Professor Shengxiang Yang

Job: Professor of Computational Intelligence, Director of the Centre for Computational Intelligence (CCI)

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH UK

T: +44 (0)116 207 8805

E: syang@dmu.ac.uk

W: www.tech.dmu.ac.uk/~syang/

 

Personal profile

Shengxiang Yang is Professor of Computational Intelligence and Director of the Centre of Computational Intelligence (CCI), De Montfort University. Before joining the CCI in July 2012, he worked at Brunel University, University of Leicester, and King's College London as a Senior Lecturer, Lecturer, and Post-doctoral Research Associate, respectively.

Shengxiang's main research interests lie in evolutionary computation. He is particularly active in the area of evolutionary computation in dynamic and uncertain environments. Shengxiang has also published on the application of evolutionary computation in communication networks, logistics, transportation systems, and manufacturing systems, etc.

Research group affiliations

Centre for Computational Intelligence

Publications and outputs 

  • Solving dynamic multi-objective problems with an evolutionary multi-directional search approach
    Solving dynamic multi-objective problems with an evolutionary multi-directional search approach Hu, Yaru; Ou, Junwei; Zheng, Jinhua; Zou, Juan; Yang, Shengxiang; Ruan, Gan The challenge of solving dynamic multi-objective optimization problems is to effectively and efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this paper proposes a multi-direction search strategy, aimed at finding the dynamic Pareto optimal front and/or Pareto optimal set as quickly and accurately as possible before the next environmental change occurs. The proposed method adopts a multi-directional search approach which mainly includes two parts: an improved local search and a global search. The first part uses individuals from the current population to produce solutions along each decision variable’s direction within a certain range and updates the population using the generated solutions. As a result, the first strategy enhances the convergence of the population. In part two, individuals are generated in a specific random method along every dimension’s orientation in the decision variable space, so as to achieve good diversity as well as guarantee the avoidance of local optimal solutions. The proposed algorithm is measured on several benchmark test suites with various dynamic characteristics and different difficulties. Experimental results show that this algorithm is very competitive in dealing with dynamic multi-objective optimization problems when compared with four state-of-the-art approaches. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals
    An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals Wang, Zhen; Zhang, Jihui; Yang, Shengxiang Random job arrivals that happen frequently in manufacturing practice may create a need for dynamic scheduling. This paper considers an issue of how to reschedule the randomly arrived new jobs to pursue both performance and stability in a job shop. Firstly, a mixed integer programming model is established to minimize three objectives, including the discontinuity rate of new jobs during the processing, the makespan deviation of initial schedule, and the sequence deviation on machines. Secondly, four match-up strategies from references are modified to determine the rescheduling horizon. Once new jobs arrive, the rescheduling process is immediately triggered with ongoing operations remain. The ongoing operations are treated as machine unavailable constraints (MUC) in the rescheduling horizon. Then, a particle swarm optimization (PSO) algorithm with improvements is proposed to solve the dynamic job shop scheduling problem. Improvement strategies consist of a modified decoding scheme considering MUC, a population initialization approach by designing a new transformation mechanism, and a novel particle movement method by introducing position changes and a random inertia weight. Lastly, extensive experiments are conducted on several instances. The experiments results show that the modified rescheduling strategies are statistically and significantly better than the compared strategies. Moreover, comparative studies with five variants of PSO algorithm and three state-of-the-art meta-heuristics demonstrate the high performance of the improved PSO algorithm. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem
    Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem Mavrovouniotis, Michalis; Yang, Shengxiang; Van, Mien; Li, Changhe; Marios, Polycarpou Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic travelling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Constrained operation optimization of a distillation unit in refineries with varying feedstock properties.
    Constrained operation optimization of a distillation unit in refineries with varying feedstock properties. Chen, Qingda; Ding, Jinliang; Yang, Shengxiang; Chai, Tianyou This paper studies the challenging operational optimization problem of a distillation unit under varying feedstock properties (e.g., density and carbon content). This problem, in which changes in the feedstock properties are incorporated, aims to quickly obtain the operating variables that control the operating condition of the distillation unit. To solve this problem, we first model this operational optimization problem considering the ever-changing feedstock properties and practical technological constraints. Then, we propose an efficient soft-sensing strategy to rapidly measure the feedstock properties. Finally, motivated by the challenges caused by the varying feedstock properties, product yield and tray temperature constraints, we propose an optimization algorithm with global search and self-repair capabilities to optimize the operating variables of the distillation unit. The proposed algorithm integrates the optimization time and survival information of each individual into the proposed mutation strategy to improve its global search capability in the irregular feasible region of the operating variables. Based on the ranking and survival information of each individual, the adaptive strategies of the mutation factor and crossover probability are designed to balance the exploration and exploitation capabilities of the optimization algorithm. Subsequently, we propose an effective correction strategy to correct the infeasible operating variables and improve the optimization efficiency of the algorithm. Computational experiments on practical production data show the accuracy of the soft-sensing model and the superiority of the optimization algorithm for operational optimization of the distillation unit. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization
    A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization Ou, Junwei; Zheng, Jinhua; Ruan, Gan; Hu, Yaru; Zou, Juan; Li, Miqing; Yang, Shengxiang; Tan, Xu Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added by another difficulty of tracking the Pareto optimal solutions set (POS) and/or the Pareto optimal front (POF) in dynamic scenarios. Confronting these two issues, this paper proposes a Pareto-based evolutionary algorithm using decomposition and truncation to address such dynamic multi-objective optimization problems (DMOPs). The proposed algorithm includes three contributions: a novel mating selection strategy, an efficient environmental selection technique and an effective dynamic response mechanism. The mating selection considers the decomposition-based method to select two promising mating parents with good diversity and convergence. The environmental selection presents a modified truncation method to preserve good diversity. The dynamic response mechanism is evoked to produce some solutions with good diversity and convergence whenever an environmental change is detected. In the experimental studies, a range of dynamic multi-objective benchmark problems with different characteristics were carried out to evaluate the performance of the proposed method. The experimental results demonstrate that the method is very competitive in terms of convergence and diversity, as well as in response speed to the changes, when compared with six other state-of-the-art methods. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection
    An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection Qiao, Junfei; Li, Fei; Yang, Shengxiang; Yang, Cuili; Li, Wenjing; Gu, Ke In general, for the iteration process of an evolutionary algorithm (EA), there exists the problem of uneven distribution of individuals in the target space for both multi-objective and single-objective optimization problems. This uneven distribution significantly degrades the population diversity and convergence speed. This paper proposes an adaptive hybrid evolutionary immune algorithm based on a uniform distribution selection mechanism (AUDHEIA) for solving MOPs efficiently. In AUDHEIA, the individuals in the population are mapped to a hyperplane, which is correlated with the objective space and are clustered to increase the diversity of solutions. To improve the distribution of the solutions, the mapped hyperplane is evenly sectioned. With the constantly changing distribution during the iteration, a threshold as a standard for judging the distribution level is adjusted adaptively. When the threshold is not satisfied in the corresponding interval, the distribution enhancement module is activated. Then, the same number of individuals should be selected in each interval. However, sometimes, there are insufficient or no individuals in the interval during the iterative process. To obtain sufficient individuals, the limit optimization variation strategy of the best individual is adopted. Experiments show that this algorithm can escape from local optima and has a high convergence speed. Moreover, the distribution and convergence of this algorithm are superior to the peer algorithms tested in this paper. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons
    Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons Wang, Yong; Yu, Jian; Yang, Shengxiang; Jiang, Shouyong; Zhao, Shuang Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Novel prediction strategies for dynamic multi-objective optimization
    Novel prediction strategies for dynamic multi-objective optimization Zhang, Qingyang; Yang, Shengxiang; Jiang, Shouyong; Wang, Ronggui; Li, Xiaoli This paper proposes a new prediction-based dynamic multi-objective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. Whenever a change is detected, PBDMO reacts effectively to it by generating three sub-populations based on different strategies. The first sub-population is created by moving non-dominated individuals using a simple linear prediction model with different step sizes. The second sub-population consists of some individuals generated by a novel sampling strategy to improve population convergence as well as distribution. The third sub-population comprises some individuals generated using a shrinking strategy based on the probability distribution of variables. These sub-populations are tailored to form a population for the new environment. Experimental results carried out on a variety of bi- and three-objective benchmark functions demonstrate that the proposed technique has competitive performance compared with some state-of-the-art algorithms. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A two-layer optimisation management method for the microgrid with electric vehicles
    A two-layer optimisation management method for the microgrid with electric vehicles Zheng, Zedong; Yang, Shengxiang The energy management of the microgrid (MG) with electric vehicles (EVs) is a large-scale optimization problem where the goal should take into account the performance and economic benefits of the power system while meeting the travel needs of EVs. Due to the development of vehicle to grid (V2G) technologies and demand response (DR), the relationship between EVs and MG becomes currently closer, which leads to a more complex situation. Therefore, the relationship of interest between MG and EVs has to be clarified to improve the performance of MG and EVs to achieve a win-win situation. This paper proposes a two-tier energy management strategy that considers the benefits for both MG and EVs. The first layer ensures the performance of the MG, while the second layer reduces the charging cost from the perspective of the car owners. In addition, based on the existence of uncertain parameters, mixed type variables and nonlinear constraints in the optimization problem, the differential evolution, stochastic search and greedy algorithm are used to analyze and find the optimal solution. Simulation results verify the effectiveness of the proposed strategy and solutions, which benefit both the MG and EV owners. The file attached to this record is the author's final peer reviewed version.
  • Memory-based multi-population genetic learning for dynamic shortest path problems
    Memory-based multi-population genetic learning for dynamic shortest path problems Diao, Yiya; Li, Changhe; Zeng, Sanyou; Mavrovouniotis, Michalis; Yang, Shengxiang This paper proposes a general algorithm framework for solving dynamic sequence optimization problems (DSOPs). The framework adapts a novel genetic learning (GL) algorithm to dynamic environments via a clustering-based multi-population strategy with a memory scheme, namely, multi-population GL (MPGL). The framework is instantiated for a 3D dynamic shortest path problem, which is developed in this paper. Experimental comparison studies show that MPGL is able to quickly adapt to new environments and it outperforms several ant colony optimization variants. The file attached to this record is the author's final peer reviewed version.

Click here to view a full listing of Shengxiang Yang's publications and outputs.

Research interests/expertise

  • Evolutionary Computation

  • Swarm Intelligence

  • Meta-heuristics

  • Dynamic Optimisation Problems

  • Multi-objective Optimisation Problems

  • Relevant Real-World Applications

Areas of teaching

Research Methods for Intelligent Systems and Robotics MSc, Software Engineering MSc, Computing MSc, and Business Intelligence Systems and Data Mining MSc Degrees.

Qualifications

BSc in Automatic Control, Northeastern University, China (1993)

MSc in Automatic Control, Northeastern University, China (1996)

PhD in Systems Engineering Northeastern University, China (1999)

Courses taught

I have taught numerous modules at both undergraduate and postgraduate level. Quite a number of modules I taught were significantly developed by myself. The modules I taught are usually designed to be practice-oriented with problem-solving lab sessions based on Java or C++ programming, and hence are highly interesting to and greatly useful for students. They are also very important for different degree programmes in Computer Science and relevant subjects. Some of the modules I have taught are listed as follows:

  • CS3002 Artificial Intelligence (2010 – 2012, Brunel University): 3rd year Computer Science (Artificial Intelligence) BSc module, module leader

  • CS2005 Networks and Operating Systems (2010 – 2012, Brunel University): 2nd year Network Computing BSc module, part module

  • CS5518 Business Integration (2011-2012, Brunel University): Business Systems Integration MSc module, part module

  • CO2017 Networks and Distributed Systems (2005–2010, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO2005 Object-Oriented Programming Using C++ (2006–2009, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO1003 Program Design (2006-2007, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO3097 Programming Secure and Distributed Systems (2003–2005, University of Leicester): 3rd year Computer Science BSc & Advanced Computer Science MSc module, module leader

  • CO1017 Operating Systems and Networks (2001 – 2004, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO1016 Computer Systems (2000 – 2002, University of Leicester): 1st year Computer Science BSc module, part module

I have also co-ordinated several BSc projects, as shown below.

  • CS3072/CS3074/CS3105/CS3109 BSc Final Year Projects (2010 – 2012, Brunel University): Co-ordination Team Member

  • CO3012/CO3013/CO3015 Computer Science BSc Final Year Projects (2004 – 2010, University of Leicester): Co-ordinator

  • CO3120 Computer Science with Management BSc Final Year Project (2007 – 2010, University of Leicester): Co-ordinator

  • CO3014 Mathematics and Computer Science BSc Final Year Project (2004 – 2010, University of Leicester): Co-ordinator

  • CO2015 Second Year BSc Software Engineering Project (2003 – 2004, University of Leicester): Co-ordinator

Honours and awards

  • Nominatee to the Best Paper Award for EvoApplications 2016: Applications of Evolutionary Computation, for the paper "Direct memory schemes for population-based incremental learning in cyclically changing environments" by Michalis Mavrovouniotis and Shengxiang Yang, published in EvoApplications 2016: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 9598, pp. 233-247, 2016.

  • Nominatee for the Best-Paper Award of the ACO-SI Track at the 2015 Genetic and Evolutionary Computation Conference, for the paper "An ant colony optimization based memetic algorithm for the dynamic travelling salesman problem" by Michalis Mavrovouniotis, Felipe Martins Muller and Shengxiang Yang, published in the Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation, pp. 49-56, 2015.

  • Winner of the 2014 IEEE Congress on Evolutionary Computation Best Student Paper Award, for the paper entitled "A test problem for visual investigation of high-dimensional multi-objective search" by Miqing Li, Shengxiang Yang and Xiaohui Liu, published in the Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 2140-2147, 2014.

  • Nominatee for the 2005 Genetic and Evolutionary Computation Conference Best Paper Award, for the paper "Memory-based immigrants for genetic algorithms in dynamic environments" by Shengxiang Yang, published in the Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 2, pp. 1115-1122, 2005.

  • Visiting Professor (2012 – 2014, 2016-2018), College of Information Engineering, Xiangtan University, China

  • Visiting Professor (2011 – 2017), College of Mathematics and Statistics, Nanjing University of Information Science and Technology, China

Membership of professional associations and societies

  • Founding Chair, Task Force on Intelligent Network Systems (TF-INS), Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), 2012–2018.

  • Chair, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2011–2018.

  • Senior Member, IEEE, since 2014.

  • Member, IEEE, 2000 – 2013.

  • Member, IEEE Computational Intelligence Society (IEEE CIS), since 2005.

  • Member, Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), since 2011.

  • Member, Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), since 2013.

  • Member, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2003 – 2010.

Current research students

First Supervisor:

  • Muhanad Tahrir Younis: Swarm intelligence for dynamic job scheduling in grid computing, started from October 2014

  • Conor Fahy: Evolutionary computation for data stream analysis, started from October 2015

  • Zedong Zheng: started from October 2016
  • Matthew Fox: started from October 2017

Second Supervisor:

  • Ahad Arshad: PhD candidate, co-supervised with Prof. Paul Fleming at De Montfort University, started in October 2017.
  • William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at De Montfort University, started in April 2012

Complete PhD Students (I was the 1st Supervisor):

  • Changhe Li: Particle swarm optimisation in stationary and dynamic environments, 2011

  • Imtiaz Ali Korejo: Adaptive mutation operators for evolutionary algorithms, 2011

  • Sadaf Naseem Jat: Genetic algorithms for university course timetabling problems, 2012

