A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

Souza, Davi Leonardo de; Lobato, Fran Sérgio; Gedraite, Rubens

doi:10.5772/38973

Cited by 2 publications

(2 citation statements)

References 30 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To fairly compare all algorithms, the same stopping criterion was used. This criterion is defined from different approaches in the literature [64,[66][67][68][69][70]. Most of them use a maximum execution time as stopping criterion, estimated in relation to the size of the problem, which is defined by the number of jobs and machines that the system has available.…”

Section: Parameter Calibrationmentioning

confidence: 99%

Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm

et al. 2022

View full text Add to dashboard Cite

In this work, we seek to design a model that contributes to the study and resolution of a multi-objective rescheduling problem in dynamic permutation flow shop contexts. In this type of problem, where the objectives can be valued in heterogeneous units, the difficulty of achieving an optimal solution leads to finding a set of non-dominated efficient solutions (also called Pareto front). On the other hand, we will also consider the potential appearance of disruptions in planned scheduling (such as machine breakdowns or arrival of new priority jobs) that require a rapid re-planning of the aforementioned scheduling. In this paper, a hybrid dynamic non-dominated sorting genetic II metaheuristic (HDNSGA-II) is proposed to find the optimal Pareto front. The algorithm is applied to a benchmark already tested in previous studies, defined by three conflicting objective functions (makespan, total weighted tardiness, and stability) and three different types of disruption (machine breakdowns, incorporation of new jobs, and modifications in process times). According to the statistical comparison performed, the HDNSGA-II algorithm performs better in the designed environment, especially in larger problems.

show abstract

Section: Parameter Calibrationmentioning

confidence: 99%

Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In order to define the stopping criterion, we analysed different approaches from the literature [58,[70][71][72][73][74]. Most use a maximum execution time, calculated depending on the size of the problem that is defined by the number of jobs and machines that the system has available, as the stopping criterion.…”

Section: Parameter Calibrationmentioning

confidence: 99%

Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments

et al. 2020

View full text Add to dashboard Cite

The aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing different concepts and are not directly comparable. Thus, it is not possible to reduce the problem to a single-objective optimization, and a set of efficient (nondominated) solutions, a so-called Pareto front, must be found. Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. In this paper, a mathematical model for this type of problem is proposed and a restarted iterated Pareto greedy (RIPG) metaheuristic is used to find the optimal Pareto front. To demonstrate the appropriateness of this approach, the algorithm is applied to a benchmark specifically designed in this study, considering three objective functions (makespan, total weighted tardiness, and steadiness) and three classes of disruptions (appearance of new jobs, machine faults, and changes in operational times). Experimental studies indicate the proposed approach can effectively solve rescheduling tasks in a multiobjective environment.

show abstract

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

Cited by 2 publications

References 30 publications

Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm

Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm

Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments

Contact Info

Product

Resources

About