A data mining-based solution method for flow shop scheduling problems

Document Type : Article


1 Department of Industrial Engineering, Kocaeli University, Kocaeli, Turkey

2 Department of Information Systems, Statistics and Management Science, University of Alabama, USA


The scheduling theme which determines where and when the manufacturing actions will be performed is important to perform the activities in time, efficiently and cost-effectively. In this paper, an algorithm is proposed for the solution of a flow shop scheduling problem which holds an important place in the scheduling literature. The path relinking algorithm and data mining are used together for the solution of the flow shop scheduling problem studied here. While data mining is used for globally searching the solution space, path relinking is used for local search. Data Mining is a method which is used to extract the embedded information in a cluster that includes implicit information. Path relinking is an algorithm which advances by making binary displacements in order to convert the initial solution to the guiding solution and repeats by assigning the best obtained solution within this process as to the starting point. The efficiency of the model was tested on Taillard’s flow shop scheduling problems. Consequently, it has been possible to solve the large-size problem without the considerable mathematical back round. The obtained results show that the proposed method is in the competitiveness level with the other metaheuristic methods.


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