Flow Shop Scheduling Problem with Transportation Times, Two-Robots and Inputs/Outputs with Limited Capacity

Lamoudan, Tarik; Elkhoukhi, Fatima; Boukachour, Jaouad; Alaoui, Ahmed Elhilali

doi:10.20533/ijicr.2042.4655.2012.0029

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…As can be observed, BRKeCS even without applying CVR tuning has a competitive performance compared with those approaches. For the biggest instances, BRKeCS has showed satisfactory performance when compared with the other metaheuristics, as ACO [16]. Table 5.1 introduces more results of problem instances considered of high and medium difficulty.…”

Section: Brkecs Without Cvr Tuningmentioning

confidence: 99%

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Metaheuristic Tuning Generalisation by Cross-Validated Racing

Fonseca¹,

Oliveira²

2020

IJCA

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Many tuning methods are based on concepts of sensitivity analysis combined with heuristics that tend to reduce the search space by eliminating less promising configurations. Nevertheless, tuning parameters is a task that requires specific and timeconsuming experiments, especially when involving large problem instances. This is particularly due to existing methods were not designed to efficiently generalise a tuning of parameters to other instances that did not participate of the training process. In this paper, the recently proposed tuning method named Cross-Validated Racing (CVR) is revised in order to clarify theoretical fundamentals of tuning problem and expand the experiments to make possible evaluating its generalisation capacity against the reduction in the size of the training set. For validation, the Biased Random-Key Evolutionary Clustering Search (BRKeCS) is applied to solve scalable instance groups of Permutation Flow Shop Scheduling Problem. The computation results have demonstrated that CVR is robust in finding an effective parameter setting, requiring training process in only a half of total instance set.

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Section: Brkecs Without Cvr Tuningmentioning

confidence: 99%

“…Initially, a test of solution quality has been led by running BRKeCS 5 times, using the same number of objective function calls as other approaches found in literature: MOACSA [33], CR(MC) [4], ACO and HAMC1 algorithms [16,23,24]. Table 5.1 shows the percentage difference of algorithms with respect to PFSP instances 20x20.…”

Section: Brkecs Without Cvr Tuningmentioning

confidence: 99%

Metaheuristic Tuning Generalisation by Cross-Validated Racing

Fonseca¹,

Oliveira²

2020

IJCA

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“…This study aimed to solve the flow shop scheduling problem by considering travel time with the MFO algorithm. Travel time was based on vehicles transporting materials back and forth between machines [27,28]. This model imitated the problem of pickup and delivery [29].…”

Section: Article Info Abstractmentioning

confidence: 99%

The Moth-Flame Optimization Algorithm for Flow Shop Scheduling Problem with Travel Time

Amallynda

Hutama

2021

JTI

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This article examined the flow shop scheduling problem by considering the travel time between machines. The objective function of this problem was to provide a makespan. The Moth Flame Optimization (MFO) algorithm was proposed to solve the flow shop problem. The MFO experiment was carried out with a combination of iteration parameters and the population of the MFO algorithm to solve the flow shop scheduling problem. The computational results showed that MFO could produce a better solution than the actual scheduling method. Furthermore, the MFO Proposal Algorithm was able to reduce the makespan by up to 3%.

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“…Work of Gupta and Stafford (2006) is a classical study of the problem. The latest results and literature review are presented in the works of Gupta (2012) and Lamoudan et al (2011).…”

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confidence: 99%

Solving Resource-Constrained Construction Scheduling Problems With Overlaps by Metaheuristic

Bożejko

Hejducki

Uchroński

et al. 2014

Journal of Civil Engineering and Management

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The paper concerns the problem of roadworks scheduling executed in the flow-shop system. Works may be performed parallelly with the acceleration (overlaps) of construction project, i.e. the following work on the assembly line can begin before the completion of the predecessor work. Taking into account the acceleration enables accurate modeling of complex real construction processes. The above fact can greatly shorten the time of realization of construction process which has a direct impact on reducing costs. The considered issue belongs to the class of NP-hard problems. We introduce the new: mathematical model, specific properties as an acceleration tools, as well as two new optimization algorithms for the problem considered: construction and tabu search. The execution of algorithms was illustrated on the example of a case study concerning the construction of roads. They were also verified on the examples taken from the literature and on already completed construction processes. The obtained results are fully satisfactory. The assigned execution times are close to optimal. The presented methods allow its practitioners (both the planners and the managers) to include in the model the acceleration of the works and the design of a much more efficient construction scheduling. The presented new scheduling method leads to a more competitive environment for contraction companies.

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Flow Shop Scheduling Problem with Transportation Times, Two-Robots and Inputs/Outputs with Limited Capacity

Abstract: In

Cited by 5 publications

References 23 publications

Metaheuristic Tuning Generalisation by Cross-Validated Racing

Metaheuristic Tuning Generalisation by Cross-Validated Racing

The Moth-Flame Optimization Algorithm for Flow Shop Scheduling Problem with Travel Time

Solving Resource-Constrained Construction Scheduling Problems With Overlaps by Metaheuristic

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