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A chance-constraint approach for optimizing social engagement-based services

Bierlaire Michel, Fadda Edoardo, Fotio Tiotsop Lohic, Manerba Daniele

Annals of Computer Science and Information Systems

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2022

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Vol. 30

301--304

EN

Social engagement is a novel business model whose goal is transforming final users of a service from passive components into active ones. In this framework, people are contacted by the decision-maker (generally a company) and they are asked to perform tasks in exchange for a reward. This paves the way to the interesting optimization problem of allocating the different types of workforce so as to minimize costs. Despite this problem has been investigated within the operations research community, there is no model that allows to solve it by explicitly and appropriately modeling the behavior of contacted candidates through consolidated concepts from utility theory. This work aims at filling this gap. We propose a stochastic optimization model including a chance constraint that puts in relation, under probabilistic terms, the candidate willingness to accept a task and the reward actually offered by the decision-maker. The proposed model aims at optimally deciding which user to contact, the amount of the reward proposed, and how many employees to use in order to minimize the total expected costs of the operations. A solution approach is proposed to address the formulated stochastic optimization problem and its computational efficiency and effectiveness are investigated through an extensive set of computational experiments.

2

Solving Assignment Problems via Quantum Computing: a Case-Study in Train Seating Arrangement

Gioda Ilaria, Caputo Davide, Fadda Edoardo, Manerba Daniele, Silva Fernández Blanca, Tadei Roberto

Annals of Computer Science and Information Systems

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2021

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Vol. 25

217--220

EN

In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should resolve complex problems currently considered to be intractable. This new research area is called Quantum Computing. What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems. This work focuses on solving assignment-like combinatorial optimization problems by exploiting this novel computational approach. A case-study, denoted as the Seating Arrangement Optimization problem, is considered. It is modeled through the Quadratic Unconstrained Binary Optimization paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv, and a quantum-classical hybrid system. The obtained experimental results are compared in terms of solution quality and computational efficiency

3

Reinforcement Learning Algorithms for Online Single-Machine Scheduling

Li Yuanyuan, Fadda Edoardo, Manerba Daniele, Tadei Roberto, Terzo Olivier

Annals of Computer Science and Information Systems

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2020

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Vol. 21

277--283

EN

Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues effectively. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins's Q(λ), and Sarsa(λ), to the online single-machine scheduling problem. Our main goal is to provide insights on how such techniques perform. The numerical results show that Watkins's Q(λ) performs best in minimizing the total tardiness of the scheduling process.

4

KPIs for optimal location of charging stations for electric vehicles: the Biella case-study

Fadda Edoardo, Manerba Daniele, Cabodi Gianpiero, Camurati Paolo, Tadei Roberto

Annals of Computer Science and Information Systems

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2019

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Vol. 18

123--126

EN

Electric vehicles are accelerating the world's transition to sustainable energy. Nevertheless, the lack of a proper charging station infrastructure in many real implementations still represents an obstacle for the spread of such a technology. In this paper, we present a real case application of optimization techniques in order to solve the location problem of electric charging stations in the district of Biella, Italy. The plan is composed by several progressive installations and decision makers pursue several objectives that might be in contrast. For this reason, we present an innovative framework based on the comparison of several ad-hoc Key Performance Indicators for evaluating many different aspects of a location solution.