Solving the Unrelated Parallel Machine Scheduling Problem with Setup Times by Efficient Algorithms Based on Iterated Local Search

The Unrelated Parallel Machine Scheduling Problem with Setup Times (UPMSPST) is a problem that belongs to the \(\mathcal {NP}\)-Hard class and it is frequently found in many practical situations, like in textile and chemical industries. The objective in UPMSPST is to schedule jobs in machines in order to achieve the maximum completion time, known as makespan. In an attempt to solve this problem, it is proposed two algorithms: the AIV and the HIVP. Both algorithms are based on Iterated Local Search (ILS) and Variable Neighborhood Descent (VND). The difference between AIV and HIVP is that the first one generates a greedy initial solution, while the second applies a partially greedy procedure to construct the initial solution and it includes the Path Relinking (PR) technique. Neighborhoods based on swaps and multiple insertions are investigated in the developed algorithms. AIV and HIVP were tested on benchmark test problems from literature and statistical analysis of the computational results showed the superiority of them, outperforming the previously best known solutions for UPMSPST.

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