An iterated local search for unrelated parallel machines problem with unequal ready times
Chun-Lung Chen
Abstract
Chun-Lung Chen
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.
OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
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