- Optimization, Evolutionary Multi-objective Optimization, Multi-Criteria Decision Making, Big Data Analytics
|April 2016||Present||Associate Professor||Department of Mechanical & Industrial Engineering, IIT Roorkee|
|Nov. 2012||April 2016||Assistant Professor||Department of Mechanical & Industrial Engineering, IIT Roorkee|
|April 2012||August2012||Liverhume Research Fellow||Department of Computer Science, Bath University, UK|
|May 2008||April 2012||Academic Fellow||Manufacturing Department, Cranfield University, UK|
|MCDM Doctoral Award Finalist (one of the top 3 Ph.Ds internationally during 5 years period (2007-11)||Cranfield University||2011|
|PhD||Evolutionary Many-objective Optimization||IIT Kanpur||2008|
|2016||-||Associate Editor||Elsevier's Swarm and Evolutionary Computation|
|Aug. 2014||May, 2017||Chief Warden KIH||IIT Roorkee|
|Jan 2015||May 2017||Member: Guest House Advisory Committee||IIT Roorkee|
|July 2015||May 2017||Member: Department Purchase Committee, under DOSW set-up||IIT Roorkee|
|July 2015||May 2017||Co-ordinator Tinkering Lab||IIT Roorkee|
|INNOVIZATION: Discovery of Innovative Knowledge through Optimization and Machine Learning||MHRD, MSU (USA), GE (105.64 Lakhs)||2019|
|A Systems Approach towards Data Mining and Prediction in Airlines operations [PI]||DeiTY-NWO-GE (483,000 Euro)||2015|
|Decomposition Based Multiobjective Evolutionary Computation [Overseas-CI]||NSF, China (800,000 RMB)||2015|
|Multi-objective Optimization of Composite Aircraft Wing.||Airbus, UK||2009|
|Tinkering Laboratory [Co-ordinator]||MHRD (5.1 crore INR: 770,000 USD)||2015|
|Many-objective Optimization: A way forward||Hewlett Packard, UK||2008|
- IEEE, Member
- Elsevier: Swarm and Evolutionary Computation Journal, Associate Editor
|Title of Project||Names of Students|
|Evolutionary Multi-objective Optimisation from a System Design Perspective.||Alessandro Rubino|
|Optimisation of Composite Aircraft Wing||Benjamin Bruner|
|Many-objective optimization: A way forward.||Wu Qin|
|Weighted Diversity Measure to Improve Convergence in a Class of Many-objective Optimization Problems||Himansu Sekhar Dash|
|Feature based Optimal Sensor Position for Fault Diagnosis in Rolling Element Bearing||Praveen Nagesh|
|Application of Axiomatic Design Principles for Weight Optimization of Automotive Chassis||Ishwar Keshav Yanganti|
|Topic||Scholar Name||Status of PHD||Registration Year|
|INNOVIZATION: Discovery of Innovative Knowledge through Optimization and Machine Learning||Sukrit Mittal||O||2018|
|Airline Scheduling under Uncertainty||Divyam Agarwal||O||2015|
|Feature construction by means of pattern mining on Big Data for Airline Operations||Sarang Kapoor||O||2015|
|Machine Learning based Decision Support for a Class of Many-objective Optimization Problems||Joao A Duro||A||2009|
|Couse Name||Sponsored By||Date|
|Designed & Conducted: Multidisciplinary Optimization: From Theory to Practice||Cranfield University & EnginSoft, UK||2010&2011|
|Optimisation of Composite Aircraft Wing.||Airbus, UK||PG|
|Many-objective Optimization: A Way Forward||Hewlett Packard, UK||PG|
 On Timing the Nadir-Point Estimation and/or Termination of Reference-Based Multi- and Many-objective Evolutionary Algorithms; D. K. Saxena and Sarang Kapoor; Evolutionary Multi-Criterion Optimization, 191-202, 2019.
 Timing the Decision Support for Real-World Many-Objective Optimization Problems; J. A Duro, D. K. Saxena; Evolutionary Multi-Criterion Optimization, 191-205, 2017.
 Entropy based Termination Criterion for Multiobjective Evolutionary Optimisation; D. K. Saxena, Arnab Sinha, J. A. Duro and Q. Zhang; IEEE Transactions on Evolutionary Computation, 20 (4), 485-498, 2016 Code
 Machine learning based decision support for many-objective optimization problems; J.A.Duro, D. K.Saxena, K.Deb and Q.Zhang; Neurocomputing, Volume 146, Pages 30–47. http://www.sciencedirect.com/science/article/pii/S0925231214008753
 Objective Reduction in Many-objective Optimization: Linear and Nonlinear Algorithms; D. K.Saxena, J.A.Duro, A.Tiwari, K.Deb and Q.Zhang; IEEE Transactions on Evolutionary Computation, 2012, 99, 1-23. Code
 An Evolutionary Multi-objective Framework for Business Process Optimization; K.Vergidis, D.K.Saxena and A.Tiwari; Applied Soft Computing, 2012, 2638-2653.
