Decision Making in Logistics and Supply Chain Management

Project Title:

Decision Making in Logistics and Supply Chain Management


David Yang Gao, Alex Rubinov Professor of Mathematics, Federation University

Tudor Ratiu, Honorary Professor, Shanghai Jiao Tong University

Ron G. Chen, Honorary Professor, City University, Hong Kong

Shu-Cherng Fang, Honorary Professor, North Carolina State University, USA

Eldar Hajilarov, Lecturer

Ning Ruan, Senior Research Fellow

Vittorio Latorre, Research Fellow

Contact person and email address:

David Yang Gao,

A brief description of the project:

Continuously supported by multi-million dollar grants from US Air Force Office for Scientific Research (AFOSR) over the past 10 years, our goal is to conduct a unique pioneering research in the multidisciplinary fields of

  • nonconvex science;
  • large-scale computational mathematics;
  • industrial and systems engineering.

The nonconvexity is essential in natural phenomena. It is the main reason that leads to chaos in complex dynamics, phase transitions in material science, bifurcation in nonlinear systems, fundamental difficulties in game theory and decision-making, as well as many NP-hard problems in global optimisation and computer science. Composed by the pioneering researchers and world well-known scholars in applied mathematicians, computer science, and systems engineering, our group has developed internationally recognised research in nonconvex science with successfully applications in multidisciplinary fields of applied mathematics, engineering mechanics, operations research, global optimisation, decision and computer science.  We are continuously providing breakthrough theories, methods, and computational techniques for young people to conduct cutting-edge research projects in multidisciplinary fields of applied mathematics and engineering sciences.

Research Project

Decision process is goal-oriented. There are often multiple, conflicting goals to be met by a single (optimal) decision. Mathematically, decision theory in operations research and logistics can be viewed as a marriage of Multi-scale Mixed Integer Nonlinear Programming (MMINLP) and Bayesian statistics. MMINLP refers to mathematical programming with continuous, discrete variables, and nonlinearities in target function and constraints. The use of MMINLP provides a natural approach for mathematically modelling many optimisation problems in decision science where it is necessary to simultaneously optimise the decision outcomes (discrete) and parameters (continuous). MMINLP problems are so difficult to solve, even subclasses MIP and NLP are among the class of theoretically difficult problems (NP-complete).

The main goal of this project is to develop a powerful deterministic method and polynomial algorithms for solving general MMINLP problems in decision science with real-world applications in logistic operations, machine learning, data mining, and artificial intelligence, etc. This project includes research in the following areas:

  1. Supply chain management, including supply chain strategy and decision making.
  2. Supply chain optimisation, including mathematically model complex supply chain problems and develop solution algorithm to analyse supply chain effectiveness and efficiency under different scenarios.