research-article

Multi-Itinerary Optimization as Cloud Service (Industrial Paper)

Authors:

Alexandru Cristian,

Flavius Stoichescu,

Ishai MenacheAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 279 - 288

https://doi.org/10.1145/3347146.3359375

Published: 05 November 2019 Publication History

Abstract

In this paper, we describe Multi-Itinerary Optimization (MIO) - a novel Bing maps service that automates the process of building itineraries for multiple agents while optimizing their routes to save travel time or distance. MIO can be used by organizations with a fleet of vehicles and drivers, mobile salesforce, or a team of personnel in the field in order to maximize workforce efficiency. MIO accounts for service time windows, duration, and priority, as well as traffic conditions between locations, resulting in challenging algorithmic problems at multiple levels (e.g., calculating travel-time distance matrices at scale, scheduling services for multiple agents).

To support an end-to-end cloud service with turnaround times of a few seconds, our algorithm design targets a sweet spot between accuracy and performance. Towards that end, we build a scalable solution based on the ALNS meta-heuristic. Our experiments show that accounting for traffic significantly improves solution quality: MIO not only avoids violating time-window constraints, but also completes up to 17% more services compared to traffic-agnostic mechanisms. Further, our solution generates itineraries with better accuracy than both a cutting-edge heuristic (LKH3) and an Integer-Programming based algorithm, with twice and orders-of-magnitude faster running times, respectively.

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https://doi.org/10.1109/TITS.2022.3232149
Mariescu-Istodor RFränti P(2021)Fast travel-distance estimation using overhead graphJournal of Location Based Services10.1080/17489725.2021.1889058(1-19)Online publication date: 4-Mar-2021
https://doi.org/10.1080/17489725.2021.1889058
Mellou KMarshall LChintalapudi KJaillet PMenache I(2020)Optimizing Onsite Food Services at ScaleProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422266(618-629)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422266
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Index Terms

Multi-Itinerary Optimization as Cloud Service (Industrial Paper)
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
  2. World Wide Web
    1. Web services
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Randomized local search
      2. Mixed discrete-continuous optimization
        Integer programming

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 05 November 2019

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SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Monteiro CDavis C(2023)Polynomial-Time Carsharing Optimization: Linear Formulation and Large-Scale SimulationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.323214924:4(4428-4437)Online publication date: Apr-2023
https://doi.org/10.1109/TITS.2022.3232149
Mariescu-Istodor RFränti P(2021)Fast travel-distance estimation using overhead graphJournal of Location Based Services10.1080/17489725.2021.1889058(1-19)Online publication date: 4-Mar-2021
https://doi.org/10.1080/17489725.2021.1889058
Mellou KMarshall LChintalapudi KJaillet PMenache I(2020)Optimizing Onsite Food Services at ScaleProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422266(618-629)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422266
Abbar SStanojevic RMokbel M(2020)STAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation2020 21st IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM48529.2020.00029(79-88)Online publication date: Jun-2020
https://doi.org/10.1109/MDM48529.2020.00029

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