDT-NL&CT). To avoid repeated expression, we provide only the procedure of generating taking-out freight operation site selection sequence.…”

Section: Selecting Operation Sites Sequence Based On Nearest Locationmentioning

confidence: 99%

“…And then, the double operation wagons are moved to the shift-into freight operation site z(1) from z (1). From the shift-into freight operation site z(1), we begin to nd the second shift-out site z (2).…”

Section: Span-shift-moving Shunting Based On Nearest Locationmentioning

confidence: 99%

“…t p(1)p(2) t p(A′−1)p(A′) t op(1) Absolute detention timeRelative detention time S = {1, ..., A′} {t oj , j ∈ S} = t oj + ξω j t oj = t oj + u( j) {t αβ , α, β ∈ S} = t αβ + ξω β t αβ = t αβ + u(β) Procedure of placing-in freight operation site selection sequence for the RTT-ECT shunting strategy. Journal of Advanced Transportation 4.2.…”

mentioning

confidence: 99%

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Shunting Strategy for Placing-In and Taking-Out Wagons on Branch-Shaped Freight Operation Network with Simulation Method

Xuan

et al. 2020

Journal of Advanced Transportation

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Because of China’s vast territory, large population, and huge demand for bulk materials, the railway transportation mode has always received considerable attention. For long-haul transportation, railway transportation can provide a scheduled-based transport plan, all-weather transport service, and cheaper carry instead of other transport modes. The study described in this paper attempts to develop a simulation platform for optimizing the placing-in and taking-out wagons system on the branch-shaped freight operation network (PTWS-BSFON). The operation process of PTWS is thoroughly analyzed from three aspects of decoupling-coupling wagons subsystem, placing-in local wagons subsystem, and taking-out local wagons subsystem. And then the simulation platform encompassing two typically shunting modes of PTWS are developed by Arena software. Under scenarios of PTWS-SO and PTWS-SSMS, the hierarchical structure of the shunting strategies is, respectively, outlined in the simulation platform. Finally, the shunting strategies based on the simulation platform are carried out by concrete examples, which prove the rationality of the methodology in applying different strategies and enhancing the performances of the PTWS-BSFON.

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Section: Overview Of Previousmentioning

confidence: 99%

“…is the travel time from railway station o to the rst freight operation site p (1). And then, the start operation time point of the next freight operation site p(2) can be written as…”

Section: Selecting Operation Sites Sequence Based On Minimizing Absolmentioning

confidence: 99%

Section: Selecting Operation Sites Sequence Based On Nearest Locationmentioning

confidence: 99%

Section: Span-shift-moving Shunting Based On Nearest Locationmentioning

confidence: 99%

mentioning

confidence: 99%

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Shunting Strategy for Placing-In and Taking-Out Wagons on Branch-Shaped Freight Operation Network with Simulation Method

Xuan

et al. 2020

Journal of Advanced Transportation

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Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platforms

Horner,

Pazour,

Mitchell

2024

Naval Research Logistics

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This article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.

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Delivery systems with crowd‐sourced drivers: A pickup and delivery problem with transfers

2020

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Rapid urban growth, the increasing importance of e‐commerce and high consumer service expectations have given rise to new and innovative models for freight delivery within urban environments. Crowdsourced solutions—where drivers are not employed by a carrier but occasionally offer their services through on‐line platforms and are contracted as required by carriers—are receiving growing attention from industry. We consider a crowdsourced system where drivers express their availability to perform delivery tasks for a given period of time and the platform communicates a schedule with requests to serve. We investigate the potential benefits of introducing transfers to support driver activities. At transfer locations, drivers can drop off packages for pick up by other drivers at a later time. We frame the problem as a multidepot pickup and delivery problem with time windows and transfers, and propose an adaptive large neighborhood search algorithm that effectively identifies beneficial transfer opportunities and synchronizes driver operations. Computational experiments indicate that introducing transfer options can significantly reduce system‐wide travel distance as well as the number of drivers required to serve a given set of requests, especially when drivers have short availability and requests have high service requirements.

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The pickup and delivery problem with time windows and occasional drivers

Cited by 74 publications

References 13 publications

Shunting Strategy for Placing-In and Taking-Out Wagons on Branch-Shaped Freight Operation Network with Simulation Method

Shunting Strategy for Placing-In and Taking-Out Wagons on Branch-Shaped Freight Operation Network with Simulation Method

Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platforms

Delivery systems with crowd‐sourced drivers: A pickup and delivery problem with transfers

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