  • Shakeel Arshad: Sequence based memetic algorithms for static and dynamic travelling salesman problems, 2012

  • Michalis Mavrovouniotis: Ant Colony Optimization in Stationary and Dynamic Environments, 2013

  •  Miqing Li: Evolutionary Many-Objective Optimization: Pushing the Boundaries, 2015
  • Jayne Eaton: Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems, 2017
  • Shouyong Jiang: Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization, 2017

Externally funded research grants information

  • EU Horizon 2020 Marie Sklodowska-Curie Individual Fellowships (PI, Project ID: 661327, 09/2015-08/2017, €195,455): Evolutionary Computation for Dynamic Constrained Optimization Problems (ECDCOP)
  • EPSRC (PI, Standard Research Project, EP/K001310/1, 18/2/2013-17/02/2017, £445,069): Evolutionary Computation for Dynamic Optimisation in Network Environments

  • EPSRC (PI, Standard Research Project, EP/E060722/1 and EP/E060722/2, 1/1/2008-1/7/2011, £307,469): Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications

  • EPSRC (PI, Overseas Travel Grants GR/S79718/01, 1/11/2003-31/1/2004, £6,700): Adaptive and Hybrid Genetic Algorithms for Production Scheduling Problems in Manufacturing. This grant supported my research visit to Waseda University, Japan, during my Sabbatical leave period. Additionally, Waseda University, Japan contributed JPY140,000 (~£800) toward the visit

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2012-31/12/2013, CNY300,000 (~£30,000)): Evolutionary Computation for Dynamic Scheduling Problems in Process Industries

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2010-31/12/2011, CNY150,000 (~£15,000)): Evolutionary Computation for Dynamic Optimization and Scheduling Problems

  • Transport iNet, European Regional Development Fund (Co-I, 11/11/2013 - 28/02/2015, £62,134), Evolutionary Computation for Optimised Rail Travel (EsCORT). This is a linked project between De Montfort University and Go Travel Solutions, a Leicester based SME specialising in assisting businesses to develop sustainable travel solutions, covering people and goods.
  • Hong Kong Polytechnic University Research Grants (Co-I, Grant G-YH60, 1/7/2009-30/6/2010, HKD120,000 (~£10,000)): Improved Evolutionary Algorithms with Primal-Dual Population for Dynamic Variation in Production Systems. Partners:

In addition, I have also received several conference travel grants from UK Research Councils, e.g., Royal Society Conference Travel Grant (£700 in 2007 and £719 in 2005) and Royal Academy of Engineering Conference Grant (£800 in 2007 and £1,200 in 2006).

Internally funded research project information

  • De Montfort University Higher Education Innovation Fund (HEIF) 2017-18 (Co-I, 01/12/2017-31/07/2018, £14,000): Brian-Computer-Interface Prototyping System: Data-based Filtering and Dynamic Characterisation.
  • De Montfort University Higher Education Innovation Fund (HEIF) 2015-16 (PI, 01/01/2016-31/07/2016, £24,800): Development of a Dynamic Resource Scheduling Prototype System for Airports.

  • De Montfort University PhD Studentships 2017-18 (PI, 1/10/2017–30/09/2020, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University Fee Waiver PhD Scholarships 2016-17 (PI, 1/10/2016–30/09/2019, approximately £40,000): supporting fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2015-16 (PI, 1/10/2015–30/09/2018, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/10/2013–30/09/2016, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/4/2013–31/03/2016, approximately £60,000): supporting stipend and fees for one home PhD student for three years

  • Brunel University PhD Studentships 2011-12 (PI, 01/10/2011–30/09/2014, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • University of Leicester PhD Studentships 2008-09 (PI, 1/10/2008–30/9/2011, approximately £50,000): supporting stipend and fees for one PhD student for three years

  • University of Leicester Research Fund 2001 (PI, 1/1/2001- 31/12/2001, £3,200): Using Neural Network and Genetic Algorithm Methods for Job-Shop Scheduling Problem.

Professional esteem indicators

Shengxiang-Yang

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