Invited Speaker---Dr. Kuan Yew Wong, Professor
Industrial and Systems Engineering Research Group, Universiti Teknologi Malaysia (UTM), Malaysia
Kuan Yew Wong received his PhD degree from the University of Birmingham, UK. He is a Professor of Industrial Engineering at the Faculty of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Malaysia. He leads the Industrial and Systems Engineering Research Group, working in close collaboration with industries and other stakeholders to develop and implement solutions for operational improvement. Before joining the academia, he was an engineer in a Japanese multinational manufacturing company. He is a Chartered Engineer, Chartered IT Professional and Fellow of The Chartered Institute for IT (formerly known as British Computer Society). He has headed and completed various research projects funded by the Malaysia’s Ministry of Higher Education, Ministry of Science, Technology and Innovation, Intel Technology Ptd Ltd and Mexico State Council of Science and Technology. He is also an editorial board member of a number of international journals.
A Pareto-Based Multi-Objective Imperialist Competitive Algorithm for Solving Green Job Shop Scheduling Problems
Job shop scheduling problems are non-deterministic polynomial-time hard (NP-hard) problems which are very complicated, especially when multiple objectives or criteria are considered. The objectives of previous studies were predominantly aimed at optimizing efficiency, make-span, tardiness, flow time and buffer capacity, among others. Studies that have considered green factors such as carbon footprint in job shop scheduling were very limited. Hence, this research was aimed to formulate a mathematical model for green job shop scheduling to simultaneously minimize a classical-based objective function (total late work criterion) and an environmental-based objective function (carbon footprint). In order to solve this highly complicated problem, a new Pareto-based multi-objective imperialist competitive algorithm (MOICA) was developed to obtain a set of non-dominated solutions. Various extended benchmarked problem instances were utilized to evaluate the algorithm. It was also compared with two famous meta-heuristics, which were non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization. The computational results showed that the proposed MOICA was able to generate good quality Pareto solutions. It produced a higher number of high quality non-dominated schedules as compared to the other meta-heuristic approaches.