A new hybrid parallel genetic algorithm for the job‐shop scheduling problem

Abstract

The job‐shop scheduling problem (JSSP) is considered one of the most difficult NP‐hard problems. Numerous studies in the past have shown that as exact methods for the problem solution are intractable, even for small problem sizes, efficient heuristic algorithms must achieve a good balance between the well‐known themes of exploitation and exploration of the vast search space. In this paper, we propose a new hybrid parallel genetic algorithm with specialized crossover and mutation operators utilizing path‐relinking concepts from combinatorial optimization approaches and tabu search in particular. The new scheme relies also on the recently introduced concepts of solution backbones for the JSSP in order to intensify the search in promising regions. We compare the resulting algorithm with a number of state‐of‐the‐art approaches for the JSSP on a number of well‐known test‐beds; the results indicate that our proposed genetic algorithm compares fairly well with some of the best‐performing genetic algorithms for the problem.

Emergency scheduling of engineering rescue tasks in disaster relief operations and its application in China

“…The tabu tenure is set to , and the maximum number of nonimproving iterations is set to . A GA that uses a two‐chromosome encoding with crossover and mutation operators as Carter and Ragsdale (). The population size is 100, and the mutation probability is 0.05. A hybrid GA that utilizes path‐relinking and TS (HGAT) (Spanos et al., ). The population size is 60, the mutation probability is 0.2, the tabu tenure is 8, and the maximum number of nonimproving iterations is 5. Another hybrid GA with simulated annealing (HGAS) previously used by the organization.…”

“…r A GA that uses a two-chromosome encoding with crossover and mutation operators as Carter and Ragsdale (2006). The population size is 100, and the mutation probability is 0.05. r A hybrid GA that utilizes path-relinking and TS (HGAT) (Spanos et al, 2014). The population size is 60, the mutation probability is 0.2, the tabu tenure is 8, and the maximum number of nonimproving iterations is 5.…”

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

“…The proposed DFOA included only two fundamental parameters, the population size ( N ), and maximum number of generations ( MaxGen ), which were defined as N = 5 and MaxGen = 100 herein, respectively. The DFOA, Parallel Hybrid Genetic Algorithm (PHGA) [42], and PSO were applied to small, middle, and large instances 20 times each using the same parameters. The results, including the reported optimal tour length (Optimum), average tour length (Mean), standard deviation (SD), best tour length (Best), and average CPU running time (CPU), are provided in Tables 1–3.…”

A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems

“…On the average, the advised method had a better performance of generating optimal or near-optimal solutions with fast convergence speed than a GA or a quantum GA for large instance problems. Spanous et al [29] designed a parallel GA for solving job shop scheduling problems with an elitist strategy based selection, a path relinking crossover and a swap mutation. The parallelization was set following the islands paradigm.…”