A Two-Phase Hybrid Particle Swarm Optimization Algorithm for Solving Permutation Flow-Shop Scheduling Problem

Huang, Ko-Wei; Yang, Chu-Sing; Tsai, Chun-Wei

doi:10.5120/7311-9883

Cited by 5 publications

(12 citation statements)

References 36 publications

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“…To maximize output, Zhao and Guo 24 proposed a branch and bound for optimizing simultaneously job scheduling and robotic moved sequences of automatic production system scheduling problems. Zhao and Guo 25 developed effective chemical reaction optimization for optimizing big-scale cases.…”

Section: Reference Reviewmentioning

confidence: 99%

“…To maximize production efficiency, the references [24][25][26][27][28] focus on designing optimization algorithm for optimizing different scenarios of the automated production system, but the due date was not considered. Wang et al 29 proposed a mixed integer programming model for automatic production system scheduling problems with parallel tanks under justin-time environment.…”

Section: Reference Reviewmentioning

confidence: 99%

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Research on solution property of automatic production system scheduling problem under just-in-time environment

Li,

Zhao,

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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Automatic production system is a kind of artificially intelligent technology. To improve the intelligence of automatic production systems and easily implement simulation experiments, the solution properties of automatic production system scheduling problems under the JIT environment are researched. Firstly, to minimize total earliness/tardiness time, a mixed integer programming of the proposed problem is shown for simultaneously optimizing job scheduling and robotic move sequence. Secondly, to combine job scheduling and robot move sequence, the definition of robotic activity is applied, therefore, two-dimension scheduling problem can be transferred to one dimension scheduling problem. Thirdly, the robotic activity sequence represents the solution to the proposed problem. and solution definition is presented. Finally, the properties of the solution are discussed. The first property means the first two robotic activities and the last two robotic activities of the solution move the first job and the last job of job scheduling, respectively. That is to say, when job scheduling is given, the first two robotic activities and the last two robotic activities of the solution are determined. Thus the number of solutions is reduced from n(m+1)! to [n(m+1)-4]!. Solution space is reduced. The second property means the start moving time of the first robotic activity of each solution is non-negative. The third property and the fourth property show the relation of adjacent tanks. Robotic activity exchanges and job exchanges are discussed in the fifth property and the sixth property, respectively. According to these proposed properties, one solution can turn into another solution, and a theoretical foundation is provided for designing an optimization operator for solving automatic production system scheduling problem under just-in-time environment. The novel mode which optimizes the proposed problem will be developed for constructing simulation experiments.

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Section: Reference Reviewmentioning

confidence: 99%

Section: Reference Reviewmentioning

confidence: 99%

Research on solution property of automatic production system scheduling problem under just-in-time environment

Li,

Zhao,

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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“…As a result, shifting the lead 1 mm in the posterior direction positioned the lateral and posterior portions of the lead closer to the ROA. This caused a reduction in stimulation through the posterior electrodes (21,25,29) and the lateral electrodes (24,28). The ventral electrodes (21,22,24) also had less current because the shape of the ROA at the new lead location had a greater ventral extent.…”

Section: Robustnessmentioning

confidence: 99%

“…To solve such a problem, we developed a particle swarm optimization (PSO) methodology, which works by iterative exploration of the electrode configuration and stimulation amplitude parameter space. The PSO approach has been successfully applied in a number of optimization problems [23], which range from permutations [24], inversion of ocean color observations [25], training multi-layer neural networks [26], predicting tremor onset [27], and tracking human motion without markers [28]. In addition to implementing the PSO approach to solve the non-convex, threshold-based problem we have formulated, we also extend the optimization problem to a multi-objective one that optimizes for three separate clinically relevant objectives: (1) maximize activation of the therapeutic target volume, (2) minimize activation of side effect volumes, and (3) minimize overall power consumption.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization for programming deep brain stimulation arrays

et al. 2017

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Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. Significance The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

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“…They proposed a two-phase hybrid particle swarm optimization algorithm to tackle this problem. Rosas-gonzález et al [16] studied a Genetic Algorithm (GA) to solve the N-Jobs M-Machines Permutation Flow-Shop Scheduling Problem with Break-down times and makespan minimization.…”

Section: Introductionmentioning

confidence: 99%

A Metaheuristic approach for Batch Sizing and Scheduling Problem in Flexible Flow Shop with Unrelated Parallel Machines

Asadi-Gangraj¹,

Nahavandi²

2014

IJCA

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This article considers a makespan minimization batch sizing and scheduling problem in a flexible flow shop scheduling problem with unrelated parallel machines and sequence dependent setup time. Because of NP-completeness of this problem, it is necessary to use the heuristics method. Therefore, this article presents a new mixed simulated-genetic algorithm (MSGA) to tackle this problem. In the comparison, this research reports optimality gaps which are calculated with respect to MSGA method and optimal solution for small instances and the average objective function for large instances. Computational studies indicate that the MSGA is computationally efficient and effective even for small and large instances.

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A Two-Phase Hybrid Particle Swarm Optimization Algorithm for Solving Permutation Flow-Shop Scheduling Problem

Cited by 5 publications

References 36 publications

Research on solution property of automatic production system scheduling problem under just-in-time environment

Research on solution property of automatic production system scheduling problem under just-in-time environment

Particle swarm optimization for programming deep brain stimulation arrays

A Metaheuristic approach for Batch Sizing and Scheduling Problem in Flexible Flow Shop with Unrelated Parallel Machines

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