Exploring Reward-based Hyper-heuristics for the Job-shop Scheduling Problem

Lara-Cárdenas, Erick; Arturo, S Bretas; Ortíz-Bayliss, José Carlos; Amaya, Iván; Cruz–Duarte, Jorge M.; Terashima-Marín, Hugo

doi:10.1109/ssci47803.2020.9308131

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…The JSSP is an NP-hard problem in which using exact methods is out of the question [29]. Because of this, approximated solvers have evolved and adapted.…”

Section: Fundamentals a Job Shop Scheduling Problemmentioning

confidence: 99%

“…In their proposal, the model distributes instances to a set of solvers, thus pursuing a divide and conquer approach. Moreover, Lara-Cardenas et al developed a reward-based model so that rules can keep on evolving [29]. Other recent works include the analysis of problems with scenario-based processing times [30] and with more complex constraints [31], as well as problems closer to reality [32].…”

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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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“…The JSSP is an NP-hard problem in which using exact methods is out of the question [29]. Because of this, approximated solvers have evolved and adapted.…”

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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“…Conversely, one may just lay out a sequence of such actions and optimize said sequence [13]. Or one could resort to ideas from diverse fields, such as those from reinforcement learning [14,15], or others [16][17][18][19][20][21]. Of course, there are other kinds of HHs that do not select among existing solvers, such as those that generate a new solver altogether [10,11,22].…”

Section: Motivation and Significancementioning

confidence: 99%

MatHH: A Matlab-based Hyper-Heuristic framework

2022

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“…We are aware that many different types of selection HHs are described in the literature [47]. For example, there are HHs that work on batches of instances [48] and HHs that decide the next heuristic based on a reward/penalty strategy [49], [50] or on a threshold acceptance criterion [51], [52]. Among the different HHs described in the literature, two types are of particular interest for this work: rule-based HHs and sequence-based ones [3].…”

Section: B Hyper-heuristics (Hhs)mentioning

confidence: 99%

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

et al. 2021

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Combinatorial optimization problems (COPs) are paramount for real-life problems with discrete variables. Even though the number of combinations is finite, some problems exhibit exponential growth, rendering exact approaches unfeasible. So, approximate methods, such as heuristics, are customary for making fast decisions. Despite their small computational cost, each heuristic specializes in specific kinds of problems. Hence, other approaches have appeared for merging their strengths. One of these approaches is commonly known as selection hyper-heuristics. However, a trained hyper-heuristic usually carries the following problem: it provides scarce information about its sensitivity. This makes it more difficult to estimate the effect of changing a parameter. Illumination algorithms seek to improve this issue by focusing on exploration rather than on exploitation while preserving information about the best solutions with different criteria. Still, literature falls short when merging both approaches, representing a knowledge gap. Therefore, in this work, we test the feasibility of using an illumination algorithm, the MAP-Elites (ME), for tuning a sequence-based selection hyper-heuristic model for Balanced Partition problems. We choose ME since it has been successfully applied to a different COP, and by linking it with hyper-heuristics, we may exploit upon its benefits. So, we may achieve a hyper-heuristic that represents the best combination of heuristics. Simultaneously, we can gain information about the expected performance of diverse hyperheuristics, e.g., those that begin with a different heuristic. Our approach operates by creating a multidimensional map, where each design variable represents the application of a given heuristic. Afterward, ME generates mutated sequences and tests them to determine if they represent a better-performing solution. To test our approach, we consider 1500 instances that include easy and hard instances, analyzed under different scenarios. We also include limit instances that are neither easy nor hard. Our resulting data supports the feasibility of the proposed approach. We show that this kind of hyper-heuristic can perform toe-to-toe with a synthetic oracle. Besides, under some conditions, the former may even outperform the latter. This represents an outstanding result, since such an oracle can only be obtained by a brute-force approach, and because it demonstrates that merging both approaches (ME and hyper-heuristics) is a path worth pursuing. We also present how each parameter affects the model performance, concluding that some parameters can be critical while others are virtually irrelevant. This is also a relevant result, since it serves as the groundwork for future works so that they can focus on exploiting the most relevant parameters.

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Exploring Reward-based Hyper-heuristics for the Job-shop Scheduling Problem

Cited by 9 publications

References 37 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

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

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