Interactive Assistive Framework for Maximum Profit Routing in Public Transportation in Smart Cities

Armaselu, Bogdan; Daescu, Ovidiu

doi:10.1145/3056540.3064962

Cited by 3 publications

(23 citation statements)

References 6 publications

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“…That is, I has the same nodes and distances as I. We solve I using the algorithm in [3] and denote by Sol the solution.…”

Section: Mprp-mmentioning

confidence: 99%

“…By using the 11 log T -approximation for MPRP in [3] for O(n 11 ) time, we thus solve MPRP-M in O(n 11 ) time.…”

Section: Mprp-mmentioning

confidence: 99%

“…MPRP was introduced in [1,3], which focuses on the case with fixed quantities and only one vehicle per site. In this paper, we consider the case where multiple vehicles may visit one site.…”

Section: Introductionmentioning

confidence: 99%

“…Armaselu and Daescu solved the fixed-supply, single vehicle version of MPRP [1,3]. They provide two APXs for MPRP, both running in O(n 11 ) time, assuming q i (l i ) ≤ αq j (l j ), ∀i, j = 1, .…”

Section: Introductionmentioning

confidence: 99%

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mentioning

confidence: 99%

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Maximum-Profit Routing Problem with Multiple Vehicles per Site

Armaselu

2021

Preprint

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We consider the Maximum-Profit Routing Problem (MPRP), a variant of pick-up routing problem introduced in [1,3], in which the goal is to maximize total profit. The original MPRP restricts vehicles to visit a site at most once. In this paper, we consider extensions of MPRP, in which a site may be visited by a vehicle multiple times. Specifically, we consider two versions: one in which the quantity to be picked up at each site is constant in time (MPRP-M), and one with time-varying supplied quantities, which increase linearly in time (MPRP-VS). For each of these versions, we come up with a constant-factor polynomial-time approximaltion scheme.

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“…That is, I has the same nodes and distances as I. We solve I using the algorithm in [3] and denote by Sol the solution.…”

Section: Mprp-mmentioning

confidence: 99%

“…By using the 11 log T -approximation for MPRP in [3] for O(n 11 ) time, we thus solve MPRP-M in O(n 11 ) time.…”

Section: Mprp-mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…

…”

mentioning

confidence: 99%

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Maximum-Profit Routing Problem with Multiple Vehicles per Site

Armaselu

2021

Preprint

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Approximation algorithms for some extensions of the maximum profit routing problem

Armaselu

2022

J Comb Optim

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In this paper, we consider extensions of the Maximum-Profit Public Transportation Route Planning Problem, or simply Maximum-Profit Routing Problem (MPRP), introduced in Armaselu and Daescu (Approximation algorithms for the maximum profit pick-up problem with time windows and capacity constraint, 2016. arXiv:1612.01038, Interactive assisting framework for maximum profit routing in public transportation in smart cities, PETRA, 13–16, 2017). Specifically, we consider MPRP with Time-Variable Supply (MPRP-VS), in which the quantity $$q_i(t)$$ q i ( t ) supplied at site i is linearly increasing in time t, as opposed to the original MPRP problem, where the quantity is constant in time. For MPRP-VS, our main result is a $$5.5 \log {T} (1 + \epsilon ) \left( 1 + \frac{1}{1 + \sqrt{m}}\right) ^2$$ 5.5 log T ( 1 + ϵ ) 1 + 1 1 + m 2 approximation algorithm, where T is the latest time window and m is the number of vehicles used. We also study the MPRP with Multiple Vehicles per Site, in which a site may be visited by a vehicle multiple times, which can have 2 flavors: with quantities fixed in time (MPRP-M), and with time-variable quantities (MPRP-MVS). Our algorithmic solution to MPRP-VS can also improve upon the MPRP algorithm in Armaselu and Daescu (2017) under certain conditions. In addition, we simulate the MPRP-VS algorithm on a few benchmark, real-world, and synthetic instances.

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An APX for the Maximum-Profit Routing Problem with Variable Supply

Armaselu

2020

Preprint

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In this paper, we study the Maximum-Profit Routing Problem with Variable Supply (MPRP-VS). This is a more general version of the Maximum-Profit Public Transportation Route Planning Problem, or simply Maximum-Profit Routing Problem (MPRP), introduced in [2]. In this new version, the quantity q i (t) supplied at site i is linearly increasing in time t, as opposed to [2], where the quantity is constant in time. Our main result is a 5.5 log T (1+ )(1+ 1 1+ √ m ) 2 approximation algorithm, where T is the latest time window and m is the number of vehicles used. In addition, we improve upon the MPRP algorithm in [2] under certain conditions.

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Interactive Assistive Framework for Maximum Profit Routing in Public Transportation in Smart Cities

Cited by 3 publications

References 6 publications

Maximum-Profit Routing Problem with Multiple Vehicles per Site

Maximum-Profit Routing Problem with Multiple Vehicles per Site

Approximation algorithms for some extensions of the maximum profit routing problem

An APX for the Maximum-Profit Routing Problem with Variable Supply

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