جست‌وجوی استاد و پژوهشگر

Djamila Ouelhadjمشاهده پروفایل

استاد

• Operational Research
• Mathematical Optimisation
• Meta-heuristics and Hyper-heuristics
• +۶ مورد دیگر

Professor Djamila Ouelhadj of the University of Portsmouth is a leading academic in Operational Research, affiliated with the School of Mathematics and Physics and Centre for Operational Research and Logistics . She serves as Course Leader for MSc Logistics and Supply Chain Management and contributes to EU-funded projects like SEABILLA and LEANWIND . Her work spans mathematical optimization, meta-heuristics, and decision support systems applied to logistics, transportation, healthcare, and renewable energy. Research Themes : Operational Research, Logistics Analytics, AI for Decision Making, Sustainable Transportation Her recent publications focus on Mobility-as-a-Service (MaaS) systems, drone scheduling for wind farm inspection, and multi-criteria frameworks for inclusive transport. Articles highlight integration of machine learning , stochastic modeling , and robotics in solving real-world optimization problems. She employs multi-objective methods , simheuristics , and clustering techniques to address challenges in renewable energy, urban mobility, and security. Scientific Contributions include: Co-investigator on EU grants for maritime security and wind farm optimization Principal investigator for HEIF-funded Operational Research Centre Chair of scheduling streams at OR53-OR55 conferences Her advising extends to 15 theses, covering topics from nurse rostering to carbon footprint reduction. Collaborative grants include TSB and EU Horizon 2020 projects. She actively contributes to European Journal of Operational Research and leads the Logistics and Operational Research Group (LORG) in developing intelligent decision support systems for complex scheduling problems.

Yi Meiمشاهده پروفایل

دانشیار

• Evolutionary Computation
• Combinatorial Optimisation
• Genetic Programming
• +۱۰ مورد دیگر

Dr. Yi Mei is an Associate Professor and Associate Dean (Research) at the Faculty of Engineering, Victoria University of Wellington, New Zealand. He serves as Programme Director for Computer Science & Computer Graphics within the School of Engineering and Computer Science and is affiliated with the Centre for Data Science and Artificial Intelligence (CDSAI). Dr. Mei's research focuses on evolutionary computation for combinatorial optimisation , with specific expertise in genetic programming , automatic algorithm design , explainable AI , and multi-objective optimisation . His work bridges theoretical foundations with practical applications in transportation, healthcare, manufacturing, and aquaculture. His publication portfolio shows a clear trajectory toward increasingly complex real-world applications of evolutionary computation, with recent work focusing on emergency medical dispatch optimization , dynamic transportation systems , and explainable AI for routing problems . The research demonstrates strong interdisciplinary connections between evolutionary computation, machine learning, and domain-specific optimization challenges. Victoria University of Wellington Ki te Pae - Research Excellence Award 2024 Multiple Best Paper Awards at ACM Genetic and Evolutionary Computation Conference (GECCO) from 2022-2024 HUMIES Silver Award at GECCO 2023 IEEE Transactions on Evolutionary Computation Outstanding Associate Editor (2024, 2025) Fellow of Engineering New Zealand and IEEE Senior Member Dr. Mei has successfully supervised numerous PhD and Master's students whose research has received recognition, including the ACM SIGEVO Dissertation Award. He leads significant research projects funded by MBIE Endeavour Smart Idea Fund ($1M NZD), New Zealand Royal Society Catalyst Leaders Fund ($150K NZD), and industry partners like Honda Research Institute Europe. His service contributions include editorial roles for top journals including IEEE Transactions on Evolutionary Computation and leadership positions in IEEE Computational Intelligence Society. Dr. Mei leads the Evolutionary Computation for Combinatorial Optimisation Group (ECCO) and the Evolutionary Computation and Machine Learning Research Group (ECRG) at Victoria University of Wellington. He also chairs the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (TESCO), fostering international collaboration in evolutionary computation applications.

