Developing a genetic-based multi-objective algorithm to optimise job shop scheduling problems
by Mohammed Hussein; Abd_Elrahman Elgendy
International Journal of Collaborative Enterprise (IJCENT), Vol. 6, No. 1, 2018

Abstract: Dynamic job shop scheduling is one of the problems that get little attention in literature as it is known as an NP-hard combinatorial optimisation problem. Few researchers handled the mathematical model and the approaches of optimising the schedule efficiency and stability. As events such as (machine breakdown, arriving new jobs or processing time variation) are hard to be formulated in a mathematical model, this research introduces a dynamic multi-objective genetic algorithm based on partial repair reactive strategy. The reactive strategy is selected to deal with the dynamic nature of job shop by applying partial repair policy for optimising the scheduling efficiency and the schedule stability simultaneously. Experimental results show that the proposed algorithm provided better solutions than key problem solutions in dynamic job shop scheduling problems published in literature.

Online publication date: Thu, 31-May-2018

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