An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem

Abstract

Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multiobjective flexible job-shop scheduling problem.

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

“…Aiming at the problem of optimal selection of manufacturing service composition, Que et al [23] proposed a new manufacturersto-users model for cloud manufacturing, established a comprehensive mathematical evaluation model with four key service quality perception indicators (i.e., time, cost, reliability, and capability), and solved the model by using information entropy immune genetic algorithm. Huang et al [24] combined genetic algorithm with particle swarm optimization, proposed a hybrid genetic particle swarm optimization algorithm based on teaching and learning, introduced learning mechanism into genetic algorithm, and enabled the descendants of genetic algorithm to learn the characteristics of elite chromosomes from double memory learning in the evolutionary process. e algorithm was searched for solutions in two subpopulations of genetic algorithm module and particle swarm optimization module and exchanged information simultaneously.…”

“…where ExpVal = f, in which f is the average fitness value of particles in the particle swarm; StaDev = r 3 × HypEnt + CloEnt, in which r 3 is a random number distributed in [0, 1]; ParEnt = f max − f/τ 1 , in which τ 1 is the control coefficient of particle entropy; HypEnt = CloEnt/τ 2 , in which τ 2 is the control coefficient of hyperentropy; ψ max is the maximum inertia coefficient of the particle swarm; ψ min is the minimum inertia coefficient of the particle swarm; f max is the maximum fitness value of the particle swarm; f min is the minimum fitness value of the particle swarm; f is the average fitness value of the particle swarm; f is the fitness value of the particle; and η 1 and η 2 are the constants in [0, 1] and can be set η 1 � 0.4 and η 2 � 0.8. From the analysis of equation (24), the inertia coefficient ψ has a larger value in the initial stage. With the increase in iterations, the inertia coefficient gradually decreases, which makes the algorithm change from global search in the initial stage to local fine search in the later stage.…”

“…Step 6: update the inertia coefficient of particles according to equation (24); update the self-acceleration coefficient and global acceleration coefficient of particles according to equations (25) and (26); update the velocity and position of particles according to equations (22) and (23).…”

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

“…F. Chen et al [32] proposed a QoS-aware multiobjective optimization algorithm for Web service composition, which took QoS performance as the optimization objective and solved the multiobjective optimization model of QoS-aware Web service composition. X. Huang et al [33] combined the genetic algorithm with the particle swarm optimization and proposed a hybrid genetic particle swarm optimization algorithm based on teaching and learning. A learning mechanism was introduced into the genetic algorithm, which enabled the descendants of the genetic algorithm to learn the characteristics of the elite chromosome from the dual memory learning.…”

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

“…Although there exists a sizable amount of literature available on the FJSSP, 6–8 the SFJSSP has not been extensively investigated. However, most of the common approaches employed to address the SFJSSP assume that the processing time follows certain probability distributions, such as uniform and normal distribution, considering values only in the positive domain.…”