Seyed Mojtaba Sajadiمشاهده پروفایل

مدرس

• Simulation-Based Optimisation
• Applied Operation Research
• Artificial Intelligence and Meta-heuristic Algorithms
• +۴ مورد دیگر

Dr. Seyed Mojtaba Sajadi is an Assistant Professor (Lecturer) in Operations and Supply Chain Simulation at Aston Business School, part of Aston University's College of Business and Social Sciences. He holds a PhD in Industrial Engineering from Amirkabir University of Technology and has extensive academic experience, including roles as Associate Professor and Assistant Professor at the University of Tehran (2010–2020), and Assistant Professor at Coventry Business School (2021–2022). His research focuses on simulation-based optimization in supply chain management, healthcare systems, and disaster management, leveraging techniques like Discrete Event Simulation (DES), Agent-Based Simulation (ABS), and metaheuristic algorithms. Education: PhD in Industrial Engineering, Amirkabir University of Technology MSc in Industrial Engineering, University of Tehran BSc in Industrial Engineering, Sharif University of Technology PGCert in Higher Education, Coventry University Research Interests: Simulation-Based Optimization Applied Operations Research Machine Learning Supply Chain and Production Optimization Healthcare and Disaster Management Editorial Roles: Associate Editor, Journal of Simulation (2023–Present) Editorial Advisory Board, Journal of Enterprise Information Management (2024–Present) Advising and Grants: Supervised over 20 PhD candidates, including current projects on disaster-resilient smart cities and Industry 4.0 in food sustainability. Active in securing funded research projects, such as an Aston PhD Studentship and a Saudi Government-funded initiative. Labs/Teams: Engaged in collaborative research across international institutions, including the Operational Research Society Simulation Workshop (SW25, 2025) and interdisciplinary projects in healthcare logistics and smart supply chains.

Shengxiang Yangمشاهده پروفایل

استاد

• Evolutionary Computation
• Swarm Intelligence
• Meta-heuristics
• +۷ مورد دیگر

Shengxiang Yang is Professor of Computational Intelligence and Director of the Centre for Computational Intelligence (CCI) at De Montfort University, within the Faculty of Computing, Engineering and Media and the School of Computer Science and Informatics. He is actively involved in research, supervision, and teaching, and leads a vibrant research group in computational intelligence. Education: BSc in Automatic Control, Northeastern University, China (1993) MSc in Automatic Control, Northeastern University, China (1996) PhD in Systems Engineering, Northeastern University, China (1999) His primary research interests lie in evolutionary computation , especially in dynamic and uncertain environments . He has made significant contributions to swarm intelligence, meta-heuristics, multi-objective and constrained optimization. His work extends to real-world applications in communication networks, logistics, transportation, and manufacturing systems. His teaching includes research methods and modules in AI, networks, programming, and software engineering at both undergraduate and postgraduate levels. The recent publications highlight a strong trend in dynamic optimization , multi-objective problems , and knowledge transfer in evolutionary algorithms. There is increasing integration of machine learning (e.g., LSTM, Q-learning) with evolutionary techniques, and a focus on hybrid and memetic algorithms for complex scheduling and real-world problems. Scientific Awards and Honors: Winner of the 2014 IEEE Congress on Evolutionary Computation Best Student Paper Award Nominee for Best Paper Awards at EvoApplications 2016, GECCO 2015, and GECCO 2005 Visiting Professor at Xiangtan University (2012–2014, 2016–2018) and Nanjing University of Information Science and Technology (2011–2017) Supervision and Funding: Professor Yang has supervised numerous PhD students, both as first and second supervisor, and has secured substantial research funding from EPSRC, EU Horizon 2020, State Key Laboratory of China, and internal DMU sources. He has led projects on evolutionary computation for dynamic optimization in networks, constrained problems, rail travel, and airport scheduling. He has also coordinated multiple BSc final year projects across institutions. Professional Leadership: He has held leadership roles in IEEE CIS, including Chair of the Task Force on Evolutionary Computation in Dynamic and Uncertain Environments and Founding Chair of the Task Force on Intelligent Network Systems. He serves on the editorial boards of top journals such as Information Sciences , IEEE Transactions on Cybernetics , and Evolutionary Computation .

