A rule-centric memetic algorithm to minimize the number of tardy jobs in the job shop

This paper addresses the job shop scheduling problem with minimizing the number of tardy jobs as the objective. This problem is usually treated as a job sequencing problem, and the permutation-based representation of solutions was commonly used in the existing search-based approaches. In this paper, the flaw of the permutation-based representation is discussed, and a rule-centric concept is proposed to deal with it. A memetic algorithm is then developed to realize the proposed idea by tailored genome encoding/decoding schemes and local search procedure. Two benchmark approaches, a multi-start hill climbing approach and a simulated annealing approach, are compared in the experiments. The results show that the proposed approach significantly outperforms the benchmarks.