A survey of dispatching rules for the dynamic unrelated machines environment

Đurasević, Marko; Jakobović, Domagoj

doi:10.1016/j.eswa.2018.06.053

Cited by 100 publications

(39 citation statements)

References 26 publications

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“…Hence, it is convenient to use this method for practical application. However, dispatching is a one-pass algorithm without iterations, it is well known that there is no one dispatching rule can beat any other rules in all scenarios and dispatching rules do not guarantee to find global optimal result [27]. According to the computational complexity theory [28], a large-scale complex scheduling problem belongs to NP-Complete problem.…”

Section: B Heuristicsmentioning

confidence: 99%

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

2019

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In the paper, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this paper involves many attributes, including working calendar, due dates, and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for the online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of the practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm, and improve the solution quality. To this end, three multi-agentbased hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for the online scheduling problem. To evaluate the performance of evolved SPs, a 5fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent-based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multiobjective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded in the manufacturing execution system that is built on JAVA and successfully applied in several manufacturing plants. INDEX TERMSScheduling, flexible job shop, multi-agent, hyper-heuristics, genetic programming. NOMENCLATURE NSGAII Nondominated sorting genetic algorithm II. SPEA2 Strength Pareto evolutionary algorithm 2. 2/3/MPGP Multi-objective cooperative coevolution genetic programming with two/three/multiple sub-populations. The associate editor coordinating the review of this manuscript and approving it for publication was Kuo-Ching Ying. 2/3/MTGP Multi-objective genetic programming with single population that an individual contains two/three/multiple sub-trees. OMOPSO Optimized multi-objective particle swarm optimization. SMPSO Speed-constrained multi-objective particle swarm optimization. MAHH Multi-agent based hyper-heuristics.

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Section: B Heuristicsmentioning

confidence: 99%

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

2019

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“…In addition of being one of the most common methods adopted for scheduling, there is a variety of dispatching rules acknowledged for a diversity of scheduling criteria. Reference [30] did a survey related to the use of dispatching rules for scheduling, presenting 26 distinct dispatching rules used for the most variable purposes. Some of these purposes include for example the use of dispatching rules for minimizing completion or execution times, maximizing the standard deviation or for a Just-in-time production.…”

Section: Approaches To Deal With the Jspmentioning

confidence: 99%

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

et al. 2018

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Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

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“…However, they cannot effectively handle dynamic environment, which requires real-time response. Scheduling heuristics such as priority-based dispatching rules [18], are the most popularly used heuristics for DFJSS. Scheduling heuristics are used to make real-time decisions based on the priority values of machines or operations at the decision points.…”

Section: Introductionmentioning

confidence: 99%

Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

Zhang

Mei

Nguyen

et al. 2021

IEEE Trans. Cybern.

170

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Dynamic flexible job shop scheduling (DFJSS) is a challenging combinational optimisation problem that takes the dynamic environment into account. Genetic programming hyperheuristics (GPHH) have been widely used to evolve scheduling heuristics for job shop scheduling. A proper selection of the terminal set is a critical factor for the success of GPHH. However, there is a wide range of features that can capture different characteristics of the job shop state. Moreover, the importance of a feature is unclear from one scenario to another. The irrelevant and redundant features may lead to performance limitations. Feature selection is an important task to select relevant and complementary features. However, little work has considered feature selection in GPHH for DFJSS. In this paper, a novel two-stage GPHH framework with feature selection is designed to evolve scheduling heuristics only with the selected features for DFJSS automatically. Meanwhile, individual adaptation strategies are proposed to utilise the information of both the selected features and the investigated individuals during the feature selection process. The results show that the proposed algorithm can successfully achieve more interpretable scheduling heuristics with fewer unique features and smaller sizes. In addition, the proposed algorithm can reach comparable scheduling heuristic quality with much shorter training time.

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A survey of dispatching rules for the dynamic unrelated machines environment

Cited by 100 publications

References 26 publications

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

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