Bugra Alkanمشاهده پروفایل

مدرس ارشد

• Smart Manufacturing Systems
• Collaborative Robotics
• Industrial Artificial Intelligence
• +۷ مورد دیگر

Dr Bugra Alkan is a Senior Lecturer in Artificial Intelligence and Robotics at the School of Computer Science and Digital Technologies , London South Bank University . With a PhD in Engineering from University of Warwick (2019), he has held postdoctoral positions at University of Bristol and University of Warwick . Academic Rank: Senior Lecturer Institutional Roles: Module Leader for Industrial Cyber-Physical Systems, Artificial Intelligence, Systems Cybersecurity Editorial Roles: Associate Editor for Industrial Robot and Heliyon , Editorial Board Member for MDPI Sensors and Frontier of Industrial Engineering Research focuses on smart manufacturing , collaborative robotics , and industrial AI with emphasis on cyber-physical production systems , digital twins , and Industry 5.0 . Key projects include: Data-driven Configuration Optimisation of CPPS AI-Enabled Digital Life-cycle Management Deep Learning-based Inline Quality Inspection Human-centric Collaborative Assembly 5.0 Scientific contributions include: Best Paper Awards at CIRP CATS 2016 and IATS’13 Over 40 peer-reviewed publications with h-index 16 Editorial leadership in Q1/Q2 journals Teaching responsibilities span artificial intelligence , cyber-physical systems , and statistical modeling at both undergraduate and postgraduate levels. Current research involves EU-funded TWIN-IT-ROMANS project (€1.76M) addressing robotics manufacturing systems.

• Operations Research
• Supply Chain Management
• Logistics Systems
• +۱۶ مورد دیگر

Martino Luis is a Senior Lecturer in Engineering Management at the Department of Engineering, College of Engineering, Mathematics and Physical Sciences, University of Exeter. He currently serves as the Director of Education for Postgraduate Taught (PGT) programmes. Prior to joining the University of Exeter, he was a faculty member at the Othman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia, where he also directed the MBA programme and contributed to accreditation efforts under ISO 9001:2015, ISO 14001:2015, and IQNet SR10:2015, in collaboration with the Chartered Management Institute (CMI) UK. His research focuses on the application of exact methods, meta-heuristics, and matheuristics to complex problems in supply chain and logistics systems. Key areas include: Sustainable supply chain network design Facility location and location-inventory optimisation Inventory routing under uncertainty Integrated production and distribution planning Resilient and closed-loop supply chains Simulation-optimisation in healthcare and energy sectors Martino is a Chartered Member of the Chartered Institute of Logistics and Transport (CMILT), a member of the Operational Research Society, and the EURO Working Group on Locational Analysis (EWGLA), reflecting his strong engagement with the logistics and operations research communities. He currently supervises multiple PhD students on topics related to sustainable and resilient supply chains, digital construction frameworks, and blood supply chain optimisation, often in collaboration with other faculty members. He welcomes prospective PhD students and research collaborators in these fields. Martino holds a PhD in Operations Research from Kent Business School, University of Kent, and brings industry experience from a supervisory role in a shoe factory in Bandung, along with industrial consultation and training experience. Professional Memberships: Chartered Member, Chartered Institute of Logistics and Transport (CMILT) Member, Operational Research Society Member, EURO Working Group on Locational Analysis (EWGLA)

• Supply Chain Modelling
• Logistics Optimisation
• Supply Chain Risk and Resilience
• +۷ مورد دیگر

