Andrea Lodi

M. Pawan Kumar

2022-10-10

TMLR (accepted)

openreview.net

OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations

Leandro R. Costa

Daniel Aloise

Luca G. Gianoli

A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). Whil… (see more)e the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.

2022-06-01

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (published)

Capacity Variation in the Many-to-one Stable Matching

Federico Bobbio

Margarida Carvalho

Alfredo Torrico

2022-05-03

ArXiv (preprint)

OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations

Leandro Rincon Costa

Daniel Aloise

Luca Giovanni Gianoli

A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). Whil… (see more)e the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.

2022-05-01

International Conference on Distributed Computing in Sensor Systems (published)

Predicting the probability distribution of bus travel time to measure the reliability of public transport services

L'ea Ricard

Guy Desaulniers

Louis-Martin Rousseau

2022-05-01

Transportation Research Part C: Emerging Technologies (published)

Predicting the probability distribution of bus travel time to measure the reliability of public transport services

L. Ricard

Guy Desaulniers

Louis-Martin Rousseau

2022-05-01

Transportation Research Part C: Emerging Technologies (published)

On the estimation of discrete choice models to capture irrational customer behaviors

Sanjay Dominik Jena

Claudio Sole

The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economi… (see more)cs has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.

2022-05-01

INFORMS Journal on Computing (published)

Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems

Luca Accorsi

Daniele Vigo

2022-03-01

Operations Research Letters (published)

JANOS: An Integrated Predictive and Prescriptive Modeling Framework

David Bergman

Teng Huang

Philip Brooks

Arvind U. Raghunathan

Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling… (see more) framework JANOS that seamlessly integrates the two streams of analytics, allowing researchers and practitioners to embed machine learning models in an end-to-end optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables: regular and predicted. The relationship between the regular and the predicted variables is specified by the user as pretrained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation. Summary of Contribution. This paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.

2022-03-01

INFORMS Journal on Computing (published)

Single Allocation Hub Location with Heterogeneous Economies of Scale

Borzou Rostami

Masoud Chitsaz

Okan Arslan

Gilbert Laporte