Overview
ⓘ Please note this journal’s peer review system has changed, it now uses Snapp (Springer Nature’s Article Processing Platform). See the journal updates page for more information.
Optimization and Engineering promotes the advancement of optimization methods and the innovative application of optimization in engineering. It provides a forum where engineering researchers can obtain information about relevant new developments in optimization, and researchers in mathematical optimization can read about the successes of and opportunities for optimization in the various engineering fields. We encourage the submission of manuscripts that make a genuine mathematical optimization contribution to a challenging engineering problem.
- Editor-in-Chief
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- Michael Ulbrich
- Impact factor
- 2.1 (2022)
- 5 year impact factor
- 2.4 (2022)
- Submission to first decision (median)
- 10 days
- Downloads
- 203,224 (2023)
Latest articles
Journal updates
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Call for Papers: Special Issue on "Smart Optimization Methods for Engineering and Management Problems"
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: November 15, 2024
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.
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Special Issue on Graph Theory-based Approaches for Optimizing Neural Network Architectures
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.
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SNAPP – A New Manuscript Submission system
Snapp (Springer Nature’s Article Processing Platform) is our new peer review platform, replacing the previous system, Editorial Manager.
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Special Issue on Machine Learning and Inverse Problems
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.
Journal information
- Electronic ISSN
- 1573-2924
- Print ISSN
- 1389-4420
- Abstracted and indexed in
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- ANVUR
- BFI List
- Baidu
- CLOCKSS
- CNKI
- CNPIEC
- Current Contents/Engineering, Computing and Technology
- Dimensions
- EBSCO
- EI Compendex
- Google Scholar
- INIS Atomindex
- INSPEC
- Japanese Science and Technology Agency (JST)
- Mathematical Reviews
- Naver
- OCLC WorldCat Discovery Service
- Portico
- ProQuest
- SCImago
- SCOPUS
- Science Citation Index Expanded (SCIE)
- TD Net Discovery Service
- UGC-CARE List (India)
- Wanfang
- zbMATH
- Copyright information
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