An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

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General data protection regulation

This paper presents a survey of vehicle routing problems with multiple synchronization constraints. These problems exhibit, in addition to the usual task covering constraints, further synchronization requirements between the vehicles, concerning spatial, temporal, and load aspects. They constitute an emerging field in vehicle routing research and are becoming a “hot” topic. The contribution of the paper is threefold: (i) It presents a classification of different types of synchronization. (ii) It discusses the central issues related to the exact and heuristic solution of such problems. (iii) It comprehensively reviews pertinent literature with respect to applications as well as successful solution approaches, and it identifies promising algorithmic avenues.

Synchronization in Vehicle Routing---A Survey of VRPs with Multiple Synchronization Constraints

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

A survey of variants and extensions of the location-routing problem

Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

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Machine learning and optimization for production rescheduling in Industry 4.0

The Optimization for Networked Data in Environmental Urban Waste Collection (ONDE-UWC) project is, to the best of our knowledge, the first attempt to apply the Internet of Things (IoT) paradigm to ...

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Waste Collection in Urban Areas: A Case Study

https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2009-39.pdf

New Valid Inequalities for the Two-Echelon Capacitated Vehicle Routing Problem

Customized multi-period stochastic assignment problem for social engagement and opportunistic IoT

One of the main endeavors of smart cities is the organization and subsidization of public transportation. To achieve this, it is important to obtain information about the way in which people move. This once-difficult problem can now be addressed by using smartphones. This paper introduces a machine learning-based framework that is able to ascertain the usage of a public or a private transportation mode by analyzing a little amount of data sampled by a user's smartphone. The presented method exhibits a good accuracy and a limited battery consumption. A public anonymized dataset based on real measurements is also provided along with this study. To the best of our knowledge, this is the first dataset of this kind that is offered to the public.

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Edoardo Fadda

Papers

Machine learning and optimization for production rescheduling in Industry 4.0

Waste Collection in Urban Areas: A Case Study

New Valid Inequalities for the Two-Echelon Capacitated Vehicle Routing Problem

Customized multi-period stochastic assignment problem for social engagement and opportunistic IoT

Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes