Abstract
This paper deals with no-idle flow shop scheduling problem with the objective of minimizing makespan. A new hybrid metaheuristic is proposed for the scheduling problem solution. The proposed method is compared with the best method reported in the literature. Experimental results show that the new method provides better solutions regarding the solution quality to set of problems evaluated.
Highlights
The flow shop scheduling problem is a problem of work scheduling in which n tasks must be scheduled, in the same sequence, in a set of m distinct machines
The solution for the problem is to determine among the (n!) possible sequences of tasks, the one that optimizes some measure of scheduling performance, being that the most common consist of minimizing the total scheduling duration, or minimize the sum of the tasks flow times
The evolutionary heuristic proposed in this research is a hybridization of the Cluster Search (CS) presented by Ribeiro Filho et al (2007) with the IGLS method proposed by Ruiz and Stützle (2007)
Summary
The flow shop scheduling problem is a problem of work scheduling in which n tasks must be scheduled, in the same sequence, in a set of m distinct machines. A particular case of flow shop scheduling, called permutational, occurs when in each machine is maintained the same processing order of tasks. The first relates to an efficient use of resources (machines) while the second aims to minimize the stock in processing. This scheduling problem has been intensely studied in literature, since the first study reported by Johnson (1954) in obtaining the optimal solution for the problem with two machines. The restrictions commonly considered in the flow shop scheduling problem restrain but do not exclude their practical applications (DUDEK et al, 1992). One of the restrictions is that the machines that compose the flow shop do not suffer
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