Journal updates

Call for papers for the Special Issue of the Optimization and Engineering Journal on Sustainable Development of Energy, Water and Environment Systems – SDEWES, dedicated to the SDEWES 2024 Conferences

The background of this Special Issue (SI) of the Optimization and Engineering (OPTE) journal are the 2024 Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conferences. This broad field was discussed by the participants of three conferences held in 2024 – the 4th Latin American SDEWES Conference (Vina del Mar), the 2nd Asia Pacific SDEWES Conference (Gold Coast) and the 19th SDEWES Conference (Rome).

This SI aims at bringing together articles that discuss recent advances of optimization methods and algorithms that integrate various life supporting systems. Very often, a decision making problem appears in the form of a parameter estimation problem, it can be formulated as an optimization model, and then solved using different optimization algorithms and techniques. All papers included in this SI of the OPTE journal will consider aspects of numerical analysis, mathematical modeling, and computational methods involved in investigating, planning and implementing sustainable development. In this context, the guest editors have confidence that the selected papers and addressed issues will substantially contribute to the increase of the knowledge body published in the OPTE journal, and the SI will be a significant platform for researchers to discuss, share, and disseminate new ideas.

Information on the upcoming SDEWES events and other related activities can be found on the website of the International Centre for Sustainable Development of Energy, Water and Environment Systems (SDEWES Centre) at <www.sdewes.org>.

Proposed date of submission of final paper:

February 28, 2025 – first version of all papers

July 31, 2025 – final version of all papers

Guest editorial team of this special volume:

Dr. Marian Trafczynski, Managing Guest Editor

The Faculty of Civil Engineering, Mechanics and Petrochemistry

Warsaw University of Technology, Plock, Poland

e-mail: Marian.Trafczynski@pw.edu.pl

Prof. Krzysztof Urbaniec, Guest Editor

The Faculty of Civil Engineering, Mechanics and Petrochemistry

Warsaw University of Technology, Plock, Poland

e-mail: krzysztof.urbaniec@yahoo.com

Dr. Slawomir Alabrudzinski, Guest Editor

The Faculty of Civil Engineering, Mechanics and Petrochemistry

Warsaw University of Technology, Plock, Poland

e-mail: Slawomir.Alabrudzinski@pw.edu.pl

Dr. Hrvoje Mikulčić, Guest Editor

University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture,

Zagreb, Croatia

e-mail: Hrvoje.Mikulcic@fsb.hr

Submissions:

The Special Issue will be limited to the conference participants. A number of papers with archival quality, presented at the SDEWES2024 conferences, will be after the conference selected for recommendation for submission in a Special Issue of the Optimization and Engineering journal. The submission procedure requires the submission of a manuscript to the Optimization and Engineering (OPTE) journal at https://www.springer.com/journal/11081 and select the special issue “ Sustainable Development of Energy, Water and Environment Systems – SDEWES 2024”. All submissions must be original and may not be under review by another publication. Interested authors should consult the journal’s “Instructions for Authors”, at https://www.springer.com/journal/11081/submission-guidelines. All submitted papers will be reviewed on a peer-review basis as soon as they are received.

Editors: Andre Milzarek, Zaiwen Wen, Junfeng Yang

This special issue aims to bring together articles discussing recent advances in machine learning and optimization. Machine learning, a branch of artificial intelligence, focuses on developing algorithms that allow computers to learn from data and make decisions. Optimization involves finding the best solution from a set of feasible options and is crucial for enhancing machine learning algorithms.

The integration of these two fields offers significant potential for a wide range of applications. Machine learning techniques can improve optimization processes by predicting optimal solutions and enhancing search efficiency. Conversely, optimization methods can refine machine learning models for better accuracy and generalization, leading to breakthroughs in domains like healthcare, finance, and engineering.

