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Mathematics
Special Issues
Computational Optimization and Scheduling Problems
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Computational Optimization and Scheduling Problems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 910

Special Issue Editors

Dr. Rosita Guido

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, University of Calabria, Ponte Bucci, 87036 Rende, Italy
Interests: combinatorial optimization; metaheuristic algorithms; healthcare; supply chain; scheduling; machine learning
Dr. Sara Ceschia

E-Mail Website
Guest Editor
Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100 Udine, Italy
Interests: combinatorial optimization; metaheuristic algorithms; logistic; healthcare; timetabling; scheduling

Special Issue Information

Dear Colleagues,

We are pleased to announce the launch of a new Special Issue on “Computational Optimization and Scheduling Problems”.

Papers should aim to report the main improvements in computational optimization, with a particular focus on new and innovative approaches for solving scheduling problems exactly or approximately, and their impact on different application contexts.

Topics include new developments in, but not limited to, the following:

  • Combinatorial optimization;
  • Large-scale optimization;
  • Stochastic optimization;
  • Multi-objective optimization;
  • Approximation algorithms;
  • Heuristic and metaheuristic search;
  • Complex real-world scheduling problems;
  • Scheduling problems in logistics, transport, sports, healthcare, manufacturing, engineering, etc.;
  • Timetabling;
  • Scheduling under resource constraints.

We look forward to receiving your submissions.

Dr. Rosita Guido
Dr. Sara Ceschia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • combinatorial optimization
  • heuristic and metaheuristic search
  • timetabling
  • scheduling
  • algorithms

Published Papers (1 paper)

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Research

16 pages, 1235 KiB  
Article
Safe Optimal Control of Dynamic Systems: Learning from Experts and Safely Exploring New Policies
by Antonio Candelieri, Andrea Ponti, Elisabetta Fersini, Enza Messina and Francesco Archetti
Mathematics 2023, 11(20), 4347; https://doi.org/10.3390/math11204347 - 19 Oct 2023
Viewed by 656
Abstract
Many real-life systems are usually controlled through policies replicating experts’ knowledge, typically favouring “safety” at the expense of optimality. Indeed, these control policies are usually aimed at avoiding a system’s disruptions or deviations from a target behaviour, leading to suboptimal performances. This paper [...] Read more.
Many real-life systems are usually controlled through policies replicating experts’ knowledge, typically favouring “safety” at the expense of optimality. Indeed, these control policies are usually aimed at avoiding a system’s disruptions or deviations from a target behaviour, leading to suboptimal performances. This paper proposes a statistical learning approach to exploit the historical safe experience—collected through the application of a safe control policy based on experts’ knowledge— to “safely explore” new and more efficient policies. The basic idea is that performances can be improved by facing a reasonable and quantifiable risk in terms of safety. The proposed approach relies on Gaussian Process regression to obtain a probabilistic model of both a system’s dynamics and performances, depending on the historical safe experience. The new policy consists of solving a constrained optimization problem, with two Gaussian Processes modelling, respectively, the safety constraints and the performance metric (i.e., objective function). As a probabilistic model, Gaussian Process regression provides an estimate of the target variable and the associated uncertainty; this property is crucial for dealing with uncertainty while new policies are safely explored. Another important benefit is that the proposed approach does not require any implementation of an expensive digital twin of the original system. Results on two real-life systems are presented, empirically proving the ability of the approach to improve performances with respect to the initial safe policy without significantly affecting safety. Full article
(This article belongs to the Special Issue Computational Optimization and Scheduling Problems)
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