The Flexible Job-Shop Scheduling Problem is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying several parallel goals. We introduce a Memetic Algorithm, based on the NSGAII (Non-Dominated Sorting Genetic Algorithm II) acting on two chromosomes, to solve this problem. The algorithm adds, to the genetic stage, a local search procedure (Simulated Annealing). We have assessed its efficiency by running the algorithm on multiple objective instances of the problem. We draw statistics from those runs, which indicate that this Memetic Algorithm yields good and low-cost solutions.

A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

The job shop scheduling problem is a well-known NP hard problem, on which genetic algorithm is widely used However, due to the lack of the major evolution direction, the effectiveness of the regular genetic algorithm is restricted In this paper, we propose a new hybrid genetic algorithm to solve the job shop scheduling problem The particle swarm optimization algorithm is introduced to get the initial population, and evolutionary genetic operations are proposed We validate the new method on seven benchmark datasets, and the comparisons with some existing methods verify its effectiveness.

A hybrid PSO/GA algorithm for job shop scheduling problem

Genetic algorithm (GA) is one of the most widely used population-based evolutionary search algorithms. One of the challenging optimization problems in which GA has been extensively applied is feature selection. It aims at finding an optimal small size subset of features from the original large feature set. It has been found that the main limitation of the traditional GA-based feature selection is that it tends to get trapped in local minima, a problem known as premature convergence. A number of implementations are presented in the literature to overcome this problem based on fitness scaling, genetic operator modification, boosting genetic population diversity, etc. This paper presents a new modified genetic algorithm based on enhanced population diversity, parents' selection and improved genetic operators. Practical results indicate the significance of the proposed GA variant in comparison to many other algorithms from the literature on different datasets.

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Enhancing the diversity of genetic algorithm for improved feature selection

The flow-shop scheduling problem with the makespan criterion is an important production scheduling problem. Although this problem has a simple formulation, it is NP-hard. Therefore many heuristic and metaheuristic methods had been proposed to solve this problem. In this paper, a hybrid genetic algorithm is presented for the flow-shop scheduling problem. In our method, a modified version of NEH with random re-start is used to generate the initial population. Also, a new orthogonal array crossover is devised as the crossover operator of the genetic algorithm. The tabu search is hybridized with the genetic algorithm and acts as the local search method. The proposed algorithm had been tested on two benchmarks. The results are compared with those of other methods that had also been tested on these benchmarks. The comparison shows that our method outperforms other methods on these benchmarks.

A hybrid genetic algorithm for the flow-shop scheduling problem

The flow shop scheduling problem (FSSP) is a NP-HARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particle swarm optimization algorithm (PSO) is introduced for better initial group. By combining PSO with GA, a hybrid optimization algorithm for FSSP is proposed. This method is validated on a series of benchmark datasets. Experimental results indicate that this method is efficient and competitive compared to some existing methods.

Hybrid Genetic Algorithm for Flow Shop Scheduling Problem

Based on the concepts of operation template and virtual job shop, this paper attempts to solve several job shop scheduling problems with different scale and analyzes the relationship among the population size, mutation probability, the number of evolving generations and the complexity of the undertaking problem visually by using the trend chart of the fitness curves. This visual analysis could provide some referencing information for the adjustment of genetic algorithm running parameters.

X.L. Xing

Papers

Genetic algorithm application on the job shop scheduling problem