A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity

Li, Dongni; Zhan, Rongxin; Zheng, Da-Zhong; Li, Miao; Kaku, Ikou

doi:10.1109/tase.2015.2470080

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…Recent papers also started to investigate feature selection to determine which features are relevant for creating new DRs (Ingimundardottir and Runarsson 2016;Mei et al 2016). GP was recently used to generate DRs for scheduling problems which were not considered until now, like the dual-constrained flow shop scheduling problem (Branke et al 2016a) and the intercell scheduling problem (Li et al 2016). A recent survey conducted by Branke et al (2016b) gives a detailed overview of the literature concerned with automatic creation of DRs by the use of GP.…”

Section: State Of the Artmentioning

confidence: 99%

Creating dispatching rules by simple ensemble combination

Đurasević

Jakobović

2019

J Heuristics

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Dispatching rules are often the method of choice for solving scheduling problems since they are fast, simple, and adaptive approaches. In recent years genetic programming has increasingly been used to automatically create dispatching rules for various scheduling problems. Since genetic programming is a stochastic approach, it needs to be executed several times to ascertain that good dispatching rules were obtained. This paper analyses whether combining several dispatching rules into an ensemble leads to performance improvements over the individual dispatching rules. Two methods for creating ensembles of dispatching rules, based on the sum and vote methods applied in machine learning, are used and their effectiveness is analysed with regards to the size of the ensemble, the genetic programming method used to generate the dispatching rules, the size of the evolved dispatching rules, and the method used for creating the ensembles. The results demonstrate that the generated ensembles achieve significant improvements over individual automatically generated dispatching rules.

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Section: State Of the Artmentioning

confidence: 99%

Creating dispatching rules by simple ensemble combination

Đurasević

Jakobović

2019

J Heuristics

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“…Secondly, FOA has no other additional control parameters besides the maximum number of iteration and population size. Therefore, FOA has widely attracted the attention of many researchers from various fields, including path planning [16,17], flowshop rescheduling [18], multi-dimensional knapsack problem [19], parameter identification [20], controller parameter tuning [21], etc.…”

Section: Introductionmentioning

confidence: 99%

“…FOA has simple operation and acceptable searching capability, however, it also has some shortcomings of the premature convergence and slow convergent speed during the later evolution process. To overcome these shortcomings as well as improve the search efficiency and global search capability, various improved FOA variants have been put forward by researchers [1,16,[18][19][20][22][23][24][25][26]. The improved fruit fly optimization (IFFO) algorithm [1] introduces a new parameter to adaptively control the search scope around the fruit fly swarm location and a new solution generating method to enhance the accuracy and convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

Zhang¹,

Xia²,

Li³

2020

IJCIS

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As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper. When establishing the quantum Delta potential well around the location of fruit fly swarm, QFOA introduces the quantum behavior-based searching mechanism into the original osphresis-based search procedure of FOA. In the process that fruit flies find and move toward the food source, fruit flies follow the wave function property of the Delta potential well rather than the Newtonian mechanics. Taking advantage of the probability and uncertainty of quantum theory, the proposed QFOA can effectively overcome the weakness in premature convergence and easy trapping into local optimum. Since there are two popular models of the basic FOA, this paper also develops two corresponding QFOAs. Experimental results on various benchmark functions show that both the two QFOA models has overall better performance compared with the basic FOA as well as other FOA variants and other well-known optimization algorithms. In addition, the proposed QFOAs are also applied to unmanned aerial vehicle (UAV) path planning problem in the three-dimensional environment, and comparative results about the obtained optimal flight path and population convergence process show the effectiveness of QFOAs.

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“…With the change of orders, routes and other elements in the shop-floor, the previously established rules may not be able to adapt to new scheduling scenarios. Therefore, it is necessary to implement hyper-heuristics to further enhance the heuristics made by experts [11].…”

Section: Introductionmentioning

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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A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity

Cited by 12 publications

References 25 publications

Creating dispatching rules by simple ensemble combination

Creating dispatching rules by simple ensemble combination

Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

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

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