Multi-objective Particle Swarm Optimisation for Robust Dynamic Scheduling in a Permutation Flow Shop

Al-Behadili, Mohanad; Ouelhadj, Djamila; Jones, Dylan

doi:10.1007/978-3-319-53480-0_49

Cited by 8 publications

(9 citation statements)

References 27 publications

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“…This emphasises the importance of stability and robustness measures where these measures provide better robust and stable solutions. Moreover, the BRIG algorithm has been tested versus PSO algorithm that has been already applied for the dynamic PFSP in the presence of machine breakdown and new job arrival (Al-Behadili et al, 2017). This comparative study shows that the BRIG algorithm outperforms the PSO algorithm, also the computational time used by the BRIG algorithm to reach good quality solution is much less than the time consumed by the PSO algorithm.…”

Section: Resultsmentioning

confidence: 97%

“…The PSO algorithm is applied for the dynamic PFSP in the presence of machines breakdowns and new jobs arrivals in (Al-Behadili et al, 2017). The authors introduced the MSR model (1) and used a predictive-reactive based PSO approach.…”

Section: Comparison Between Brig and Pso Algorithmsmentioning

confidence: 99%

“…Thus, the three models are compared to show the impact and efficiency of models. Moreover, the BRIG based predictive-reactive approach is compared against the Particle Swarm Optimisation (PSO) based predictive-reactive approach (Al-Behadili et al, 2017) for same MSR model and same problem. The remainder of the paper is organised as follows; in section 2 a multi-objective optimisation model for the dynamic PFSP is presented.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective biased randomised iterated greedy for robust permutation flow shop scheduling problem under disturbances

Al-Behadili

Ouelhadj

Jones

2019

Journal of the Operational Research Society

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Nowadays, scheduling problems under different disruptions are a key to become competitive in the global market of this century. Permutation flow shop scheduling problems are very important as they consider one of the important types of scheduling problems. In this paper, we consider a challenging scheduling problem of a permutation flow shop in the presence of different types of real-time events such as new job arrival and machine breakdown. A multi-objective optimisation model that takes into account multiple performance measures in order to minimise the effect of different real-time events is used in this paper. To solve this problem, we apply the proposed multi-objective model and adapt a predictive-reactive based Biased Randomised Iterated Greedy approach for the problem, which is hybridised a Biased Randomisation process and the Iterated Greedy algorithm. Furthermore, the proposed approach is compared against the predictive-reactive based Particle Swarm Optimisation method for the same problem. Additionally, to show the efficiency of the proposed model, we compare this model by testing the predictive-reactive based Biased Randomised Iterated Greedy approach to two other models: the biobjective model that consider only two objectives and the classical single-objective model of minimising the makespan. Further statistical analysis is performed in this study by using an Analysis of Variance measure. The extensive experiments and statistical analysis demonstrate that the proposed multi-objective model is better than the other models in reducing the relative percentage deviation. Additionally, despite their simplicity, the Biased Randomised Iterated Greedy algorithm is shown to be State-of-the-art method that outperform the Particle Swarm Optimisation algorithm.

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Section: Resultsmentioning

confidence: 97%

Section: Comparison Between Brig and Pso Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective biased randomised iterated greedy for robust permutation flow shop scheduling problem under disturbances

Al-Behadili

Ouelhadj

Jones

2019

Journal of the Operational Research Society

Self Cite

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“…Moreover, Li et al [20] argued that the imperialist competition algorithm can solve the fuzzy distributed assembly flow shop scheduling problem. Furthermore, Al-Behadili et al [21] proposed a multi-objective optimization model and particle swarm optimization solution method for the robust dynamic scheduling of permutation flow shops with uncertainty. Likewise, Zhang [22] emphasized that DAPFSP is a new generalization of the distributed displacement flow shop scheduling problem and the assembly flow shop scheduling problem, proposed an enhanced population-based metaheuristic genetic algorithm, and designed an effective crossover strategy based on local search to accelerate convergence.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem

Zheng

Wang

2021

Applied Sciences

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In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the population diversity by classifying the populations, which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat’s velocity and location, solving the difficulty of the algorithm to trade-off exploration and mining capacity. Finally, the Gaussian learning strategy and elite learning strategy assist the whole population to jump out of the local optimal frontier. The simulation results demonstrate that the algorithm proposed in this paper can well solve the DAPFSP. In addition, compared with other metaheuristic algorithms, IHBA has better performance and gives full play to its advantage of finding optimal solutions.

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“…Due to its importance, many scholars paid much attention on FJDSP and established some dynamic scheduling models, including: genetic algorithm-based dynamic scheduling [ 4 ], particle swarm algorithm-based dynamic scheduling [ 5 ] and simulated annealing algorithm-based dynamic scheduling [ 6 ] and so on. However, the survey found that although many machining companies in China have deployed MES, they have not used the scheduling functions, and have only used resource management and monitoring modules.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Dynamic Scheduling Model of Flexible Job Shop Based on NSGAII Algorithm and Scroll Window Technology

Wang

2020

Lecture Notes in Computer Science

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The production process is often accompanied by a lot of disturbances, which make it difficult for flexible job shop to execute production according to the original job plan. It is necessary to dynamically adjust the production plan according to real-time conditions. To this end, this paper proposes a multi-objective dynamic scheduling model. In this model, scroll window technology and NSGAII algorithm is adopted to adapt the dynamic production evironment. A specific chromosome retention strategy and a variable objective selection mechanism are designed to ensure that the proposed model can select different objectives according to different disturbance events to solve the optimal solution. Finally, a case test is used to verify the feasibility and effectiveness of the model.

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Multi-objective Particle Swarm Optimisation for Robust Dynamic Scheduling in a Permutation Flow Shop

Cited by 8 publications

References 27 publications

Multi-objective biased randomised iterated greedy for robust permutation flow shop scheduling problem under disturbances

Multi-objective biased randomised iterated greedy for robust permutation flow shop scheduling problem under disturbances

A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem

Multi-objective Dynamic Scheduling Model of Flexible Job Shop Based on NSGAII Algorithm and Scroll Window Technology

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