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An iterated local search for unrelated parallel machines problem with unequal ready times

Chun-Lung Chen

Open publisher page 11 citations

Abstract

The problem considered in this paper is a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and unequal ready times so as to minimize total weighted number of tardy jobs. An iterated local search (ILS) is developed to solve the problem. It employs a swap shaking procedure and a tabu search algorithm with hybrid neighborhoods to ensure high ILS effectiveness and efficiency. To evaluate the performance of the suggested heuristic, some heuristic rules, a conventional tabu search algorithm, and optimal solutions are examined for comparison purposes. The computational experimental results show that the proposed ILS is a promising method for solving this problem.

About this research paper

What this paper is about

The problem considered in this paper is a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and unequal ready times so as to minimize total weighted number of tardy jobs. An iterated local search (ILS) is developed to solve the problem. It employs a swap shaking procedure and a tabu search algorithm with hybrid neighborhoods to ensure high ILS effectiveness and efficiency. To evaluate the performance of the suggested heuristic, some heuristic rules, a conventional tabu search algorithm, and optimal solutions are examined for comparison purposes. The computational experimental results show that the proposed ILS is a promising method for solving this problem.

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OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

The problem considered in this paper is a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and unequal ready times so as to minimize total weighted number of tardy jobs. An iterated local search (ILS) is developed to solve the problem. It employs a swap shaking procedure and a tabu search algorithm with hybrid neighborhoods to ensure high ILS effectiveness and efficiency. To evaluate the performance of the suggested heuristic, some heuristic rules, a conventional tabu search algorithm, and optimal solutions are examined for comparison purposes. The computational experimental results show that the proposed ILS is a promising method for solving this problem.

Key concepts: Tabu search, Iterated local search, Swap (finance), Iterated function, Mathematical optimization, Guided Local Search, Hill climbing, Heuristic

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