Solving dual flexible job-shop scheduling problem using a Bat Algorithm

Xu, Han; Bao, Zhenqiang; Zhang, T.

doi:10.14743/apem2017.1.235

Cited by 22 publications

(10 citation statements)

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“…To overcome the disadvantage of low efficiency in convergence speed, Wu and Wu [128] developed an elitist quantum-inspired evolutionary algorithm to solve the FJSP. Xu et al [129] proposed an improved bat algorithm to solve the FJSP with a new encoding strategy. Wang et al [130] developed an improved ant colony optimisation with high computational efficiency to solve the FJSP with makespan criterion.…”

Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Review on flexible job shop scheduling

Xie

Gao

Peng

et al. 2019

IET Collaborative Intelligent Manufacturing

158

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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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Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Review on flexible job shop scheduling

Xie

Gao

Peng

et al. 2019

IET Collaborative Intelligent Manufacturing

158

View full text Add to dashboard Cite

show abstract

“…Swarm intelligence (SI) algorithms mainly include ant colony optimization (ACO), particle swarm optimization (PSO) algorithm, and artificial bee colony (ABC). Xu et al [14] used bat algorithm to solve a dual flexible job shop problems (DFJSP). That algorithm used crossover and mutation as well as an adjusted value of the inertia weight with a linear decreasing strategy to enforce the search ability of the algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel

Gao

2019

Chin. J. Mech. Eng.

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The flexible job shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them difficult to code and not easy to reproduce. This paper proposes a modified iterated greedy (IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an effective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

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“…In general, any other search strategy can be used, such as particle swarm optimization, simulated annealing, bat algorithm, etc. [33][34][35]. In our case, during simulated evolution the organisms in form of geometric entries and their locations in the stock piece (i.e., solutions to the problem) undergo adaptation.…”

Section: Search Strategymentioning

confidence: 99%

A Universal CAD System for Cutting Stock Problem

Kovačić¹,

Brezočnik²

2017

Int. j. simul. model.

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In this research the universal system using CAD geometry for solving of cutting stock problem is proposed. The system consists of the three main modules: geometry definition module, objective definition module, and the search strategy module. In the first module the reference points and orientation of parts and stock are defined. In the second module some constraints such as the quantity of the desired type of parts to be placed on the stock can be taken into account. The third module contains the search strategy. For the purpose of this research, the genetic algorithm was used as the search method. For illustration of the generality of the proposed approach, three practical examples for solving orthogonally and irregularly-shaped cutting stock problems are presented. The parts which are to be cut from stock-material with minimal loss and also the stock-material itself are presented as CAD geometries without any kind of geometrical constraints (i.e., concavity, convexity, sections, and holes). In the presented approach the AutoCAD environment was used. The AutoLISP in-house developed system for cutting stock problem was integrated into the AutoCAD. The developed system is without any kind of geometrical constraints and thus highly applicable in the practice. The presented approach can be developed also in other CAD systems where the application programming interfaces (API) are available.

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Solving dual flexible job-shop scheduling problem using a Bat Algorithm

Cited by 22 publications

References 0 publications

Review on flexible job shop scheduling

Review on flexible job shop scheduling

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

A Universal CAD System for Cutting Stock Problem

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