Bio-inspired and musical-harmony approaches for machine allocation optimization in flexible manufacturing system

Abstract

Manufacturing industries need to constantly adjust to the rapid pace of change in the market. Many of the manufactured products often have a very short life cycle. These scenarios imply the need to improve the efficiency of capacity planning, an important aspect of machine allocation plan that is known for its complexity. Two common approaches to solve the machine allocation problem include optimization-based methods and heuristic oriented methods. Although optimization-based methods are robust in their applicability, they tend to become impractical when the problem size increases, while heuristic approaches are mainly dependent on rules and constraints of an individual problem. Due to this, heuristic approaches always face difficulties to estimate results in a changed environment. The use of new and innovative meta-heuristic searching techniques of populationbased algorithms in this research can overcome these limitations. The objectives of this research are to minimize the system unbalance and machine makespan utilization, and to increase throughput taking into consideration of the technological constraints. Population-based algorithms that consist of constraint-chromosome genetic algorithm (CCGA), constraint-vector harmony search (CVHS) and hybrid of constraint-chromosome genetic algorithm and harmony search (H-CCGaHs) were adopted. To ensure the feasibility of the results and to promote for a faster convergence, the right mapping chromosome or harmony memory representation was applied to the domain problem in all the three algorithms. Genetic algorithm is known for its exploitative ability, whereas harmony search is recognized for its explorative capability. H-CCGaHs combines these strengths to produce a more effective algorithm where both aspects will be optimized and helps avoid getting trapped in local minima. These three algorithms (CCGA, CVHS and H-CCGaHs) were tested on both benchmark data (10 datasets) and industrial data (5 datasets). The results indicated that the proposed H-CCGaHs achieves better results, with faster convergence and a reasonable time to run the algorithm.