A hybrid artificial bee colony algorithm for a flexible job shop scheduling problem with overlapping in operations

Meng, Tao; Pan, Quan-Ke; Sang, Hongshi

doi:10.1080/00207543.2018.1467575

Cited by 63 publications

(25 citation statements)

References 47 publications

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“…Compared to heuristic algorithms, meta-heuristics can achieve better solutions at the cost of additional computation time [25,26]. Regarding the meta-heuristics, Atighehchian,et al [27] combined ant colony optimization and iterative algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

et al. 2020

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Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

et al. 2020

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“…Furthermore, Gao et al [149] addressed an improved ABC algorithm to solve the FJSP with fuzzy processing time whose objective is to minimise the maximum fuzzy completion time and the maximum fuzzy machine workload. Meng et al [150] presented a hybrid ABC (hyABC) algorithm to minimise the total flowtime for the FJSP with overlapping in operations. In the proposed hyABC, a dynamic scheme is introduced to fine-tune the search scope adaptively.…”

Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Review on flexible job shop scheduling

Xie

Gao

Peng

et al. 2019

IET Collaborative Intelligent Manufacturing

156

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Flexible job shop scheduling problem (FJSP) is an NP‐hard combinatorial optimisation problem, which has significant applications in the real world. Due to its complexity and significance, lots of attentions have been paid to tackle this problem. In this study, the existing solution methods for the FJSP in recent literature are classified into exact algorithms, heuristics and meta‐heuristics, which are reviewed comprehensively. Moreover, the real‐world applications of the FJSP are also introduced. Finally, the development trends of the manufacturing industry are analysed, and the future research opportunities of the FJSP are summarised in detail.

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“…In recent years, many new swarm intelligence methods have been developed to solve production scheduling problems. These methods include Particle Swarm Optimization (PSO) [50], Ant Colony Optimization (ACO) [51], Artificial Immune Algorithm (AIA) [52], Artificial Bee Colony Algorithm (ABC) [53], Grey Wolf Optimization (GWO) [54], Harmony search algorithm (HS) [55], Shuffled Frog-Leaping Algorithm (SFLA) [56], Firefy Algorithm (FA) [57], Fruit fly optimization (FOA) [58]. Nouiri et al [59] proposed an effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem.…”

Section: Meta-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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A hybrid artificial bee colony algorithm for a flexible job shop scheduling problem with overlapping in operations

Cited by 63 publications

References 47 publications

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

Review on flexible job shop scheduling

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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