Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons

@article{Dimopoulos2000RecentDI,
  title={Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons},
  author={Christos Dimopoulos and Ali M. S. Zalzala},
  journal={IEEE Trans. Evol. Comput.},
  year={2000},
  volume={4},
  pages={93-113}
}
The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to… 

Figures and Tables from this paper

A review of evolutionary multiobjective optimization applications in the area of production research
  • C. Dimopoulos
  • Business
    Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
  • 2004
TLDR
The impact of the fast-growing evolutionary multiobjective optimization field in this area of research is examined, leading to a number of conclusions and establishes directions for future research.
A review of the current applications of genetic algorithms in assembly line balancing
TLDR
A survey has been conducted from the recent published literature on assembly line balancing including genetic algorithms, which summarized the main specifications of the problems studied, the Genetic algorithms suggested and the objective functions used in evaluating the performance of the genetic algorithms.
Scheduling Distributed Flexible Manufacturing Systems Using Multi-Objective Gravity Search Algorithm
The use of intelligent techniques and heuristic methods in the manufacturing field has been growing in the last decade due to the fact that mostly manufacturing optimization and scheduling problems
An Adaptive Genetic Algorithm For Multi Objective Flexible Manufacturing Systems
TLDR
In the implementation, a Pareto-based approach with an adaptive weighted sum technique for tackling the multi-objective flexible manufacturing systems problem and results demonstrate that this approach is very effective for handling such complex systems.
A new multi-objective optimization method for master production scheduling problems based on genetic algorithm
In an environment of global competition, the success of a manufacturing corporation is directly related to the optimization level of its processes in general, but, in particular, to how it plans and
Evolutionary Optimization in Production Research
TLDR
The aim of the chapter is to present researchers, practitioners, and managers with a basic understanding of the current use of evolutionary computation techniques and allow them to either initiate further research or employ the existing algorithms in order to optimize their production lines.
Genetic programming for manufacturing optimisation
TLDR
The applicability of genetic programming in the field of manufacturing optimisation is investigated and the introduction of novel genetic programming frameworks for the solution of these problems are introduced.
Multi-objective evolutionary algorithms for a class of sequencing problems in manufacturing environments
  • C. Meloni, D. Naso, B. Turchiano
  • Business
    SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483)
  • 2003
TLDR
A novel evolutionary algorithm is devised, and compared with two other state-of-art genetic optimizers used in similar contexts, on both small-size problems with known Pareto-sets and larger problems derived from industrial production of furniture.
...
...

References

SHOWING 1-10 OF 306 REFERENCES
A Comparison of Heuristic Methods in Scheduling Flexible Assembly Systems
TLDR
For optimization problems that are NP-hard, which includes most problems arising in scheduling, there are at present no easy solutions, so the objective of finding the global optimum has to be changed in the target to find the best result possible over a given period.
A Comparison of Optimization Techniques for Integrated Manufacturing Planning and Scheduling
TLDR
A comparison between Simulated Annealing, Dispatch Rules, and a Coevolutionary Distributed Genetic Algorithm solving a random sample of integrated planning and scheduling problems found the DGA consistently outperformed SA and DR.
A genetic algorithm approach to group machines into manufacturing cells
  • H. Pierreval, M. Plaquin
  • Business
    Proceedings of the Fourth International Conference on Computer Integrated Manufacturing and Automation Technology
  • 1994
Grouping machines into manufacturing cells is now a commonly used technique for improving the flow of products in some manufacturing systems. The machines are grouped into cells to minimize the
Application of genetic algorithm to scheduling problems in manufacturing processes
  • N. Sannomiya, H. Iima
  • Business
    Proceedings of IEEE International Conference on Evolutionary Computation
  • 1996
TLDR
A genetic algorithm approach to optimal scheduling in manufacturing processes is considered and a numerical result is shown for a modified flowshop scheduling problem.
Genetic algorithm based scheduling in a dynamic manufacturing environment
TLDR
The application of adaptive optimization methods to production scheduling has recently become a research topic of broad interest and a temporal decomposition of the nondeterministic problem leads to a scheduling control that combines simulation and adaptive search.
GA with hierarchical evaluation: a framework for solving complex machine scheduling problems in manufacturing
TLDR
This paper proposes an intuitive yet efficient framework for solving complex machine scheduling problems and shows that this same framework has better performance than most of the other known heuristics even in traditional n/m/J/Cmax problems.
A Promising Hybrid GA/Heuristic Approach for Open-Shop Scheduling Problems
TLDR
This work describes a hybrid approach to this problem which combines a Genetic Algorithm (GA) with simple heuristic schedule building rules, and describes issues relating to further improvement of performance and integration of the approach into industrial job shop environments.
...
...