Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

Taşgetiren, M. Fatih; Kızılay, Damla; Pan, Quan-Ke; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.cor.2016.07.002

Cited by 103 publications

(39 citation statements)

References 46 publications

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“…So, the value of the Hill coefficient was chosen to be 1.5. In order to analyze the performance of the proposed algorithm, it was compared with the hybrid genetic algorithm (HGA) in [18], the improved iterated greedy algorithm (IIGA) in [19], the iterated greedy algorithm with a referenced insertion scheme (IG-RIS) in [20], the memetic algorithm (MA) in [21], and the discrete self-organizing migrating algorithm (DSOMA) in [22]. Table 4 shows the makespan results of the proposed algorithm and the other four algorithms tested in different sizes of Taillard benchmark.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Ding et al [19] presented an improved iterated greedy algorithm with a tabu-based reconstruction strategy. An iterated greedy algorithm in [20], a high-performing memetic algorithm in [21], and a discrete self-organizing migrating algorithm in [22] have been used for flow shop problems with the minimum makespan criterion and obtained better results. Eddaly et.al [23] proposed a hybrid combinatorial particle swarm optimization (HCPSO) algorithm as a resolution technique for solving this problem.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Wei

Jiang

et al. 2018

Applied Sciences

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Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Wei

Jiang

et al. 2018

Applied Sciences

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“…The RIS local search is an insertion local search neighborhood. The details can be found in [47,[54][55][56][57][58][59]. Note that the fast fitness calculation is used in the RIS local search.…”

Section: Variable Local Searchmentioning

confidence: 99%

“…Note that the cost function devised for any constructive heuristic has a significant impact on the results. For this reason, Tasgetiren et al [47] developed a _ ( ) heuristic for the BFSP with the makespan criterion, which was inspired from the _ ( ) heuristic in [16]. In [47], a new cost function was devised by adding the departure time of the last job on the last machine to the total idle and blocking times.…”

Section: Initial Solutionmentioning

confidence: 99%

“…For this reason, Tasgetiren et al [47] developed a _ ( ) heuristic for the BFSP with the makespan criterion, which was inspired from the _ ( ) heuristic in [16]. In [47], a new cost function was devised by adding the departure time of the last job on the last machine to the total idle and blocking times. In this paper, we extend the _ ( ) heuristic to the total flowtime criterion and denote it as the _ ( ) heuristic.…”

Section: Initial Solutionmentioning

confidence: 99%

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A Variable Block Insertion Heuristic for the Blocking Flowshop Scheduling Problem with Total Flowtime Criterion

et al. 2016

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Abstract:In this paper, we present a variable block insertion heuristic (VBIH) algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm, we define a minimum and a maximum block size. After constructing the initial sequence, the VBIH algorithm starts with a minimum block size being equal to one. It removes the block from the current sequence and inserts it into the partial sequence sequentially with a predetermined move size. The sequence, which is obtained after several block moves, goes under a variable local search (VLS), which is based on traditional insertion and swap neighborhood structures. If the new sequence obtained after the VLS local search is better than the current sequence, it replaces the current sequence. As long as it improves, it keeps the same block size. However, if it does not improve, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the current sequence. This process is repeated until the block size reaches at the maximum block size. Furthermore, we present a novel constructive heuristic, which is based on the profile fitting heuristic from the literature. The proposed constructive heuristic is able to further improve the best known solutions for some larger instances in a few seconds. Parameters of the constructive heuristic and the VBIH algorithm are determined through a design of experiment approach. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed VBIH algorithm outperforms the discrete artificial bee colony algorithm, which is one of the most efficient algorithms recently in the literature. Ultimately, 52 out of the 150 best known solutions are further improved with substantial margins.

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A Novel Discrete Particle Swarm Optimization Algorithm for the Travelling Salesman Problems

Sequeiros

Silva

Santos

et al. 2021

Innovations in Industrial Engineering

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Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

Cited by 103 publications

References 46 publications

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

A Variable Block Insertion Heuristic for the Blocking Flowshop Scheduling Problem with Total Flowtime Criterion

A Novel Discrete Particle Swarm Optimization Algorithm for the Travelling Salesman Problems

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