Comparative Performance of Genetic Algorithm, Simulated Annealing and Ant Colony Optimisation in solving the Job-shop Scheduling Problem

Abstract

Planning requires decision making which is most important factor in the manufacturing production process. Effective decision making determines efficiency and cost of the production process. However, it is well-known that job-shop scheduling problem (JSP) is the hardest combinatorial optimisation problem, especially in the planning and managing of manufacturing processes. In this paper, a real case study of a brewery production scheduling problem is introduced which belongs to the JSP. In the brewery, orders will be received to queuing for production with a varying demand in the business process. A sequencing of orders will be allocated optimally whilst satisfying constraints subsequently forms the basis of a model-based control-theoretical approach. The paper implements three tools that included genetic algorithm; simulated annealing; ant colony optimisation to solve this problem which is to minimise the total production time and their performances are thus compared.