Routing and scheduling in a flexible job shop by tabu search
Annals of Operations Research - Special issue on Tabu search
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Design and Analysis of Experiments
Design and Analysis of Experiments
Computers and Operations Research
Computers and Industrial Engineering
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
Computers and Operations Research
Flexible job-shop scheduling with parallel variable neighborhood search algorithm
Expert Systems with Applications: An International Journal
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
Applied Soft Computing
Expert Systems with Applications: An International Journal
An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Computers and Operations Research
Flexible job shop scheduling using hybrid differential evolution algorithms
Computers and Industrial Engineering
A hybrid harmony search algorithm for the flexible job shop scheduling problem
Applied Soft Computing
Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm
Expert Systems with Applications: An International Journal
A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem
Knowledge-Based Systems
Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem
Computers and Operations Research
Hi-index | 0.00 |
In this paper, an effective bi-population based estimation of distribution algorithm (BEDA) is proposed to solve the flexible job-shop scheduling problem (FJSP) with the criterion to minimize the maximum completion time (makespan). The BEDA stresses the balance between global exploration and local exploitation. In the framework of estimation of distribution algorithm, two sub-populations are used to adjust the machine assignment and operation sequence respectively with a splitting criterion and a combination criterion. At the initialization stage, multiple strategies are utilized in a combination way to generate the initial solutions. At the global exploration phase, a probability model is built with the superior population to generate the new individuals and a mechanism is proposed to update the probability model. At the local exploitation phase, different operators are well designed for the two sub-populations to generate neighbor individuals and a local search strategy based on critical path is proposed to enhance the exploitation ability. In addition, the influence of parameters is investigated based on Taguchi method of design of experiment, and a suitable parameter setting is determined. Finally, numerical simulation based on some widely used benchmark instances is carried out. The comparisons between BEDA and some existing algorithms as well as the single-population based EDA demonstrate the effectiveness of the proposed BEDA in solving the FJSP.