Toward evolving dispatching rules for dynamic job shop scheduling under uncertainty

Karunakaran, Deepak; Mei, Yi; Chen, Gang; Zhang, Mengjie

doi:10.1145/3071178.3071202

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…To further enhance the performance of the generated DRs, several studies analysed how different ensemble learning methods could be used to create ensembles of DRs, which can perform better than a single DR [34]- [38]. Other studies focused on scheduling with uncertainties in which some problem parameters were not deterministic [39]- [41], or on applying surrogate assisted GP to reduce the computational cost of evolving new DRs [42]- [44]. A framework for visualisation of the evolutionary process in order to better understand the process of evolving DRs was proposed in [45].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Dispatching Rule Representations in Evolutionary Algorithms for the Dynamic Unrelated Machines Environment

et al. 2022

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Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time consuming process, it can be automatised by using various machine learning and evolutionary computation methods. In previous research, genetic programming was the most predominantly used method for the automatic design of new dispatching rules. However, there are many other evolutionary methods which use different representations than genetic programming, that can be used for generating dispatching rules. Some, like gene expression programming, were already successfully applied, while others like Cartesian genetic programming or grammatical evolution, were not used to generate dispatching rules. This paper will test six different methods (genetic programming, gene expression programming, Cartesian genetic programming, grammatical evolution, stack representation and analytic programming) to generate new dispatching rules for the unrelated machines environment, and will analyse how the tested methods perform on various scheduling criteria. The paper also analyses how different individual sizes in the tested methods affect the performance and average size of the generated dispatching rules. The results indicate that, except for the grammatical evolution and analytic programming, all tested methods perform quite similar, with the results depending on selected scheduling criterion. The results also demonstrate that Cartesian genetic programming was the most resistant to the occurrence of bloat, and that it evolved dispatching rules of the smallest average sizes.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Dispatching Rule Representations in Evolutionary Algorithms for the Dynamic Unrelated Machines Environment

et al. 2022

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“…a machine failure). Since the scheduling process must respond to these anomalies, this approach is named reactive scheduling [5]. A number of heuristic approaches has been proposed to find a quasi-optimal schedule in reasonable time for DJSP [6].…”

Section: Introductionmentioning

confidence: 99%

Implementing Digital Twins of Smart Factories with Interval Algebra

Dziurzański

Swan

Indrusiak

et al. 2019

2019 IEEE International Conference on Industrial Technology (ICIT)

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This paper presents an interval algebra that can be used to create an analytic description of a smart factory. Such a description (recently termed a 'digital twin' of the factory) is used to evaluate alternative manufacturing configurations as part of a search-based optimisation process. Several extensions are proposed to the interval algebra for specifying smart factory production line details. A number of real-life manufacturing scenarios are described, related to Wire-cut Electrical Discharge Machining. The experimental results show the applicability and scalability of the proposed method.

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“…p j,i is known only at the time of realization on the machine. Also p j,i > p j,i is a practical assumption which is supported by the relationship below [117]:…”

Section: Experiments Designmentioning

confidence: 94%

“…We repeat some of the important details for clarity. The job arrival follows a Poisson process with λ = 0.85 [117]. This assumption has been used in large number of works [34,158,162].…”

Section: Experiments Designmentioning

confidence: 99%

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Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment

Karunakaran¹

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<p>Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolutionary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic toward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling problem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse-grained model of parallel evolutionary approach for GPHH for multi-objective scheduling problems.</p>

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Toward evolving dispatching rules for dynamic job shop scheduling under uncertainty

Cited by 12 publications

References 21 publications

A Comparative Study of Dispatching Rule Representations in Evolutionary Algorithms for the Dynamic Unrelated Machines Environment

A Comparative Study of Dispatching Rule Representations in Evolutionary Algorithms for the Dynamic Unrelated Machines Environment

Implementing Digital Twins of Smart Factories with Interval Algebra

Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment

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