Dr Dnyaneshwar (Danny) Mogale is Senior Lecturer in Supply Chain and Logistics Modelling at Cardiff Business School, Cardiff University . A Fellow of the Higher Education Academy (FHEA), he previously served as Visiting Researcher at Cranfield School of Management and Research Assistant at the Geneva School of Business Administration, Switzerland. With a PhD from IIT Kharagpur, MTech from NIT Tiruchirappalli, and BTech from SRTMU Nanded, he brings extensive interdisciplinary expertise to his role. Research Interests Dr Mogale's research centres on developing mathematical models for complex, real-world supply chain and logistics challenges. His core areas include: Supply Chain & Logistics Modelling – designing optimisation frameworks for freight distribution, vehicle routing, and network design. Risk & Resilience – analysing disruptions (COVID-19, geopolitical, climatic) and devising mitigation strategies. Sustainability – integrating economic, environmental, and social objectives into supply chain planning. His work aligns with Cardiff Business School’s Public Value strategy, aiming to deliver tangible societal impact. Research Projects & Funding Dr Mogale has secured competitive grants totalling over £245,000 : Knowledge Transfer Partnership , Innovate UK & Tower Cold Chain Solutions – £239,719 (2023-2026, Co-I). ESRC Impact Acceleration Account – £3,000 (2022-2023, PI): robust distribution of temperature-sensitive pharmaceuticals. Global Wales International Research Mobility Fund – £3,180 (2020-2022, PI): COVID-19 impacts on sustainable food supply chains. Publications & Knowledge Exchange He has published 30+ peer-reviewed articles in leading journals such as Transportation Research Part E , International Journal of Production Economics , Production Planning & Control , and Computers & Industrial Engineering . His research trends reveal a consistent focus on: Multi-objective optimisation for sustainable supply chains. Resilience modelling during disruptions. Integration of AI & Big Data analytics in logistics. Scientific Awards & Recognition IFAC 2019 Young Author Award Finalist Invited session chair at WPGRC 2022 (Cardiff) and ICIEM 2021 (MNIT Jaipur) Organiser & chair – LRN 2021 special session on “Sustainability & Circular Economy Challenges in Food Supply Chains” Judge – CILT UK Logistics Dissertation of the Year Awards, James Cooper Memorial Cup, and Seed Corn Research Fund Cardiff University representative on CILT(UK) Logistics & Research Network Forum since 2020 Teaching & PhD Supervision Dr Mogale currently leads three MSc modules: BST846 – Modelling and Analysis for Sustainable Supply Chains & Logistics BST835 – Risk Management in Supply Chains BST811 – Business Data Analytics He welcomes PhD candidates in supply chain modelling, food supply chains, sustainability, and risk/resilience. Current supervisees include Jianhao Yang and S M Salauddin Salauddin . Professional Service & Outreach Dr Mogale serves as: Reviewer for Transportation Research Part E , IJPE , IJPR , PPC , Annals of OR , CAIE and other leading journals International PhD examiner and conference reviewer (IFAC, LRN, IPSERA) Invited guest lecturer at IIT(BHU) Varanasi, MNIT Jaipur, Clemson University, and Mahatma Gandhi Institute of Technology Organising committee member – LRN PhD Workshop 2022 (Aston)

Ozgur Akgunمشاهده پروفایل

مدرس ارشد

• Constraint Programming
• Data Mining
• Cloud Computing
• +۸ مورد دیگر

Dr Ozgur Akgun is a Senior Lecturer and Director of Impact at the School of Computer Science, University of St Andrews. His research focuses on Constraint Programming (CP), including the development of the automated constraint modelling tool Conjure. He explores applications of CP in areas like data mining, cloud computing, network science, logistics, and economics. Akgun teaches core CS modules for undergraduates and advanced courses in constraint programming, discrete optimization, and video games. He supervises PhD students and leads the VIP project 'Artificial Intelligence for Decision Making'. His work also spans functional programming, data linkage, and the intersection of machine learning with CP. Research interests include automated constraint modelling, algorithm selection/configuration, and data linkage using graph databases. Current projects involve cost-efficient algorithm training and frugal algorithm selection. He collaborates on interdisciplinary projects such as the ADR UK Programme and Modelling and Optimisation with Graphs. His teaching includes supervising undergraduate, master's, and PhD students, contributing to both academic and applied computer science education.

Ramin Raeesiمشاهده پروفایل

مدرس

• Combinatorial optimisation
• Exact and (meta-) heuristic solution algorithms
• Multi-objective optimisation
• +۵ مورد دیگر

Dr Ramin Raeesi is a Lecturer in Management Science at Kent Business School, University of Kent, and an Interim Director of the Centre for Logistics and Heuristic Optimisation (CLHO). He also holds a visiting researcher position at the Centre for Transport and Logistics (CENTRAL) at Lancaster University Management School. Research Overview: Focuses on combinatorial optimisation, vehicle routing, and sustainable logistics with environmental considerations. Research Interests include: Exact and metaheuristic solution algorithms Multi-objective optimisation Electric and hydrogen-fuelled transport systems Shared logistics and container terminal optimisation Publications highlight work on electric vehicle routing with time windows and mobile battery swapping. His research integrates big data analytics into transport and logistics networks. Labs & Groups: Centre for Logistics and Heuristic Optimisation (CLHO) Centre for Transport and Logistics (CENTRAL)

Daniel Karapetyanمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Artificial Intelligence
• Data Science
• +۱۷ مورد دیگر

Daniel Karapetyan is an Assistant Professor at the School of Computer Science , University of Nottingham, and a member of the Computational Optimisation and Learning Lab . His research spans Artificial Intelligence , Data Science , Operational Research , and Optimization , with applications in transportation , logistics , satellite mission planning , and access control . Education: PhD in Computer Science, Royal Holloway, University of London (2007–2010) BSc/MSc in Computer Science, Bauman Moscow State Technical University (2001–2007) His research interests focus on the automated design of heuristic algorithms , multi-objective optimization , and parameter tuning for machine learning and combinatorial problems. He integrates Large Language Models (LLMs) and hyperparameter optimization into practical systems, addressing real-world challenges in airport sequencing , TV ad scheduling , and electric vehicle range prediction . Notable scientific contributions include the CORS Practice Prize (2014) for ferry scheduling work and pioneering fixed-parameter algorithms in security-related optimization problems. His publications emphasize combinatorial optimization , Markov Chain methods , and automated algorithm configuration . Teaching roles include module convener for COMP3008: Knowledge Representation and Reasoning , personal tutoring for COMP1002 , and supervision of COMP2002: Software Engineering Group Project . He also offers PhD supervision in areas like AI for optimization , interpretability in ML , and emergency department coordination .

Vinod Kumar Chauhan Kumarمشاهده پروفایل

مدرس

• Causality
• Healthcare
• Artificial Intelligence
• +۵ مورد دیگر

Vinod Kumar Chauhan Kumar is a Strathclyde Chancellor's Fellow in AI (Lecturer rank) in the Faculty of Science at the University of Strathclyde . He concurrently holds a Visiting Scholar position and the MPLS Enterprise and Innovation Fellowship (2025–26) at the University of Oxford . Education: PhD in Optimisation for Large-Scale Machine Learning, Panjab University, Chandigarh (Awarded 2019) Supported by University Grants Commission’s JRF and SRF Fellowships Research Focus: Dr. Chauhan pioneers data-driven Causal AI for personalized healthcare, bridging Causality , Healthcare , and Artificial Intelligence . His work develops novel methods for treatment effect estimation, bias correction in healthcare ML, and graph-based EHR analysis, with applications in gastric disease prediction and industrial optimization. The UN Sustainable Development Goals framework guides his healthcare impact initiatives. Publication Trends: Recent work (2023-2025) demonstrates three converging thrusts: (1) causal inference frameworks for composite treatments/outcomes in healthcare, (2) graph neural networks for EHR analysis (e.g., CliqueFluxNet), and (3) industrial AI applications. Over 30% of his publications address healthcare bias challenges, while 25% focus on handwriting recognition for Indic scripts. Scientific Recognition: Institute for Manufacturing Postdoctoral Award for Research Excellence (2021) Student Travel Grant Award (2017) Academic Service: Dr. Chauhan actively mentors future researchers and currently seeks PhD students for causal AI projects. He has reviewed 200+ articles across 50+ venues and serves as Associate Editor for PLOS Digital Health and Editorial Board Member for the International Journal of Artificial Intelligence in Healthcare . His NeurIPS AI4Science Area Chair role highlights community leadership. Collaborative Impact: His research integrates academic rigor with real-world validation through partnerships with clinicians and industry leaders including Boeing and Rolls-Royce, developed during six years of postdoctoral work at Oxford and Cambridge.

• Smart Grids
• Power System Optimization
• Renewable Energy Integration
• +۷ مورد دیگر

Souhil Mouassa is a Spanish-based power-systems researcher who earned his PhD in 2021 from the University of Jaén , Spain, under the supervision of Dr. Francisco Jurado Melguizo. He is currently affiliated with the Department of Electrical Engineering at the same university, where he pursues advanced studies in smart-grid optimisation, renewable-energy integration and meta-heuristic algorithms. Education PhD in Electrical Engineering, University of Jaén, Spain (2021) Thesis: Optimisation de l’écoulement de puissance par les méthodes non-conventionnelles dans les réseaux électriques intelligents (Smart Grid) Research Interests Mouassa’s work spans the entire spectrum of modern power-systems engineering, focusing on: Optimal power-flow computations for hybrid thermal-wind-solar networks Meta-heuristic and bio-inspired optimisation (Electric Eel, Gorilla Troops, Kepler, Bonobo, Dwarf Mongoose, Tuna Swarm, AEO, GTO, FFA, etc.) Integration of FACTS devices (TCSC, SVC, UPFC) to enhance grid stability and power quality Smart-home and smart-grid energy-management systems with storage and demand response Photovoltaic systems, MPPT techniques and active-power filtering under non-ideal grid conditions Stochastic modelling of renewable-generation uncertainty and its impact on isolated and large-scale networks Power-electronics control—Lyapunov-based adaptive sliding-mode schemes for DC-DC converters Publication Profile Between 2017 and 2025 Mouassa authored more than thirty peer-reviewed papers, concentrated in high-impact energy, power-engineering and applied-soft-computing journals. His 2024-2025 output alone includes nine articles that apply cutting-edge swarm and evolutionary algorithms to real-world optimal-power-flow and energy-management problems, demonstrating a clear trajectory toward large-scale, practical implementation of renewable-rich power systems. Notable case studies include the isolated Adrar network in Algeria and various European-scale transmission systems. Scientific Awards No specific prizes, fellowships or medals have been publicly listed to date. Advising & Grants No supervised students or funded projects are explicitly mentioned in the provided materials. Laboratories & Teams Mouassa carries out his research within the Department of Electrical Engineering research group at the University of Jaén, accessing power-system simulation laboratories and computational clusters necessary for large-scale optimisation studies.

Seyedali Mirjaliliمشاهده پروفایل

استاد

• Optimization
• Swarm Intelligence
• Evolutionary Algorithms
• +۶ مورد دیگر

Seyedali Mirjalili is a Professor of Artificial Intelligence and Director of the Centre for Artificial Intelligence Research and Optimization (AIRO) at Torrens University Australia. He holds distinguished professorships at VSB Technical University of Ostrava, Óbuda University, and De La Salle University, and is an adjunct researcher at Griffith University and Yonsei University. His research focuses on optimization algorithms, swarm intelligence, evolutionary computation, and machine learning. Education: PhD in Computer Science (Griffith University, 2016). Research interests include optimization techniques, swarm intelligence algorithms (e.g., Grey Wolf Optimizer, Whale Optimization Algorithm), machine learning applications, and robust optimization frameworks. He has published over 600 papers with an H-index of 110 and is among the most cited researchers in optimization fields globally. Awards include the Pro-Vice Chancellor Research Special Award (2019) and recognition as a top 1% highly cited researcher (since 2019). He serves as an associate editor for journals like Neurocomputing and Applied Soft Computing. Labs/Teams: Leads the AIRO Centre at Torrens University, focusing on AI-driven solutions for complex optimization problems.

• Operational Research
• Combinatorial Optimisation
• Mathematical Programming
• +۷ مورد دیگر

Greet Vanden Berghe is a full professor at the Faculty of Engineering Technology of KU Leuven. She leads the Subdivision Combinatorial Optimisation and Decision Support at Ghent and Aalst Campuses, and contributes to research networks like Leuven.AI and LIM. Her work focuses on industrial combinatorial optimization problems through mathematical programming, heuristics, and decomposition approaches. Affiliations: Numerical Analysis and Applied Mathematics (NUMA), Leuven.AI, LIM Institute Research: Logistics optimization, personnel scheduling, vehicle routing, and algorithm development for industrial decision support Projects: MoVeRS (Modular Vehicle Routing Solver), Trustworthy Algorithms for Industrial Decisions, Fairness in Allocation Problems, and Electric Vehicle Charging Station Optimization Her research emphasizes bridging theoretical advancements with real-world industrial challenges, particularly in maritime logistics, healthcare scheduling, and production planning. She has developed scalable solvers and integrated legal constraints into routing algorithms.