A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

A comprehensive review and evaluation of permutation flowshop heuristics

A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility

The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan ( C max ). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.

Two new robust genetic algorithms for the flowshop scheduling problem

An application of genetic algorithms for flow shop problems

This research studies on application of genetic algorithms for flow shop problems with total flowtime as the criterion. There is still very little research focusing on total flow time for flow shop problems. We develop a genetic algorithm based heuristic for the problems, and use an integer programming model and an existing heuristic to evaluate the efficiency of the genetic algorithm based heuristic. We generate a set of problems with different numbers of machines, different numbers of jobs, and solve ten of each of the problems using the integer programming model, the existing heuristic, and the genetic algorithm based heuristic, respectively. The results are very encouraging and appear to indicate the genetic algorithms are efficient approaches for flow shop problems.

Venkateswara S. Vempati

Papers

An application of genetic algorithms for flow shop problems

An effective heuristic for flow shop problems with total flow time as criterion