Abstract
This research considers an unrelated parallel machine scheduling problem with energy consumption and total tardiness. This problem is compounded by two challenges: differences of unrelated parallel machines energy consumption and interaction between job assignments and machine state operations. To begin with, we establish a mathematical model for this problem. Then an ant optimization algorithm based on ATC heuristic rule (ATC-ACO) is presented. Furthermore, optimal parameters of proposed algorithm are defined via Taguchi methods for generating test data. Finally, comparative experiments indicate the proposed ATC-ACO algorithm has better performance on minimizing energy consumption as well as total tardiness and the modified ATC heuristic rule is more effectively on reducing energy consumption.
Highlights
In recent years, energy saving has been growing a great interest due to sequence of serious environmental impacts and rising energy cost [1,2,3]
Performance of solutions to yield using test problem is compared with two multiobjective optimization algorithms: GRASPTETT and original ACO algorithm (OACO)
The OACO algorithm shows the weakest performance with 5.95 mean relative percentage deviation (RPD)
Summary
Energy saving has been growing a great interest due to sequence of serious environmental impacts and rising energy cost [1,2,3]. Mouzon and Yildirim [14] considered the problem of minimizing total energy consumption and total tardiness on signal machine. Lin et al [20] considered an ACO algorithm to solve the problem of scheduling unrelated. Arnaout et al [21] addressed the nonpreemptive unrelated parallel machine scheduling problem with machine dependent and sequence-dependent setup times via a modified ACO algorithm. We begin the research of minimizing energy consumption and total tardiness on unrelated parallel machines. The problem is formulated by a weighted summation of energy consumption and total tardiness For solving this problem, we develop an ACO algorithm with ATC rules in which a machine reselection operation is applied.
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