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This paper considers an open shop scheduling problem that minimizes bi-objectives, namely makespan and total tardiness. This problem, due to its complexity, is ranked in the class of NP-hard problems. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient method based on multi-objective simulated annealing and ant colony optimization,...
This paper presents a hybrid efficient method namely hybrid immune algorithm (HIA) based on artificial immune algorithm (AIA) and bacterial optimization for clustering problems. Four local searches on the basis of heuristic rules for the given clustering problem are designed and applied. This proposed method is implemented and tested on two real datasets. Further, its performance is compared with...
This article considers the bi-objective job shop scheduling problem in which the make span and the total tardiness of jobs are minimized. In order to find a set of dominant solutions, that is, an approximation of the Pareto optimal solutions, we propose three versions of a genetic algorithm with techniques like hybridization with local search, path relinking and elitism. The three versions of the...
Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain...
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