Tailoring Job Shop Scheduling Problem Instances Through Unified Particle Swarm Optimization

Vela, Alonso; Cruz–Duarte, Jorge M.; Ortíz-Bayliss, José Carlos; Amaya, Iván

doi:10.1109/access.2021.3076426

Cited by 9 publications

(9 citation statements)

References 44 publications

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“…Moreover, only one operation of a given job can be performed simultaneously. We also adopt the definition of a JSSP that considers an operation as the building block of a job [34]. Besides, and to make things easier for the reader, we have summarized the main symbols required for dealing with a JSSP in Table 1.…”

Section: Fundamentals a Job Shop Scheduling Problemmentioning

confidence: 99%

“…So, in this work, we aim to fill such a knowledge gap by proposing a more general selection hyper-heuristic model, which can accommodate other hyper-heuristics. We then exploit a previously proposed instance generator that allows for the creation of instances where simple heuristics excel or fail [34]. The idea of such an instance generator was adopted from [35].…”

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confidence: 99%

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Beyond Hyper-Heuristics: A Squared Hyper-Heuristic Model for Solving Job Shop Scheduling Problems

et al. 2022

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Hyper-heuristics (HHs) stand as a relatively recent approach to solving optimization problems. There are different kinds of HHs. One of them deals with how low-level heuristics must be combined to deliver an improved solution to a set of problem instances. Literature commonly refers to them as selection hyper-heuristics. One of their advantages is that the strengths of each heuristic can be fused into a highlevel solver. However, one of their drawbacks is that sometimes this generalization scheme does not suffice. Additionally, it is not easy to reuse these HHs since the model cannot be easily tweaked. So, in this work, we develop a hyper-heuristic model with an additional layer of generalization. The rationale behind it is to preserve the general structure of selecting an adequate solver for a particular situation but to use HHs instead of low-level heuristics. We call this model a Squared Hyper-Heuristic (SHH). To validate our proposal, we pursue a four-stage methodology that covers several testing scenarios. Our data reveal that, under proper conditions, our model can outperform the base HHs. Moreover, it is flexible enough to allow for an increased number of layers so that the complexity of the final model can be tuned. Additionally, different kinds of instances can be used to train each stage of the model, thus setting the groundwork for developing a transfer learning approach for hyper-heuristics.

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Section: Fundamentals a Job Shop Scheduling Problemmentioning

confidence: 99%

mentioning

confidence: 99%

Beyond Hyper-Heuristics: A Squared Hyper-Heuristic Model for Solving Job Shop Scheduling Problems

et al. 2022

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“…Hence, we have renamed them with a code associated to the ID the authors used in their work. A full description of such features can be found in [38]. As with heuristics, the user is free to implement additional features within the framework.…”

Section: Software Architecturementioning

confidence: 99%

MatHH: A Matlab-based Hyper-Heuristic framework

2022

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“…COPs include vehicle routing problems 13 , job shop scheduling problems 14 , packing problems 15 and problems in other application elds. The COP of jamming resource allocation is a kind of packing problem and is also an NP-hard problem.…”

Section: Introductionmentioning

confidence: 99%

A Study of Jamming Resource Allocation Based on a Hyperheuristic Framework

Hao

Wang

2022

Preprint

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There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. To solve this problem, this study proposes an improved genetic selection electronic warfare operator hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the hyperheuristic (HH) framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four electronic warfare operators (EWOs) based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed.

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Tailoring Job Shop Scheduling Problem Instances Through Unified Particle Swarm Optimization

Cited by 9 publications

References 44 publications

Beyond Hyper-Heuristics: A Squared Hyper-Heuristic Model for Solving Job Shop Scheduling Problems

Beyond Hyper-Heuristics: A Squared Hyper-Heuristic Model for Solving Job Shop Scheduling Problems

MatHH: A Matlab-based Hyper-Heuristic framework

A Study of Jamming Resource Allocation Based on a Hyperheuristic Framework

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