Comparative evaluation of genetic algorithms for job-shop scheduling

Many optimization problems from the industrial engineering world, in partic ular the manufacturing systems, are very complex in nature and quite hard t o solve by conventional optimization techniques. There has been increasing interest in imitating living beings to solve such kinds of hard optimizatio n problems. Simulating the natural evolutionary process of human beings res ults in stochastic optimization techniques called evolutionary algorithms, which can often outperform conventional optimization methods when applied t o difficult real-world problems. There are currently three main avenues of this research: genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs). Among them, genetic algorithms are perhaps the most widely known types of evolutionary algorithms today. During the past years, several GAs for the job-shop scheduling problems hav e been proposed, each with different chromosome representation. In this pap er, the different GAs are collected from the literature and an attempt has been made to evaluate them. The benchmark problems available in open litera ture are used for evaluation and the performance measure considered is make span. The algorithms are coded in C+ +.