Computational intelligence paradigm for job shop scheduling and routing in an uncertain environment

Computational Intelligence (CI) is a more efficient paradigm for solving real-world problems in uncertain conditions. The traditional CI approaches are not capable to provide the complete and sufficient solutions for problems. Therefore, new techniques are necessary to efficiently solve these issues seriously. New techniques, such as Emergent Intelligence (EI), Multi-Agent System (MAS), etc., provide robust, generic, flexible, and self-organised to solve complex real-world problems. In this paper, we discuss Emergent Intelligence (EI) and its uniqueness in solving problems in an uncertain environment. We also discuss EI, Swarm Intelligence (SI) and MultiAgent System (MAS)-based problem-solving in an uncertain environment and compared their performance. We have considered two different problems: job shop scheduling using EI and MAS and route establishment for routing using MAS, SI and EI in an uncertain environment. Each problem is categorically analysed and solved step by step using MAS, SI and EI in a dynamic environment. We measure the performance of these three methods by varying the number of agents, tasks and time. Performance measures are compared and shown to demonstrate the importance of EI over MAS and SI for solving problems in an uncertain environment.