Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems
Fuzzy Sets and Systems - Special issue on operations research
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A new approach for ranking fuzzy numbers by distance method
Fuzzy Sets and Systems
Tabu Search
Scheduling Computer and Manufacturing Processes
Scheduling Computer and Manufacturing Processes
Tabu search for a class of single-machine scheduling problems
Computers and Operations Research
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Minimizing tardiness in a two-machine flow-shop
Computers and Operations Research
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
| Hi-index | 0.00 |
In this paper, two different approaches to permutation flow shop scheduling problem are investigated. The first one is based on a mixed integer programming model and is solved by an optimization package GAMS. As the problem belongs to NP-complete problems, this approach is limited to smaller instances, its reasonable bounds are indicated using benchmarks from OR-Library. For large instances, an approach using genetic algorithm is proposed including its appropriate parameter settings. Finally, a modification of the problem using uncertain processing times of jobs is presented.