Risk Optimization-Based Logistics Management
Various approaches in the existing literature optimize operations in different logistics areas, such as procurement, production and distribution. However, all of them synthesize knowledge in an area only from the past observations which are related to it. This process of knowledge synthesis is classified as historic-based process of logistics operations optimization. However, in today’s information centred age, Big Data provides a wealth of information from different relevant areas to analyse it in real time and synthesize knowledge for another area. For example, analytic techniques can be applied on the real-time Big Data information coming from production planning and control (production logistics area) to synthesize knowledge and make decisions relevant to supplier management, ordering and order control (procurement logistics area). Furthermore, it is not necessary for the information coming from different areas to be relevant to the same supply chain; it can also be from other similar production units or supply chains. This process of knowledge synthesis is termed as the current-trend based process of supply chain operation optimization, where real-time information from other parts of the supply chain are utilized to make optimization decisions, improve efficiency, reduce wastage and losses. The objective of this project is to design, model, apply and test such a framework for managing operations in the logistics ecosystem by using knowledge across different areas.
Description of Work:
This project involves design and development of algorithms, risk modelling knowledge, information analysis techniques, good mathematical knowledge as well as possible numerical modelling.
Dr Omar Hussain (firstname.lastname@example.org)