The Share-a-Ride problem with stochastic travel times and stochastic delivery locations

Li, B Baoxiang; Krushinsky, Dmitry; Woensel, Tom Van; Reijers, HA Hajo

doi:10.1016/j.trc.2016.01.014

Cited by 78 publications

(33 citation statements)

References 23 publications

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“…They present an exact MILP formulation to solve this problem in tiny instances to maximize taxi provider's profit in a deterministic configuration. They proposed a solution based on large neighborhood search heuristic for a case in which travel times and delivery locations are stochastic [Li et al, 2016]. Ma et al [Ma et al, 2013] defined an algorithm to dispatch the best vehicle in a taxi sharing scheme when passengers send real-time queries.…”

Section: Resultsmentioning

confidence: 99%

The merits of sharing a ride

Ehsani

2017

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi reallife data set. Results represent a substantial reduction in overall overheads.

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Section: Resultsmentioning

confidence: 99%

The merits of sharing a ride

Ehsani

2017

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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“…With Proposition IV.1(e) we can eliminate every route with a cycle in the first leg. Thus we construct the reduced route set for (16) (17), we can solve (16) with strict equality in the constraints of (6a) and (6b). Thus, we can reduce (16) to an optimization problem over decision variables f i r (l), the allocations, and S l , the supply at a node.…”

Section: ) General Properties For Feed-in Supply Optimizationmentioning

confidence: 99%

“…Thus we construct the reduced route set for (16) (17), we can solve (16) with strict equality in the constraints of (6a) and (6b). Thus, we can reduce (16) to an optimization problem over decision variables f i r (l), the allocations, and S l , the supply at a node. This elimination of the variables f r leads to a significant reduction in the number of optimization variables, specifically equal to the number of routes in R − .…”

Section: ) General Properties For Feed-in Supply Optimizationmentioning

confidence: 99%

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One-Shot Coordination of Feeder Vehicles for Multi-Modal Transportation

Goswami

Tallapragada

2019

2019 18th European Control Conference (ECC)

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In this paper, we consider coordinated control of feeder vehicles for the first and last mode of a multi-modal transportation system. We adopt a macroscopic approach and model a geographical region as a graph with one of the nodes being an interchange between different modes of transportation. We model customer demands and supplies of vehicles as volumes and consider flows of vehicles. We propose one-shot problems for passenger transportation to or from the interchange within a fixed time window, under the knowledge of the demand distribution. In particular, we pose the problem of operator profit maximization through routing and allocations of the vehicles as well as pricing. With K.K.T. analysis we propose an offline method for reducing the problem size. Further, we also analyse the problem of maximizing profits by optimally locating the supply for a given total supply and present a closed form expression of the maximum profits that can be earned over all supply distributions for a given demand distribution. We also show an equivalence between optimal supply location problem and the last mode problem. Finally we present a model for determining the comparative cost of the best alternate transportation for the feeder service to be viable. We illustrate the results through simulations and also compare the proposed model with a traditional vehicle routing problem.

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“…At this level, three main decisions are taken: Scheduling Model, Route Model, and Order Processing Model. However, research in operational planning has focused mainly on solving three problems: (a) collaborative vehicle routing [10,[45][46][47]; (b) crowd-sourced delivery routing [48][49][50][51]; and (c) ride sharing [52][53][54][55] Once companies establish trust, the decisional collaboration stage begins. The decisions taken at a strategic level need to follow a decentralised coordination among partners guaranteeing that every partner has an input and are considered equally.…”

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confidence: 99%

Towards a Business Model Framework to Increase Collaboration in the Freight Industry

Vargas¹,

Patel

2018

Logistics

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Collaboration in the freight industry has not been widely adopted mainly due to the perceived barriers in competition resulting in a lack of trust among fleet operators. Collaboration in this sector has significant benefits, including the reduction of empty running, operating costs (OPEX) and greenhouse gas emissions (GHG) resulting in greater utilisation of existing logistics assets. A review of the literature to establish the critical aspects of freight collaboration was undertaken, as well as analyses of published case studies and European Union (EU)-funded projects. The critical aspects and barriers identified include: revenue sharing; compliance with competition law; process synchronization; organisational and systems interoperability; different forms of collaboration from a physical and coordination structure perspective; and strategies for collaboration. To facilitate collaboration a freight collaborative business model (FCBM) framework that highlights problematic areas in freight collaboration is proposed to support standardizing collaborative practices in the freight industry. Three published freight industry collaboration business cases were evaluated against the model. The business model framework is intended as a tool to be used to compare different business models and identify the best innovations to help facilitate collaborative practices. The freight collaboration business model was applied to the Freight Share Lab research project in order to demonstrate the concept and investigate whether efficiency can be unlocked through deployment of a dynamic data and asset sharing platform to enable route and load optimization across multiple fleets of freight vehicles, rail freight wagons and containers.

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The Share-a-Ride problem with stochastic travel times and stochastic delivery locations

Cited by 78 publications

References 23 publications

The merits of sharing a ride

The merits of sharing a ride

One-Shot Coordination of Feeder Vehicles for Multi-Modal Transportation

Towards a Business Model Framework to Increase Collaboration in the Freight Industry

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