 Identifying the Redundant and Ranking the Critical Constraints in Practical Optimization Problems; D.K.Saxena, A.Rubino, J.A.Duro and A.Tiwari; Engineering Optimization, 2012, 1-23.
 Using Objective Reduction and Interactive Procedure to Handle Many-objective optimization Problems; A.Sinha, D.K.Saxena, K.Deb and A.Tiwari, Applied Soft Computing, 2013, 3(1), 415-427.
 Framework for Many-objective Test Problems with both Simple and Complicated Pareto-set Shapes; D.K.Saxena, Q.Zhang, J.A.Duro and A.Tiwari; Evolutionary Multi-Criterion optimization, 2011, 197-211.
 On Handling a Large Number of Objectives A Posteriori and During Optimization; D.Brockhoff, D.K.Saxena, K.Deb and E.Zitzler; Multi-objective Problem Solving from Nature, 2008, 4, 377-403.
 Non-linear Dimensionality Reduction Procedures for certain Large-dimensional Multi-objective Optimization Problems: Employing Correntropy and a Novel Maximum Variance Unfolding; D.K.Saxena and K.Deb; Evolutionary Multi-Criterion Optimization, 2007, 772-787.
Refereed Conference Papers
 Service Information in the Provision of Support Service Solutions: A State-of-the-art Review; S. Kundu, A. McKay, R. Cuthbert, D. McFarlane, D. K. Saxena, A. Tiwari and P. Johnson; CIRP Industrial Product-Service Systems; Cranfield, U.K, 2009, ISBN: 978-0-9557436-5-8, 100-106.
 Constrained many-objective optimization: A way forward; D. K. Saxena, T. Ray, K. Deb and A. Tiwari; IEEE Congress on Evolutionary Computation, Trondheim, Norway, 2009, ISBN:978-1-4244-2958-5, 545-552.
 Dimensionality Reduction of Objectives and Constraints in multi-objective optimization problems: A system design perspective; D. K. Saxena and K. Deb; IEEE Congress on Evolutionary Computation, Hongkong, 2008, ISBN:978-1-4244-1822-0, 3204-3211.
 Trading on infeasibility by exploiting constraint’s criticality through multi-objectivization: A system design perspective; D. K. Saxena and K. Deb; IEEE Congress on Evolutionary Computation, Singapore, ISBN:978-1-4244-1339-3, 919-926.
 Searching for Pareto-optimal Solutions through Dimensionality Reduction for Certain Large-dimensional Multi-Objective Optimization Problems; K. Deb and D.K.Saxena; IEEE Congress on Evolutionary Computation, Vancouvar, Canada, 2006, IEEE: 0-7803-9487-9, 3353-3360.
Deliverables to "British Aerospace Systems & Engineering and Physical Sciences Research Council, UK"
for the project: "S4T : Support Service Solutions: Strategy and Transition"
|1||Current state of service information||2008||31||5||University of - Leeds, Cranfield, & Cambridge, UK.|
|2||Service information requirements||2009||43||6||University of - Cranfield, Cambridge, & Leeds, UK.|
|3||Blueprint for future service information||2009||37||5||University of - Leeds, Cranfield, & Cambridge, UK.|
Industrial case studies
|2009||30||5||University of - Cranfield, Cambridge, & Leeds, UK.|
|5||A roadmap for the transition to future service information solutions||009||11||10||University of - Cambridge, Leeds, Cranfield, & BAES, UK.|
- Dhish obtained his Ph.D in Evolutionary Many-objective Optimization (2008), under the supervision of Shanti Swaroop Bhatnagar Awardee Prof. Kalyanmoy Deb, IIT Kanpur. In MCDM Conference, Finland, 2011, Dhish's Ph.d was adjudged as one of the three most impactful Ph.Ds in the world, during 2007-11, in the area of Evolutionary Multi-objective Optimization and Multi-criterion Decision Making. Dhish brings on board his work-experience in the United Kingdom, for almost half-a-decade, where he worked with universities like Cranfield and Bath, in collaboration with companies like British Aerospace Systems, Hewlett Packard, and Airbus. The focus of his research has been two fold. At a fundamental level, his research has focused on facilitating a better understanding of highly constrained practical optimization problems, characterized by high degree of non-linearity and several (many) conflicting objectives. In that, machine learning techniques have been integrated with evolutionary algorithms to rank the objectives and also the constraints by order of their importance, to facilitate a decision support for a given problem. At the applied level, his research focus has been on demonstrating the utility of the self-developed tools and techniques on a wide range of real-world: engineering design, business-process, and multi-disciplinary optimization & multi-criterion decision making problems.