Job sequencing in an evolutionary paradigm

The use of traditional optimization approaches has reached a point of saturation in solving many complex optimization problems. However, the design of an efficient stochastic algorithm whose solution is close to optimal is still possible, albeit the complexity of such problems. These algorithms basically search through a space of potential solutions using randomness as a major factor in decision making. In any optimization process, the goal is to find the best solution. For small spaces, classical exhaustive search methods usually suffice. For larger spaces, special artificial intelligence techniques must be employed. Evolutionary algorithm models natural selection which generates new points in the search by applying operators to current points and statistically moving toward more optimal points in the search space. The paper describes the job sequencing (JS) problem and the evolutionary programming (EP) approach, and proposes an evolutionary algorithm (EA) that solves the JS problem. It also compares EA with the following methods: Shortest processing time (SPT), longest processing time (LPT) and greedy method (GM), and then demonstrates the effectiveness of EA in obtaining better results.