We invite contributions that highlight innovative approaches and significant findings in the intersection of machine learning and optimization. Topics of interest include, but are not limited to:

- Optimization algorithms for machine learning

- Machine learning methods for optimization

- Artificial intelligence and optimization

- Applications in various fields such as healthcare, finance, and engineering

- Data-driven optimization techniques

We look forward to your submissions that push the boundaries of research in machine learning and optimization. This special issue aims to provide a platform for researchers to share their latest findings and explore new directions in this exciting and rapidly evolving field.

Important Dates: Deadline for submissions: January 31, 2025.

Submission Procedure: Please submit to the Optimization and Engineering (OPTE) journal at https://www.springer.com/mathematics/journal/11081 and select special issue “SI: Machine Learning and Optimization”. All submissions must be original and may not be under review by another publication. Interested authors should consult the journal’s “Instructions for Authors”, at https://www.springer.com/mathematics/journal/11081. All submitted papers will be reviewed on a peer review basis as soon as they are received. Accepted papers will become immediately available at Online First until the complete Special Issue appears.

Guest Editors: Dr. Jia-Bao Liu (Anhui Jianzhu University, China), Dr. Muhammad Javaid (University of Management and Technology, Pakistan), Dr. Mohammad Reza Farahani (Iran University of Science and Technology, Iran)

Submission Deadline: February 08, 2024

This special issue aims at bringing together articles that discuss recent advances in Graph Theory-based Approaches for Optimizing Neural Network Architectures. Graph theory has emerged as a powerful tool for optimizing neural network architectures. As the field of artificial intelligence continues to advance, researchers and engineers look for innovative methods to design more efficient and effective neural networks. Exploiting graph theory principles can address challenges related to model complexity, training efficiency and generalization capabilities. In Neural networks, especially deep learning models have demonstrated remarkable success in various tasks such as image recognition, natural language processing and speech synthesis. However, the increased complexity of these models comes with a trade-off. Graph theory provides a framework for modeling neural networks as graphs provided with neurons as nodes and connections as edges. We encourage submissions from researchers in this background to demonstrate the effectiveness of graph theory-based approaches on various benchmark datasets and real-world applications.

Guest Editors:

Yassine Ouazene, University of Technology of Troyes, France

Achraf Jabeur Telmoudi, University of Tunis, Tunisia

Meng Chu Zhou, New Jersey Institute of Technology, USA

Fabio Fruggiero, Università degli Studi della Basilicata, Italy

Special issue description:

This special issue is dedicated to fostering the collaboration between optimization experts, industrial practitioners, and management scientists. Our aim is to address significant practical problems in Engineering and management through the application of advanced optimization techniques.

The primary objective of this special issue is to bridge the gap between theoretical optimization methodologies and practical applications in Engineering and management. We are seeking original research that applies intelligent computing and advanced optimization techniques to solve complex and challenging problems in these fields. Our focus is on promoting significant contributions to optimization methods, with a direct impact on real-world Engineering applications.

This is a call for papers that make a significant contribution to the topic of “Smart Optimization Methods for Engineering and Management Problems”. It is open to all researchers of this area. We would like to encourage, in particular, all participants of the IEEE 2024 International Conference on Control, Decision and Information Technologies (CoDIT 2024), held in Valletta (Malta), July 01-04, 2024 (https://codi2024.com), to submit extended versions of their presented papers.

Topics of Interest: We invite submissions that cover a range of topics, including but not limited to:

• Simulation-Based Optimization: Innovative uses of simulation in optimizing industrial and management processes
• Data-Driven Optimization Algorithms: Research focusing on the development and application of algorithms that leverage data analytics in industrial and management contexts.
• Mathematical Programming Approaches: Advanced applications of mathematical programming in solving industrial and management challenges
• Matheuristic Methods: Papers exploring matheuristic methods and their applications in complex optimization configurations
• Multiobjective Optimization Techniques: Studies dealing with multiobjective or multicriteria decision-making in Engineering and management problems.

