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

A comprehensive review and evaluation of permutation flowshop heuristics

A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem

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 effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem

A very fast Tabu search algorithm for the permutation flow shop problem with makespan criterion

http://staff.iiar.pwr.wroc.pl/wojciech.bozejko/papers/2010/CAIE_Boz_Uch_Wod_2010.pdf

Parallel hybrid metaheuristics for the flexible job shop problem

Parallel tabu search for the cyclic job shop scheduling problem

Block approach-tabu search algorithm for single machine total weighted tardiness problem

In this paper we present two simulated annealing algorithms (sequential and parallel) for the permutation flow shop sequencing problem with the objective of minimizing the flowtime. We propose a neighbourhood using the so-called blocks of jobs on a critical path and specific accepting function. We also use the lower bound of cost function. By computer simulations on Taillard [17] and other random problems, it is shown that the performance of the proposed algorithms is comparable with the random heuristic technique discussed in literature. The proposed properties can be applied in any local search procedures.

Mieczysław Wodecki

Papers

A very fast Tabu search algorithm for the permutation flow shop problem with makespan criterion

Parallel hybrid metaheuristics for the flexible job shop problem

Parallel tabu search for the cyclic job shop scheduling problem

Block approach-tabu search algorithm for single machine total weighted tardiness problem

Solving the flow shop problem by parallel simulated annealing