Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems

Abstract

Jobs to be delivered in a production system are usually weighted according to clients requirements and relevance. Attempting to achieve higher customer satisfaction trends in manufacturing are focussed today on production policies, which emphasizes minimum weighted tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced multiple-crossovers-on-multiple-parents (MCMP) a multirecombinative approach allowing multiple crossovers on the selected pool of (more than two) parents. MCMP-SRI is a novel MCMP variant, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents. Members of this mating pool subsequently undergo multiple crossover operations. This paper briefly describes the weighted tardiness problem in a single machine environment, and summarizes implementation details and MCMP-SRI performance for a set of problem instances extracted from the OR-Library.