Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines

Boxuan Zhao (), Jianmin Gao, Kun Chen and Ke Guo
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Boxuan Zhao: Xi’an Jiaotong University
Jianmin Gao: Xi’an Jiaotong University
Kun Chen: Xi’an Jiaotong University
Ke Guo: Xi’an Jiaotong University

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 1, No 6, 93-108

Abstract: Abstract The flexibilities of alternative process plans and unrelated parallel machines are benefit for the optimization of the job shop scheduling problem, but meanwhile increase the complexity of the problem. This paper constructs the mathematical model for the multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines, splits the problem into two sub-problems, namely flexible processing route decision and task sorting, and proposes a two-generation (father and children) Pareto ant colony algorithm to generate a feasible scheduling solution. The father ant colony system solves the flexible processing route decision problem, which selects the most appropriate process node set from the alternative process node set. The children ant colony system solves the sorting problem of the process task set generated by the father ant colony system. The Pareto ant colony system constructs the applicable pheromone matrixes and heuristic information with respect to the sub-problems and objectives. And NSGAII is used as comparison whose genetic operators are re-defined. The experiment confirms the validation of the proposed algorithm. By comparing the result of the algorithm to NSGAII, we can see the proposed algorithm has a better performance.

Keywords: Flexible job shop scheduling; Two-generation Pareto ant colony algorithm; Alternative process plan; Unrelated parallel machines; Multiple objective scheduling (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-015-1091-z

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