A variable block insertion heuristic for permutation flowshops with makespan criterion

Taşgetiren, M. Fatih; Pan, Quan-Ke; Kızılay, Damla; Vélez-Gallego, Mario C.

doi:10.1109/cec.2017.7969382

Cited by 7 publications

(5 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraint (9) ensures that the completion time of each job on each machine cannot be processed before their completion time on the previous machine. Constraints (10) and (11) specify the relationship between the processing of two consecutive jobs on the same machine. Constraint (11) starts that if job i precedes job j in the permutation, then job i should be completed before job j on each machine.…”

Section: Decision Variablesmentioning

confidence: 99%

See 1 more Smart Citation

A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

et al. 2019

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then, it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally, again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution, it replaces the current solution with the new one. As long as it improves, it retains the same block size. Otherwise, the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results, mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately, we run Taillard’s benchmark suite and compare the algorithms. In addition to the above, three instances of Taillard’s benchmark suite are also further improved for the first time in this paper since 1993.

show abstract

Section: Decision Variablesmentioning

confidence: 99%

“…In this study, we employ new hard VRF instances which are first introduced in [9], and they also applied an IG algorithm. In addition, the same problem was studied in [10] to minimize the makespan over Taillard's benchmark suite.…”

Section: Introductionmentioning

confidence: 99%

A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nevertheless, it is obvious that given a problem instance, optimal makespan for the typical PFSP version will be smaller than the optimal makespan for the PFSP-SDST or MBPFSP, which will be smaller than the optimal makespan for the PFSP-BS. In the absence of optimal values for PFSP-BS, we compare our results with the best known makespan values of the typical PFSPs (Tasgetiren et al, 2017b) and PFSP- SDSTs (Ruiz and Stützle, 2008) and MBPFSPs. However, we note that these comparisons are just indicative and not definitive.…”

Section: Comparing With Lower Boundsmentioning

confidence: 99%

Scheduling blocking flowshops with setup times via constraint guided and accelerated local search

Newton

Riahi

et al. 2019

Computers & Operations Research

View full text Add to dashboard Cite

“…The permutation flow shop scheduling problem (PFSP) is one of the classic combinatorial optimization problems, which has been proved as an NP-hard problem [7,8]. Due to its importance in engineering applications and academic, certain methods have been proposed to solve the PFSP [9,10].…”

Section: Introductionmentioning

confidence: 99%

A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem

Zhao

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

The distributed manufacturing has become a prevail production mode under the economic globalization. In this article, a memetic discrete differential evolution (MDDE) algorithm is proposed to address the distributed permutation flow shop scheduling problem (DPFSP) with the minimization of the makespan. An enhanced NEH (Nawaz–Enscore–Ham) method is presented to produce potential candidate solutions and Taillard’s acceleration method is adopted to ameliorate the operational efficiency of the MDDE. A new discrete mutation strategy is introduced to promote the search efficiency of the MDDE. Four neighborhood structures, which are based on job sequence and factory assignment adjustment mechanisms, are introduced to prevent the candidates from falling the local optimum during the search process. A neighborhood search mechanism is selected adaptively through a knowledge-based strategy which focuses on the adaptive evaluation for the neighborhood selection. The optimal combinations of parameters in the MDDE algorithm are testified by the design of experiment. The computational results and comparisons demonstrated the effectiveness of the MDDE algorithm for solving the DPFSP.

show abstract

A variable block insertion heuristic for permutation flowshops with makespan criterion

Cited by 7 publications

References 60 publications

A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

Scheduling blocking flowshops with setup times via constraint guided and accelerated local search

A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem

Contact Info

Product

Resources

About