A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop

A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem

In this paper we propose PSO–EDA, a hybrid particle swarm optimization (PSO) with estimation of distribution algorithm (EDA) to solve permutation flowshop scheduling problem (PFSP). PFSP is an NP-complete problem, for which PSO was recently applied. The social cognition in the metaphor of canonical PSO is incomplete, since information conveyed in the non-gbest particles is lost. Also, the intel...

A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve ...

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on m machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Alg...

A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem

Hybrid Taguchi-based Genetic Algorithm for Flowshop Scheduling Problem

A hybrid Taguchi-based genetic algorithm (HTGA) is developed for solving multi-objective flowshop scheduling problems (FSPs). Search performance is improved by using Taguchi-based crossover to avoid scheduling conflicts, and dynamic weights are randomly selected by a fuzzy inference system. The conventional approach to selecting dynamic weights randomly ignores small value for the objective whe...