𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲𝗼𝗿𝘆, 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀, 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴

𝗖all for papers

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲𝗼𝗿𝘆, 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀, 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴

𝗝𝗼𝘂𝗿𝗻𝗮𝗹:
Engineering Optimization

𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗿:
Taylor & Francis Group

𝗠𝗮𝗻𝘂𝘀𝗰𝗿𝗶𝗽𝘁 𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲:
28 February 2027

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲

Optimization has long served as the foundation of engineering decision-making through mathematical programming, metaheuristics, and robust and stochastic optimization approaches. Recent advances in machine learning, predictive modelling, and reinforcement learning are increasingly transforming how optimization methods are designed and implemented.

This Special Issue focuses on the principled integration of learning techniques into optimization, where learning augments rather than replaces optimization methodologies. The issue aims to advance research on learning-guided optimization methods that improve efficiency, scalability, robustness, and solution quality while addressing uncertainty and dynamic decision environments.

The Special Issue welcomes original contributions spanning theory, algorithms, rigorous empirical studies, and engineering applications that demonstrate practical relevance and computational viability.

𝗧𝗼𝗽𝗶𝗰𝘀 𝗼𝗳 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁

Submissions may address, but are not limited to:

• Learning-guided and adaptive metaheuristics
• Decision-focused learning and predict-then-optimize approaches
• Reinforcement learning integrated with mathematical programming and combinatorial optimization
• Surrogate-assisted optimization for computationally expensive problems
• Data-driven robust, stochastic, and distributionally robust optimization
• Learning-enhanced multi-objective optimization and multi-criteria decision-making
• Theoretical foundations of learning-augmented optimization
• Production scheduling and planning
• Supply chain and logistics network design
• Vehicle routing and transportation optimization
• Energy systems planning and operation
• Structural and engineering design optimization
• Maintenance, reliability, and resilience optimization
• Resource allocation in service and healthcare systems

𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀

• Organized in conjunction with the Asia Pacific Industrial Engineering and Management Systems (APIEMS) 2026 Conference
• Selected high-quality conference papers may be invited to submit extended versions
• Open to submissions from researchers worldwide
• Emphasis on both methodological innovation and practical engineering applications
• All manuscripts will undergo the standard single-blind peer-review process of Engineering Optimization

𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀

• Submissions open: 1 October 2026
• Manuscript submission deadline: 28 February 2027
• Manuscripts should be prepared according to the journal's author guidelines and submitted through the Engineering Optimization online submission system.
• Authors should clearly demonstrate how learning techniques contribute to the optimization process and validate their methods against meaningful baselines.

𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗘𝗱𝗶𝘁𝗼𝗿𝘀

• Janghyeok Yoon (Managing Guest Editor), Konkuk University, Republic of Korea
• Byung Soo Kim, Incheon National University, Republic of Korea
• Byung Do Chung, Yonsei University, Republic of Korea
• Chia-Yu Hsu, National Tsing Hua University, Taiwan
• Rapeepan Pitakaso, Ubon Ratchathani University, Thailand
• Sobhan Arisian, La Trobe University, Australia

𝗣𝗼𝘀𝘁𝗲𝗱 𝗼𝗻 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗦𝗲𝘁𝘂 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰𝘀 — 𝗣𝗿𝗲𝗺𝗶𝗲𝗿 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 & 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻

Visit official website of the publisher