Effective Heuristics and an Iterated Greedy Algorithm to Schedule Identical Parallel Machines Subject to Common Restrictive Due Windows

In this paper, we address a variant of the identical parallel machines scheduling problem subject to common restrictive due windows. The performance measure adopted is the minimization of total weighted earliness and tardiness. Since the variant under study is an NP-hard problem for two or more mach...

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Vydáno v:Arabian journal for science and engineering (2011) Ročník 47; číslo 3; s. 3899 - 3913
Hlavní autoři: Rolim, Gustavo Alencar, Nagano, Marcelo Seido, Prata, Bruno de Athayde
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Springer Nature B.V
Témata:
Algorithms
Engineering
Greedy algorithms
Heuristic
Humanities and Social Sciences
multidisciplinary
Research Article-Systems Engineering
Scheduling
Science
Windows (computer programs)
Machine scheduling
Common due window
Identical parallel machines
Iterated greedy
Heuristics
Earliness and tardiness
ISSN:2193-567X, 1319-8025, 2191-4281
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Shrnutí:In this paper, we address a variant of the identical parallel machines scheduling problem subject to common restrictive due windows. The performance measure adopted is the minimization of total weighted earliness and tardiness. Since the variant under study is an NP-hard problem for two or more machines, we develop a family of constructive heuristics, which are comprised of four phases. First, jobs are sequenced according to priority rules. Second, jobs are assigned to machines using a greedy strategy. Third, a local search is performed to find a better distribution of jobs into machines. Fourth, two heuristics are applied for individually sequencing jobs in each machine, namely RN-RGH and RN-SEA. In addition, we also propose an iterated greedy algorithm to improve the solutions of the best performing heuristic. The computational experiments were carried out to prove the ability of these heuristics to find high-quality solutions in acceptable CPU time. More specifically, the RN-SEA family of algorithms stands out as the most efficient for the problem, however, with a higher computational effort. We also confirm that the IG algorithm has the potential for improving existing solutions, specially for problems with two machines and instances with up to 100 jobs in size.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-021-06244-9