Application Areas: We encourage submissions that demonstrate the applicability of smart optimization methods in various domains, including but not limited to:

• Energy Systems
• Telecommunication
• Industrial Systems
• Supply Chain Management 
• Transportation and Logistics
• Healthcare Systems
• Environmental Management
• Control Systems

Submission Guidelines:

• Manuscripts must be original and not currently under review by other journals or conferences
• Submissions should adhere to the journal's standard formatting and submission guidelines
• Each paper will undergo a rigorous peer-review process, ensuring high standards of academic quality and relevance.

Key Dates:

Submission deadline: February 28, 2025

First-round review decisions:  January 15, 2025

Deadline for revision submissions: February 15, 2025

Notification of Final Acceptance: March 30, 2025

Submission Process:Manuscripts should be submitted electronically through the journal's submission system at https://www.springer.com/mathematics/journal/11081 . Please indicate that the submission is for this special issue. Interested authors should consult the journal’s “Instructions for Authors”, at https://www.springer.com/mathematics/journal/11081 .

All submitted papers will be reviewed on a peer review basis as soon as they are received. Accepted papers will become immediately available at Online First until the complete Special Issue appears.

Snapp (Springer Nature’s Article Processing Platform) is our new peer review platform, replacing the previous system, Editorial Manager.

Guest Editors: D. Auroux (Universite’ Cote d’Azur, France), V. Kovanis (Virginia Tech, USA), H. Kunze (University of Guelph, Canada), D. La Torre (SKEMA Business School, France)

Submission deadline: November 30, 2023

This special issue aims at bringing together articles that discuss recent advances in machine learning and inverse problems. Machine Learning is a subset of Artificial Intelligence focusing on computers’ ability to learn from data and to imitate intelligence human behaviour. A typical inverse problem seeks to find a mathematical model that admits given observational data as an approximate solution. Recent contributions in these areas aim at exploring potential synergies between their two different domains of research.  From one hand, in fact, machine learning algorithms can leverage large collections of training data to directly compute regularized reconstructions and estimate unknown parameters. From the other hand, machine learning algorithms can benefit from the vast inverse problem literature and the existing contributions to the theory of inverse problems, and they can be used to simulate boundary value data when they are missing.

Guest editors: Dr. Marian Trafczynski (Warsaw University of Technology), Prof. Neven Duić (University of Zagreb), Prof. Krzysztof Urbaniec (Warsaw University of Technology), Dr. Hrvoje Mikulčić (Xi'an Jiaotong University/ University of Zagreb, Zagreb), Dr. Slawomir Alabrudzinski (Warsaw University of Technology).

Submission Deadline: December 31, 2022

The background of this Virtual Special Issue of the Optimization and Engineering journal are the 2022 Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conferences. This broad field was discussed by the participants of three conferences held in 2022 – 3rd Latin American SDEWES Conference (Sao Paulo), 5th Southeast European SDEWES Conference (Vlorë) and 17th SDEWES Conference (Paphos).

Guest Editors: H. Kunze (University of Guelph, Canada); D. La Torre (SKEMA Business School, France); and M. Ruiz-Galan (University of Granada, Spain)

Submission deadline: May 31, 2021

This special issue aims at bringing together articles that discuss recent advances of optimization methods and algorithms in inverse problems and application to science and engineering.  A typical inverse problem seeks to find a mathematical model that admits given observational data as an approximate solution. This sort of question is of great interest in many application areas, including biomedical engineering and imaging, remote sensing and seismic imaging, astronomy, oceanography, atmospheric sciences and meteorology, chemical engineering and material sciences, computer vision and image processing, ecology, economics, environmental systems, physical systems. Very often an inverse problem appears in the form of a parameter estimation problem, it can be formulated as an optimization model, and then solved using different optimization algorithms and techniques. All papers included in this special issue will consider aspects of numerical analysis, mathematical modeling, and computational methods.

Optimization and Engineering's Howard Rosenbrock Prize is awarded annually to honor the authors of the best paper published in the journal in the previous year.