Optimisation as its term implies often brings diversified contexts under one umbrella. Despite having opulence of existing optimisation approaches, integration of proper methods with real-life applications is still a challenging and onerous task. Here, we as a team, have been working on many such techniques to foot-print in diversified domains. We have been working on creating different Decision Support Systems (DSS) in Decision Analytics.
Following the growth and revolution of Industry 4.0 context in industries (particularly, on supply chain and project management domain), the emergence and inevitability of digitalised supply chain (aka, smart supply chain management (SCM)) and digitalised project management and scheduling (or, smart PM&S) became obvious to us. So, from 2017, banking on my Industrial Engineering (IE) skill and computer science knowledge, I along with my team members have been working on different technology decision-making approaches by integrating methodologies related to artificial intelligence (e.g., machine learning, deep learning), evolutionary algorithms (e.g., swarm intelligence and nature-inspired meta-heuristic approaches) and information systems (e.g., influence maximisation). In this process, the range of our research spectrum and skill set (i.e., Operations Research + IE + Systems Engineering + PM&S + SCM + Artificial Intelligence + Evolutionary Algorithms) unveiled a unique and extremely rare dimension in the decision-making literature or technology-enabled decision-making literature.
Similarly, this unique skillset has enabled me (and my team) to apply digitalisation focused optimisation techniques to many important engineering and non-engineering domains. For example, in recent years, we have been applying our OR and optimisation skills in many problems related to computer science (e.g., resource allocation and management for pattern recognition, image processing, internet of things, cyber-physical systems), electrical and electronic engineering (e.g., identification of photovoltaic cells, augmenting inverters, applying optimisation to pulse width modulation and selective harmonic elimination), and system engineering (e.g., simulation-optimisation, multi-criteria decision making).
Considering the recent buzzword on "sustainability", we have further extended our research span by adding different perspectives of sustainability in the industry context (e.g., carbon footprint, minimum transportation, minimisation of energy usage, and waste minimisation). Thence, our future directions would be to “design sustainable technology decision making approaches for cross-disciplinary optimisation problems in a capability context: digitalisation focused”.
A few UNSW courses related to our research portfolio:
- ZZCA6510: Decision Making in Analytics (UNSW Online)
- ZEIT8403: Capability Options Analysis
- ZEIT8404: Decision Making Analytics
- ZEIT8402: Evidence-based Decision Making
- ZEIT8310: Project Scheduling & Budget Control
- ZEIT3506: Managing the Development of Engineered Systems
Professional Education Short Courses
- Project Management Analytics (Click here for Registration)
- Supply Chain Analytics in Industry 4.0 era (Click here for Registration)
- Business Analysis and Valuation (Click here for Registration)
