Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

Nouri, Houssem Eddine; Driss, Olfa Belkahla; Ghédira, Khaled

doi:10.1007/s40092-017-0204-z

Cited by 45 publications

(20 citation statements)

References 35 publications

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“…If the value of dev 2 is negative, the solution obtained by the IGA-AVNS is better. Table 4 shows the comparison results between IGA-AVNS and the following algorithms: the Genetic Algorithm (GA) proposed by Pezzella et al [17], the Genetic Algorithm Combined with Tabu Search in a Holonic Multiagent model (GATS + HM) proposed by Nouri et al [5], the hybrid evlolutionary agorithm (HEA) proposed by Wang et al [10], and the neighborhood-based genetic algorithm (NGA) proposed by Driss et al [7]. The data in the table are from the corresponding literature.…”

Section: Simulation and Analysismentioning

confidence: 99%

“…We select 14 instances of the dataset considering a number of jobs ranging from 10 to 15 with a number of operations for each job ranging from 10 to 15, which will be processed on a number of machines ranging from 11 to 18. Table 5 shows the comparison results between the IGA-AVNS and GATS + HM [5] proposed by Nouri et al In Table 5, for the value of dev 1 , the smaller the value, the closer to LB. For the value of dev 2 , 0 indicates that the two algorithms get the same solution, a positive value indicates that the solution obtained by GATS + HM is better, and if the value is negative, the solution obtained by IGA-AVNS is better.…”

Section: Simulation and Analysismentioning

confidence: 99%

“…A hybrid non-dominated sorting simulated annealing algorithm for FJSP was proposed by Shivasankaran et al [4], in which critical or incapable machines were eliminated by non-dominated sorting for all operations and simulated annealing was used to search for an optimal solution. Nouri et al [5] solve the flexible job-shop problem with a multi-agent model based on mixed-element heuristics. Zhao et al [6] proposed a bilevel neighborhood search hybrid algorithm to solve FJSP, which achieved remarkable results.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Huang

2019

Algorithms

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For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.

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Section: Simulation and Analysismentioning

confidence: 99%

Section: Simulation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Huang

2019

Algorithms

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“…Therefore, the authors had proposed hybrid meta-heuristics based on clustered holonic multiagent model to solve the above complex problem. While Nouri et al (2018) proposed hybrid meta-heuristic-based multi-agent model for solving flexible job-shop scheduling problem in which neighbourhood-based genetic algorithm (NGA) is used for global exploration of the search. Erol et al (2012) proposed agent-based approach for real-time scheduling of machines and automated guided vehicles in production system.…”

Section: The Concept Of Agent Technology In Manufacturing Systemsmentioning

confidence: 99%

Analysis of critical drivers affecting implementation of agent technology in a manufacturing system

et al. 2018

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Technological advancement in the manufacturing system in current scenario is inevitable due to today's customer-driven and volatile nature of the market. Implementation of agent technology in a manufacturing system increases flexibility which handles uncertainty generated due to advance technology. Therefore, in this paper, the critical drivers affecting implementation of agent technology are identified and the relationships among them are analysed for a case study of a manufacturing system in an Indian steering wheel manufacturing company. Interpretive structural modelling (ISM) is used to provide binary relationships among identified critical drivers (CDs), while MICMAC approach describes sensitive analysis of driving and dependence behaviour of CDs. The classification of the drivers affecting agent technology and their relationships according to ISM-MICMAC approach provides importance to this study. A structural model is developed for providing rank to the identified critical drivers, and driving-dependent power diagram is presented for analysing the behaviour of different critical drivers with respect to others. The identification of the most influential CDs that lead to increase the effect of other drivers is the major finding of this study. Finally, the implication of this research for the industries is also described.

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“…Maleki-Darounkolaei et al 2012, Noori-Darvish and Tavakkoli-Moghaddam (2012), Mirabi et al (2014) and Kia et al (2017) worked on sequence-dependent scheduling problems. Nouri et al (2017) proposed a holonic multiagent model to solve FJSP.…”

Section: Introductionmentioning

confidence: 99%

An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method

Buddala

Mahapatra

2018

J Ind Eng Int

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In this paper, teaching-learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic jobshop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, then the FJSP comes under integrated approach. Otherwise, it becomes a hierarchical approach. Very less research has been done in the past on FJSP problem as it is an NP-hard (non-deterministic polynomial time hard) problem and very difficult to solve till date. Further, very less focus has been given to solve the FJSP using an integrated approach. So an attempt has been made to solve FJSP based on integrated approach using TLBO. Teaching-learning-based optimization is a meta-heuristic algorithm which does not have any algorithm-specific parameters that are to be tuned in comparison to other meta-heuristics. Therefore, it can be considered as an efficient algorithm. As best student of the class is considered as teacher, after few iterations all the students learn and reach the same knowledge level, due to which there is a loss in diversity in the population. So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain diversity, respectively, in the population. Tests have been carried out on all Kacem's instances and Brandimarte's data instances to calculate makespan. Results show that TLBO outperformed many other algorithms and can be a competitive method for solving the FJSP.

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Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

Cited by 45 publications

References 35 publications

An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Analysis of critical drivers affecting implementation of agent technology in a manufacturing system

An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method

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