Dr Parminder Singh Kang

Job: Research Fellow

Faculty: Technology

School/department: School of Engineering and Sustainable Development

Research group(s): Lean Engineering Research Group, Advanced Manufacturing Processes and Mechatronics Centre

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

T: +44 (0)116 207 8089

E: pkang@dmu.ac.uk

W: uk.linkedin.com/pub/parminder-singh-kang-phd-m-sc-b-tech-miet/46/74b/96/


Personal profile

Dr Parminder Singh Kang is working as a Research Fellow at De Montfort University (DMU), Leicester, UK. His main responsibilities are providing the academic lead and day to day management of externally funded research projects. Dr Kang received B-Tech degree (Computer Science and Engineering) in 2006 form Punjab Technical University, India. He received M.Sc. degree (IT) in 2008 and completed his PhD (Improving Manufacturing Systems Using Integrated Discrete Event Simulation and Evolutionary Algorithms) in Manufacturing Science in 2012 at DMU, UK. He is member of institution of mechanical engineers (IMechE), Institution of Engineering and Technology (IET) and The American Society of Mechanical Engineers (ASME). He has over 5 years of experience working on externally funded collaborative industrial R&D cross-disciplinary project in the area of organizational operations optimization. His research interests are; evolutionary algorithms, combinatorial optimization, simulation modelling, Lean/Six Sigma, autonomous decision making and application of these techniques (integrated approaches) in industrial/service process improvement and operations optimization.

Having educational background and experience in the area of computer science and engineering and manufacturing science Dr Kang’s key competencies are in the area of;

Operations Management and Manufacturing Science

- Lean Tools and Techniques, Designing and Modelling (Mathematical and Simulation), Process Improvement, Lead Time Reduction, Cost Reduction, Setup reduction, buffer sizing, Bottleneck Detection and Analysis,    Sequencing, Scheduling (finite capacity scheduling) and Buffer Management, Multi-Objective Combinatorial Optimization, Evolutionary Algorithms, Autonomous planning, scheduling and decision making, Problem Solving and Root Cause Analysis.

- Experience working with simulation packages (Simul8, Simio and Arena) and Finite Capacity Scheduling (Preactor).

Computer Science and Engineering;

Knowledge and experienced in R&D software development for large collaborative research projects, development of Evolutionary Algorithms, Artificial Intelligence Techniques and Autonomous Systems development and using these techniques in process improvement and optimization, algorithm and data structure design for complex systems.

- Competent in a number of Programming Languages; C/C++, C#, Visual Basic and Java, Handling .COM objects, Advanced multithreading, advanced shell scripting and data base design and management skills.

Publications and outputs 

Click here to view a full listing of Dr Parminder Kang's publications and outputs.

Research interests/expertise

  • Application of Lean manufacturing techniques to high variety/low volume manufacturing environments and services operations.
  • Evolutionary algorithms based combinatorial optimization in Industrial/Service process improvement.
  • Applications of simulation and mathematical modelling.
  • Interdisciplinary research to provide solution for real world engineering problems; Evolutionary Algorithms, advanced database, Simulation and ICT approaches to solve real world problems; such as job sequencing, batch size optimization, planning and scheduling, buffer management.
  • Application of autonomous systems in manufacturing operations planning and scheduling.
  • Root Cause analysis based problem solving combined with multi-objective evolutionary optimization, especially in the highly variable environments.
  • Operations planning within industrial and service organizations, improving process competencies and autonomous decision making.
  • Automatic and optimum design of operational systems with particular emphasis on highly variable environments.
  • Application of my distinctive knowledge in a wide range of industrial sectors to address problems such as; theatre, healthcare, creative product design, manufacturing, logistics, distribution and materials processing.


  • Ph.D. in Manufacturing Science
  • M.Sc. Information Technology (Distinction)
  • B-Tech Computer Science and Engineering

Courses taught

  • ENGT5103 – Engineering Business Environment & Energy Studies
  • CSCI 1412 Computer Technology
  • TECH 2018 Multimedia and the Internet
  • TECH 1001 Electronic Production and Publishing
  • IMAT 5122 Computer Systems and Networks

Honours and awards

  • Preactor User Group Meeting Presentation; 18th June 2015; Topic: Autonomous Production Planning and Scheduling 
  • Presenter at 2014 Preactor User Group Meeting; 18 – 19th June 2014 (http://www.preactor.com/Home.aspx) Topic: Autonomous scheduling for industrial applications; http://www.reactivescheduling.com 
  • June 2009 – June 2012 -  De Montfort University, Leicester, UK -  Ph.D. Bursary Student
  • March 2012 -  De Montfort University, Leicester, UK – Laxton Travel Funding Award

Membership of professional associations and societies

  • Member of American Society of Mechanical  Engineers [ASME]
  • Member of Institution of Mechanical Engineers [MIMechE]
  • Member of Institution of Engineering and Technology [MIET]

Externally funded research grants information


Project Title: Development of an Innovative Autonomous & Intelligent Demand Management Tool for Refrigerated and Shelf-Life Constrained Food Supply Chains (AIDMT)
Project Type: Collaborative R&D, 23792-161273
Role: Researcher/Day-to-Day Management

Project Title: Development of an Innovative Autonomous Model Development Tool for boosting Manufacturing Process Competencies (AMDT)
Project Type: Collaborative R&D, 18834-132285
Role: Researcher/Day-to-Day Management


Project Title: Development of an Autonomous Systems Development Tool for application within Manufacturing Operations-Planning (ASDT)
Project Type: Collaborative R&D, 5908-45002
Role: Researcher and  Developer/Day-to-Day Management

Project Title: Achieving vertically-integrated carbon-fibre reinforcement design and manufacture demonstrators for structural manufacturing and construction 3-D composites (AVISC)
Project Type: Collaborative R&D, 13451-87159
Role: Researcher/Day-to-Day Management

Project Title: Development of Metadata-based Animation & Art Production Improvement Tool (MAAPIT)
Project Type: Collaborative R&D, 12160-76206
Role: Researcher/Day-to-Day Management

Project Title: Reducing Road Freight Empty Running (REFER) - R&D, TSB CB157C
Role: Researcher

Project Title: Accelerated Process Excellence using Virtual Discreet Event Simulation (VDEPS)
Project Type: Collaborative R&D, K1532G
Role: Researcher/ Programmer  

Project Title: Reducing Theatre Production Cost and Lead Times using Advanced Technology
Project Type: Collaborative R&D,AJ151K
Role: Researcher/ Programmer

Project Title: Improving Customer Demand and Cost Forecasting Methods
Project Type: Collaborative R&D, H0254E
Role: Programmer

Professional esteem indicators

  • Member of XI International Conference Committee on Computational Complexity and Intelligent Algorithms (ICCCIA 2014)
  • Member of The IEEE International Conference reviewing  committee on Industrial Engineering and Engineering Management (IEEM)
  • Reviewer of European journal of Operational Research, Journals of Computers and Operations Management and International Journal of Production Research.
  • Presenter at 2014 Preactor User Group Meeting; 18 – 19th June 2014 (http://www.preactor.com/Home.aspx) Topic: Autonomous scheduling for industrial applications
  • Preactor User Group Meeting Presentation; 18th June 2015; Topic: Autonomous Production Planning and Scheduling 

Case studies

1. Autonomous Finite Capacity Scheduling (AutoPlan) - DMU research boosts productivity for one of the world's biggest steel companies:

See more at: http://www.dmu.ac.uk/about-dmu/news/2015/february/dmu-research-boosts-productivity-for-one-of-the-worlds-biggest-steel-companies.aspx



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