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Ji-bo Wang - One of the best experts on this subject based on the ideXlab platform.
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Research on common due window assignment flowshop scheduling with Learning Effect and resource allocation
Engineering Optimization, 2019Co-Authors: Hui-bin Shi, Ji-bo WangAbstract:In this article, the two-machine no-wait permutation flowshop scheduling problem with common due window assignment, Learning Effect and resource allocation is considered, wherein Learning Effect an...
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Resource constrained scheduling with general truncated job-dependent Learning Effect
Journal of Combinatorial Optimization, 2015Co-Authors: Mengqi Liu, Ji-bo WangAbstract:Scheduling with general truncated job-dependent Learning Effect and resource-dependent processing times is studied on a single machine. It is assumed that the job processing time is a function of the amount of resource allocated to the job, the general job-dependent Learning Effect and the job-dependent control parameter. For each version of the problem that differs in terms of the objective functions and the processing time functions, the optimal resource allocation is provided. Polynomial time algorithms are also developed to find the optimal schedule of several versions of the problem.
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flowshop scheduling with a general exponential Learning Effect
Computers & Operations Research, 2014Co-Authors: Ji-bo Wang, Jianjun WangAbstract:This paper investigates flowshop scheduling problems with a general exponential Learning Effect, i.e., the actual processing time of a job is defined by an exponent function of the total weighted normal processing time of the already processed jobs and its position in a sequence, where the weight is a position-dependent weight. The objective is to minimize the makespan, the total (weighted) completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times, respectively. Several simple heuristic algorithms are proposed in this paper by using the optimal schedules for the corresponding single machine problems. The tight worst-case bound of these heuristic algorithms is also given. Two well-known heuristics are also proposed for the flowshop scheduling with a general exponential Learning Effect.
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Scheduling jobs with a general Learning Effect model
Applied Mathematical Modelling, 2013Co-Authors: Ji-bo Wang, Jianjun WangAbstract:Abstract The paper deals with machine scheduling problems with a general Learning Effect. By the general Learning Effect, we mean that the actual processing time of a job is not only a non-increasing function of the total weighted normal processing times of the jobs already processed, but also a non-increasing function of the job’s position in the sequence, where the weight is a position-dependent weight. We show that even with the introduction of a general Learning Effect to job processing times, some single machine scheduling problems are still polynomially solvable under the proposed model. We also show that some special cases of the flow shop scheduling problems can be solved in polynomial time.
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Single machine scheduling with truncated job-dependent Learning Effect
Optimization Letters, 2012Co-Authors: Xue-ru Wang, Ji-bo Wang, Jian JinAbstract:In this paper we consider the single machine scheduling problem with truncated job-dependent Learning Effect. By the truncated job-dependent Learning Effect, we mean that the actual job processing time is a function which depends not only on the job-dependent Learning Effect (i.e., the Learning in the production process of some jobs to be faster than that of others) but also on a control parameter. The objectives are to minimize the makespan, the total completion time, the total absolute deviation of completion time, the earliness, tardiness and common (slack) due-date penalty, respectively. Several polynomial time algorithms are proposed to optimally solve the problems with the above objective functions.
Marco Centofanti - One of the best experts on this subject based on the ideXlab platform.
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Mild Learning Effect of short-wavelength automated perimetry using SITA program.
Journal of glaucoma, 2010Co-Authors: Paolo Fogagnolo, Luca Rossetti, Nicola Orzalesi, Francesco Oddone, Gianluca Manni, Lucia Tanga, Marco CentofantiAbstract:PURPOSE: To evaluate the Learning Effect at Short-wavelength Automated Perimetry (SWAP) using the Swedish Interactive Threshold Algorithm (SITA) program over the central 24 degrees on patients with ocular hypertension experienced with standard automated perimetry. METHODS: Twenty-seven patients underwent 5 SITA SWAP tests at intervals of 5+/-2 days in a randomized eye. Learning Effect was defined as an improvement in results for duration, perimetric indices, foveal sensitivity, Glaucoma Hemifield Test, number of points with a P
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Learning Effect of humphrey matrix frequency doubling technology perimetry in patients with ocular hypertension.
Journal of glaucoma, 2008Co-Authors: Marco Centofanti, Paolo Fogagnolo, Michele Vetrugno, Nicola Orzalesi, Francesco Oddone, Gianluca Manni, Luca RossettiAbstract:Aim: To evaluate the Learning Effect of Frequency Doubling Technology (FDT) perimetry using the Humphrey Matrix-FDT perimetry (Matrix) 24-2 full-threshold program on patients with 7 ocular hypertension experienced with standard automated perimetry. Methods: Twenty-four patients with Ocular hypertension underwent 5 full-threshold Matrix tests at intervals of 5 2 days. Learning Effect was defined as an improvement at results for duration, perimetric indices, foveal sensitivity, Glaucoma Hemifield Test, and the number of points with a P < 5% and < 1% in the total and pattern deviation maps. Eccentricity, hemifield, and quadrant sensitivities were also addressed as Sources of differences in Learning Effect. Test-retest variability was also calculated for each repetition as the mean of the point-to-point interindividual standard deviations. Results: A Learning Effect was demonstrated for mean defect (P = 0.031, analysis of variance) and foveal sensitivity (P = 0.009) and it only affected the first test for both parameters. All the other parameters did not show any significant Learning Effect. The Effect was independent From eccentricity and quadrant or hemifield sensitivities. Conclusions: The results of this study demonstrate that the Learning Effect for Matrix-FDT is mild and it may affect only the first test. Caution is needed in the analysis of the first Matrix-FDT examination and retest may be advisable in the presence of low mean defect.
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Learning Effect of short-wavelength automated perimetry in patients with ocular hypertension.
Journal of glaucoma, 2006Co-Authors: Luca Rossetti, Paolo Fogagnolo, Stefano Miglior, Marco Centofanti, