In our daily lives, we benefit from the application of Optimization theories and algorithms. They are used, for example, by IoT devices, GPS systems, by shipping companies delivering packages to our homes, by financial companies, airline reservations systems, etc. Optimization is a discipline that solves a great variety of real-world applied problems in diverse areas: transportation, supply chain, manufacturing, finance, government, economics, etc.

Our research group consists of academic staffs in the School of Information and Technology (SoICT) who are actively involved in teaching, supervising and researching across a wide range of topics and areas in Optimization. Our research focuses on the development, analysis and implementation of advanced theories and algorithms to provide high value solutions to complex real-world problems and challenges. We also have research collaboration with other optimization groups of Nanyang Technological University, Singapore Management University (Singapore) and University of Sydney, La Trobe University, University of Technology Sydney (Australia).

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Contact: Assoc. Prof. Huynh Thi Thanh Binh, Email:

Research Directions

  • Logistics and transportation optimization: Transportation is a very relevant sector for contemporary society, both for companies and individuals. Every day, thousands types of commodities such as fresh food, frozen or dairy products, small to large packages, etc. are shipped between locations within or outside cities.Thanks to the development of digitization and automation technologies, in the past few years, we have seen an increase in the number of logistics companies. Their main goal is to transport different kinds of goods domestically and internationally based on customer needs. They thus all share the same challenges: how to manage limited fleets of vehicles, handle warehouses, mange inventory, etc. to make profit as much as possible, while enhancing customer satisfaction, improving working conditions for drivers, reduce carbon footprint, etc. Given these presence of multiple business constraints, our research is to analyze, design and develop planning and optimization methods to create better decision support to companies within the supply chains, transport sector.
  • Multitask learning, transfer learning: Inspired by the human solving ability that routinely uses a pool of knowledge drawn from past experiences whenever faced with a new task, Transfer Learning and Multitask Learning have gained much attention within the Artificial Intelligence community. Besides, real-world problems seldom exist in isolation. For example, many routing problems are repetitive or and network designs share common characteristics or support each other. Knowledge from solving one problem may help solve other problems more efficiently. Transfer Learning aims at solving problems that occur sequentially, and the knowledge obtained when tackling preceding tasks is employed as external information when dealing with new problems/instances. In contrast, Multitasking Learning tackles multiple different tasks simultaneously by dynamically online exploiting synergies existing among them. Viewed from different angles, Transfer Learning and Multitask Learning can be applied to various optimization fields, ranging from theory to practice. Our work focuses on designing novel multitasking algorithms that can simultaneously solve a massive number of tasks. Besides, we will investigate these approaches to solve real-world optimization problems.
  • Multi-domain network design optimization: A multi-domain network consists of multiple domains, in which each domain is administrated as a unit with the same rules and procedures. Routing is the fundamental problem showing the path of resources in the network. In a communication network, efficient routing provides more control to the operators and enables the delivery of services with quality of service across domain boundaries. In Military communication, various elements of military forces, such as the army, navy, air forces, and special units, cooperate to achieve specific tactical goals. Each one of these organizations has its network structure in one or more domains. Besides, freight routing planning aims at assigning optimal routes to move commodities from their origins to the respective destinations through the transportation networks. Although it is a short-term decision making in the transportation network design, freight routing planning is oriented directly on satisfying the customers’ demand, and its performance determines the competitiveness of a transportation carrier or a third-party logistics company in the freight market.
  • Charging schedule optimization in Wireless Rechargeable Sensor Networks: Sensor nodes in conventional Wireless Sensor Networks are often powered by batteries, thus, they can only operate for a limited period of time depending on battery capacities. When some sensors deplete their energy, the network would become fragmented and the data from some parts of the sensing field can no longer be extracted. Wireless energy charging was proposed as a promising technique to address the energy provisioning problem. However, this technology also brings new challenges, including charging scheduling and energy forecasting for constructing an effective charging schedule. Solving these problems is of great significance, as the first step to exploiting the wireless charging technology and providing sustainable power for sensor networks. It can be applied in the harshness of environments, such as earthquakes, soil monitoring, large scale wireless sensor networks.
  • Path planning with energy optimization for mobile robots: Mobile robots have become more commonplace in commercial and industrial settings. Hospitals have been using autonomous mobile robots to transfer materials. Warehouses have installed mobile robotic systems to efficiently move materials from stocking shelves to order fulfillment zones. Mobile robots are also found in industrial, military and security settings. To power a mobile robot usually use batteries. Battery power is limited. In order for the robot to work effectively, it is necessary to schedule the optimal robot path to optimize energy; need to predict exhausted energy to plan charging in time.
  • Optimization techniques for Credit scoring problem: Credit scores are the most commonly used tool by financial institutions for determining consumer credit risk. In the last few decades, quantitative methods known as credit scoring models have been developed for the credit granting decision. The objective of quantitative credit scoring models is to assign credit applicants to one of two groups: a “good credit” group that is likely to repay the financial obligation, or a “bad credit” group that should be denied credit because of a high likelihood of defaulting on the financial obligation. However, quantitative methods have some limitations, such as they can not consider additionally constraints defined by risk management experts, they base completely on training dataset. This leads to our research direction, how to apply optimization techniques to build credit scoring models (non-parametric approaches), which not only are more accurate, but also can consider side constraints defined by users. Potential optimization techniques in this research are both complete search approaches (Integer Programming, Constraint Programming) and incomplete search approaches (Local Search, Meta-Heursitic).
  • Resources management in the cloud-fog environment optimization: In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT applications, fog computing is an emerging distributed computing paradigm that has recently attracted the attention of both the industry and academic community for guaranteeing the requests of computational applications in IoT smart devices. In the fog environment, IoT applications are executed by the intermediate computing nodes in the fog, as well as the physical servers in cloud data centers. Fog computing contributes to processing large amounts of data generated in smart transportation, smart grid, smart health, smart home, and smart home, and other latency-sensitive applications. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of the fog environment, it necessitates resource management issues as one of the challenging problems to be considered in the fog landscape. To solve the resource management challenge, we adopt several promising approaches such as heuristics, deep learning, reinforcement learning, …
  • Graph Neural Network for combinatorial optimization: Combinatorial optimization (CO) aims to find optimal configurations in discrete spaces where exhaustive enumeration is intractable. In general, the CO problems can be divided into three subclasses: Mixed Integer Program (MIP), Satisfiability Problem (SAT), and Constraint Program (CP). Many real world problems with high applicability can be formulated as CO problems, especially MIP problems such as Traveling Salesman Problem, Set Covering, Maximal Independent Set, etc. Current SOTA CO solvers often use sophisticated heuristics to solve hard CO problems. However, these solvers will try to solve the new issue from scratch without utilizing knowledge from previous problems. On the other hand, most CO problems have many common elements. So can we take advantage of the common ground between these problems? Obviously, machine learning can! We formulate CO problems with graph representation and use the Graph Neural Network to approximate the distribution of each CO problem. This can support traditional algorithms, speeding up the solving time with a data-driven approach.
  • Maximizing Wireless Sensor Network Coverage: Coverage which is one of the most important performance metrics for sensor networks reflects how well a sensor field is monitored. Individual sensor coverage models are dependent on the sensing functions of different types of sensors, while network-wide sensing coverage is a collective performance measure for geographically distributed sensor nodes. A major problem when designing these networks is deploying sensors such that their area coverage is maximized. Given a number of sensors with heterogeneous sensing ranges, the problem of coverage maximization is known to be NP-hard. As such, our main goal is to analyze and develop novel methods that rely on metaheuristic algorithms to support sensor network design/deployment with realistic requirements.

Research Problems

  • Optimization in the transportation, drone problems: Research optimization algorithms in the Trucks and Drones delivery problem, Door to Door sampling services, Multi-echelon distribution system in city logistics; Transportation Logistics Networks; minimum routing cost problems in the multi-domain networks;
  • Multi-objective optimization, multi-tasking evolutionary algorithms: Research and application of multi-objective, multi-tasking evolutionary algorithms in solving interdisciplinary optimization problems.
  • Multi-domain network design optimization: Develop multitasking evolutionary and heuristic algorithms to find the shortest path with uniqueness constraints and efficient network structures in the multi-domain network.
  • Charging schedule optimization:study wireless charging models to prolong the life of the network; Propose algorithms to optimize the cycle of the charging robot, optimize the charging stop time; energy prediction to plan charging on time.
  • Path planning with energy optimization for mobile robots: Propose optimization algorithms to find shortest path, minimizing energy for mobile robots; predict energy to plan charging in time.
  • Optimization techniques for Credit scoring problem: Study optimization techniques – complete search approaches (Integer Programming, Constraint Programming) and incomplete search approaches (Local Search, Meta-Heursitic) to solve credit score problem.
  • Reinforcement Learning for Combinatorial Optimization: Design Reinforcement Learning frameworks to solve complex combinatorial optimization instead of using traditional optimization algorithms.
  • Resources management in the cloud-fog environment optimization: Propose heuristics, deep learning, reinforcement learning to solve resources management in fog computing.

Team Members

Assoc. Prof. Huynh Thi Thanh Binh
Team Leader

Assoc. Prof. Do Phan Thuan

Dr. Pham Quang Dung

Dr. Do Tien Dung

Dr. Nguyen Khanh Phuong

Dr. Ban Ha Bang

Dr. Bui Quoc Trung

Post-doc and PhD students

Do Tuan Anh
PhD Student

Tran Thi Huong
PhD Student

Do Bao Son
PhD Student

Nguyen Van Son
PhD Student

Nguyen Thi Tam

Dr. Nguyen Thi Hanh

Dr. Hoang Thi Diep

Dr. Pham Dinh Thanh
Past PhD students (researcher)

Dr. Nguyen Thi My Binh
Past PhD students (researcher)

Projects and Solutions

Latest Publications

Publications in 2023

  1. Bùi Thị Mai Anh, Dương Việt Anh, Bùi Quốc Trung. A Filter Approach Based on Binary Integer Programming for Feature Selection. RIVF 2022. 677-682. Ho Chi Minh City. 20/12/2022
  2. Do Tuan Anh, Huynh Thi Thanh Binh, Nguyen Duc Thai, Pham Dinh Thanh. A Particle Swarm Optimization and Variable Neighborhood Search based multipopulation algorithm for Inter-Domain Path Computation problem. Applied Soft Computing Journal. 20/01/2023

Publications in 2022

  1. Son Nguyen Van, Nhan Vu Thi Hong, Dung Pham Quang, Hoai Nguyen Xuan, Behrouz Babaki, Anton Dries. Novel online routing algorithms for smart people-parcel taxi sharing services. ETRI Journal. 220-231. 20/02/2022
  2. Do Tuan Anh; Nguyen Hoang Long; Tran Van Diep; Huynh Thi Thanh Binh. A Genetic Ant Colony Optimization Algorithm for Inter-domain Path Computation problem under the Domain Uniqueness constraint. 2022 IEEE Congress on Evolutionary Computation (CEC). 1-8. Padua, Italy. 18/07/2022
  3. Phạm Đăng Hải, Ban Hà Bằng. Multifactorial Evolutionary Algorithm for Simultaneous Solution of TSP and TRP. The journal Computing and Informatics. 1370–1397. 18/02/2022
  4. Minh Anh Nguyen, Hai Long Luong, Minh Hoang Ha, Ha-Bang Ban. An efficient branch-and-cut algorithm for the parallel drone scheduling traveling salesman problem. 4OR. 08/11/2022
  5. Huynh Thi Thanh Binh, Le Van Cuong, Ta Bao Thang, Nguyen Hoang Long. Ensemble Multifactorial Evolution with Biased Skill-Factor Inheritance for Many-task Optimization. IEEE Transactions on Evolutionary Computation. 1-15. 18/11/2022
  6. Bui Quoc Trung, Le Minh Duc, Bui Thi Mai Anh. A Hybrid Approach based on Genetic Algorithm with Ranking Aggregation for Feature Selection. Thirty-Fifth International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems. 127-140. Kitakyushu, Japan. 19/07/2022
  7. Viet Dung Nguyen, Phi Le Nguyen, Kien Nguyen, Phan Thuan Do. Constant Approximation for Opportunistic Sensing in Mobile Air Quality Monitoring System. Computer Networks Journal. 15/01/2022
  8. Tạ Bảo Thắng, Huỳnh Thị Thanh Bình. A hybrid Multifactorial Evolutionary Algorithm and Firefly Algorithm for the Clustered Minimum Routing Cost Tree Problem. Knowledge-Based Systems. 1-14. 14/01/2022
  9. Nguyen Duc Anh, Tran Thi Huong, Nguyen Thanh Tung, Huynh Thi Thanh Binh, Frederica Free Nelson, Le Trong Vinh. Bi-level optimization for charging path and charging time in wireless rechargeable sensor networks. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV. 1-16. Florida, United Satets. 06/06/2022
  10. Van Son Nguyen, Quang Dung Pham, Thanh Hoang Nguyen, Quoc TrungBui. Modeling and solving a multi-trip multi-distribution center vehicle routing problem with lower-bound capacity constraints. Computers & Industrial Engineering. 18/08/2022
  11. Nguyen Thi My Binh, Huynh Thi Thanh Binh, Nguyen Hong Ngoc, Nguyen Khanh Van, Shui Yu. A family system based evolutionary algorithm for obstacles-evasion minimal exposure path problem in Internet of Things. Expert Systems with Applications. 1-14. 17/03/2022
  12. Van Son Nguyen, Quang Dung Pham. Solving a Real-World Problem of Truck-Trailer Scheduling in Container Transportation by Local Search. Journal of Science and Technology. 064-073. 28/03/2022
  13. Bùi Quốc Trung, Trần Văn Trí, Bùi Thị Mai Anh. Empirical Analysis of Filter Feature Selection Criteria on Financial Datasets. SOICT 2022. 413-419. Hanoi - Quang Ninh. 01/12/2022
  14. Lê Văn Cường, Nguyễn Ngọc Bảo, Nguyễn Khánh Phương, Huỳnh Thị Thanh Bình. Dynamic perturbation for population diversity management in differential evolution. GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. 391-394. 09/07/2022
  15. Do Bao Son, Ta Huu Binh, Hiep Khac Vo, Binh Minh Nguyen, Binh Huynh Thi Thanh, Shui Yu. Value-based reinforcement learning approaches for task offloading in Delay Constrained Vehicular Edge Computing. Engineering Applications of Artificial Intelligence. 1-14. 14/04/2022

Publications in 2021

  1. Ban Hà Bằng. Applying Metaheuristic for Time-Dependent Traveling Salesman Problem in Post-Disaster. J. International Journal of Computational Intelligence Systems. 1087-1107. 07/02/2021
  2. Tuan Anh Do, Huynh Thi Thanh Binh, Hoang Long Nguyen, Bao Thang Ta, Simon S. A Two-level Genetic Algorithm for Inter-domain Path Computation under Node-defined Domain Uniqueness Constraints. 2021 IEEE Congress on Evolutionary Computation. 1-8. Poland. 28/06/2021
  4. Van An Le, Tien Thanh Le, Phi Le Nguyen, Huynh Thi Thanh Binh, Yusheng Ji. Multi-time-step Segment Routing based Traffic Engineering Leveraging Traffic Prediction. IFIP/IEEE International Symposium on Integrated Network Management. 1-8. France. 17/05/2021
  5. Ta Bao Thang, Nguyen Binh Long, Ngo Viet Hoang, Huynh Thi Thanh Binh. Adaptive Knowledge Transfer in Multifactorial Evolutionary Algorithm for the Clustered Minimum Routing Cost Problem. Applied Soft Computing. 1-16. 27/02/2021
  6. Nguyễn Thị Tâm, Vũ Đình Hoàng, Huỳnh Thị Thanh Bình, Lê Trọng Vĩnh. Multi-objective teaching-learning evolutionary algorithm for enhancing sensor network coverage and lifetime. Engineering Applications of Artificial Intelligence. 1-6. 08/11/2021
  7. Bing Chen, Zhang Jiale, Chen Bing, Cheng Xiang, Huynh Thi Thanh Binh, Yu, Shui. PoisonGAN: Generative Poisoning Attacks against Federated Learning in Edge Computing Systems. IEEE Internet of Things Journal. 3310-3322. 02/09/2020
  8. Ban Hà Bằng. A metaheuristic for the Deliveryman Problem with Time Windows. J. Combinatorial Optimization. 794–816. 12/03/2021
  9. Ban Hà Bằng, Nguyễn Khánh Phương. A hybrid metaheuristic for Solving Asymmetric Distance-Constrained Vehicle Routing Problem. J. Computational Social. 1-19. 21/12/2020
  10. Ban Hà Bằng. A Metaheuristic for the Multiple Minimum Latency Problem with the Min-Max Objective. International Journal of Operational Research. 15/03/2021
  11. Nguyen Thi Tam, Vi Thanh Dat, Phan Ngoc Lan, Huynh Thi Thanh Binh, Le Trong Vinh, Ananthram Swami. Multifactorial evolutionary optimization to maximize lifetime of wireless sensor networkInformation Sciences. Information Sciences. 355-373. 17/06/2021
  12. Viet An Nguyen, Viet Hung, Van Sang Doan, Thanh Hung Nguyen, Phan Thuan Do, Kien Nguyen, Phi Le Nguyen, Minh Thuy Le. Realizing Mobile Air Quality Monitoring System: Architectural Concept and Device Prototype. 2021 26th IEEE Asia-Pacific Conference on Communications (APCC). 11/10/2021
  13. Tran Thi Huong, Le Van Cuong, Nguyen Bao Ngoc, Ngo Minh Hai, Huynh Thi Thanh Binh. Effective partial charging scheme for minimizing the energy depletion and charging cost in wireless rechargeable sensor networks. IEEE Congress on Evolutionary Computation. 1-8. Poland. 28/06/2021
  14. Do Bao Son, Vu Tri An, Trinh Thu Hai, Binh Minh Nguyen, Phi Le Nguyen, Huynh Thi Thanh Binh. Fuzzy Deep Q-learning Task Offloading in Delay Constrained Vehicular Fog Computing. 2021 International Joint Conference on Neural Networks (IJCNN). 18/07/2021
  15. Viet Dung Nguyen ; Ba Thai Pham ; Phan Thuan Do. Efficient algorithms for maximum induced matching problem in permutation and trapezoid graphs. Fundamenta Informaticae. 257-283. 22/07/2021
  16. Do Tuan Anh, Nguyen Hoang Long, Ta Bao Thang, Huynh Thi Thanh Binh, Simon Su. A two-level strategy based on evolutionary algorithm to solve the inter-domain path computation under node-defined domain uniqueness constraint. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III. 1-8. USA. 12/04/2021
  17. Tran Thi Huong, Le Van Cuong, Ngo Minh Hai, Nguyen Phi Le, Le Trong Vinh, Huynh Thi Thanh Binh. A bi-level optimized charging algorithm for energy depletion avoidance in wireless rechargeable sensor networks. Applied Intelligence. 1-23. 16/08/2021
  18. Phan Thuan Do, Thi Thu Huong Tran, Vincent Vajnovszki. The equidistribution of some Mahonian statistics over permutations avoiding a pattern of length three. Discrete Mathematics. 112684. 09/10/2021
  19. Tran Cong Dao, Tran Huy Hung, Nguyen Thi Tam, Huynh Thi Thanh Binh. A multifactorial evolutionary algorithm for minimum energy cost data aggregation tree in wireless sensor networks. IEEE Congress on Evolutionary Computation. 1-8. Poland. 28/06/2021
  20. Quang Ngoc Nguyen, Nghia Nguyen Duc, Phuong Khanh Nguyen. Two-echelon Multi-trip Multi-traffic Pickup and Delivery with Time Windows and Synchronization. Journal of Science and Technology. 25-32. 01/05/2021
  21. Le Van Cuong, Tran Thi Huong, Huynh Thi Thanh Binh. A multi-task approach for maximum survival ratio problem in large-scale wireless rechargeable sensor networks. IEEE Congress on Evolutionary Computation. 1-8. Poland. 28/06/2021
  22. Phan Thi Hong Hanh, Pham Dinh Thanh, Binh Huynh Thi Thanh. Evolutionary Algorithm and Multifactorial Evolutionary Algorithm on Clustered Shortest-Path Tree problem. Information Sciences. 280-304. 12/10/2020
  23. Ban Hà Bằng. Penalty Variable Neighborhood Search for the Bounded Single-Depot Multiple Traveling Repairmen Problem. J. Informatica. 93–104. 28/12/2020
  24. Van An Le, Tien Thanh Le, Phi Le Nguyen, Huynh Thi Thanh Binh, Rajendra Akerkarx, Yusheng Ji. GCRINT: Network Traffic Imputation Using Graph Convolutional Recurrent Neural Network. IEEE International Conference on Communications. 1-8. Canada. 14/06/2021
  25. Nguyen Thi Tam, Tran Huy Hung, Huynh Thi Thanh Binh, Le Trong Vinh. A decomposition-based multi-objective optimization approach for balancing the energy consumption of wireless sensor networks.. Applied Soft Computing. 1-14. 25/03/2021
  26. Ban Hà Bằng. Variable Neighborhood Search based Algorithm to Solve the Minimum Back-Walk-Free Latency Problem. International Journal of Computer Applications in Technology. 55-64. 30/05/2020
  27. Huynh Thi Thanh Binh, Ta Bao Thang, Nguyen Duc Thai, Pham Dinh Thanh. A bi-level encoding scheme for the clustered shortest-path tree problem in multifactorial optimization. Engineering Applications of Artificial Intelligence. 1-14. 29/01/2021
  28. Van Son Nguyen, Quang Dung Pham, and Van Hieu Nguyen. Exploiting Demand Prediction to Reduce Idling Travel Distance for Online Taxi Scheduling Problem. 4th international conference on “Modelling, Computation and Optimization in Information Systems and Management Sciences” MCO 2021. 51-62. Hanoi, Vietnam. 13/12/2021
  29. Ta Bao Thang, Tran Cong Dao, Nguyen Hoang Long, Huynh Thi Thanh Binh. Parameter adaptation in multifactorial evolutionary algorithm for many-task optimization. Memetic computing. 433-446. 04/09/2021
  30. Anh Son TA. Sovling problem. NICS. 17/01/2021
  31. Nguyen Thieu; Nguyen Thang; Vu Quoc Hien; Huynh Thi Thanh Binh; and Nguyen Binh Minh*. Multi-objective Sparrow Search Optimization for Task Scheduling in Fog-Cloud-Blockchain Systems. International Conference on Services Computing (IEEE SCC 2021). 450-455. Chicago, USA. 05/09/2021
  32. Manh Hung Dinh, Ngoc Thach Hoang, Mai Phuong Nguyen, Phi Le Nguyen, Phan Thuan Do. Node Deployment Optimization for Target Coverage and Connectivity in WSNs with a Delay-constrained Mobile Sink. ICCE 2020. Phu Quoc Island. 13/01/2021
  33. Tien Thanh Le, Van Cuong Le, Bao Thang Ta, Huynh Thi Thanh Binh. Multi-Armed Bandits for Many-task Evolutionary Optimization. IEEE Congress on Evolutionary Computation. 1-8. Poland. 28/06/2021