An effective fireworks algorithm for warehouse-scheduling problem

Ye, Sengang; Ma, Haiping; Xu, Shiyi; Yang, Wei; Fei, Minrui

doi:10.1177/0142331215600047

Cited by 21 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…"Machine" denotes the machine number index, "Route" denotes the route number index, and "Scheduling orders" denote the scheduling orders that one machine implements in one route. and swarm intelligence algorithms, for example, fireworks algorithm (FWA) [38,39], brain storm optimization (BSO) algorithm [40,41], teaching-learning based optimization (TLBO) algorithm [42][43][44], and Jaya optimization algorithm [45]. Second, in this paper, we do not consider the communication between subpopulations.…”

Section: Functionmentioning

confidence: 99%

Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems

et al. 2018

Complexity

Self Cite

View full text Add to dashboard Cite

Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpopulations during the evolution process for a given problem, which may significantly affect optimization ability of EAs. This paper proposes a simple multipopulation management strategy to dynamically adjust the subpopulation number in different evolution phases throughout the evolution. The proposed method makes use of individual distances in the same subpopulation as well as the population distances between multiple subpopulations to determine the subpopulation number, which is substantial in maintaining population diversity and enhancing the exploration ability. Furthermore, the proposed multipopulation management strategy is embedded into popular EAs to solve real-world complex automated warehouse scheduling problems. Experimental results show that the proposed multipopulation EAs can easily be implemented and outperform other regular single-population algorithms to a large extent.

show abstract

Section: Functionmentioning

confidence: 99%

Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems

et al. 2018

Complexity

Self Cite

View full text Add to dashboard Cite

show abstract

“…The algorithm is simple, strong randomness, high accuracy and global convergence. Since the fireworks explosion algorithm was proposed, many scholars have tried to combine it with their respective fields to solve various optimization problems [7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Solving degree constrained minimum spanning tree problem based on improved fireworks algorithm

Zun-hai

Gui

Liu

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In the iterative process of fireworks algorithm, an improved algorithm is proposed to improve the local search ability of optimal fitness value and make it more suitable for solving discrete problems, so as to obtain a method to find degree constrained minimum spanning tree. Firstly, the corresponding mathematical model is constructed by analyzing the degree constrained minimum spanning tree, then the explosion operator and the corresponding fitness function are established according to the model, and finally the algorithm is improved to adapt to the discrete fireworks algorithm, so as to achieve the purpose of seeking the degree constrained minimum spanning tree. The example calculation shows that the improved fireworks algorithm can reduce the number of iterations and achieve high accuracy.

show abstract

“…Industry 4.0, a new wave of industrialization, driven by smart information and communication technology, sparks a transformative view of framing process manufacturing and factory management practice [1][2][3]. With the recent technical advances in industry, such as sensor network, cloud computing, and artificial intelligence, production manufacturing is becoming hyperconnected because of the communication and control between more components and parts than those in the past [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multisystem Optimization for an Integrated Production Scheduling with Resource Saving Problem in Textile Printing and Dyeing

Sun

Wang

et al. 2020

Complexity

Self Cite

View full text Add to dashboard Cite

Resource saving has become an integral aspect of manufacturing in industry 4.0. This paper proposes a multisystem optimization (MSO) algorithm, inspired by implicit parallelism of heuristic methods, to solve an integrated production scheduling with resource saving problem in textile printing and dyeing. First, a real-world integrated production scheduling with resource saving is formulated as a multisystem optimization problem. Then, the MSO algorithm is proposed to solve multisystem optimization problems that consist of several coupled subsystems, and each of the subsystems may contain multiple objectives and multiple constraints. The proposed MSO algorithm is composed of within-subsystem evolution and cross-subsystem migration operators, and the former is to optimize each subsystem by excellent evolution operators and the later is to complete information sharing between multiple subsystems, to accelerate the global optimization of the whole system. Performance is tested on a set of multisystem benchmark functions and compared with improved NSGA-II and multiobjective multifactorial evolutionary algorithm (MO-MFEA). Simulation results show that the MSO algorithm is better than compared algorithms for the benchmark functions studied in this paper. Finally, the MSO algorithm is successfully applied to the proposed integrated production scheduling with resource saving problem, and the results show that MSO is a promising algorithm for the studied problem.

show abstract

An effective fireworks algorithm for warehouse-scheduling problem

Cited by 21 publications

References 39 publications

Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems

Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems

Solving degree constrained minimum spanning tree problem based on improved fireworks algorithm

Multisystem Optimization for an Integrated Production Scheduling with Resource Saving Problem in Textile Printing and Dyeing

Contact Info

Product

Resources

About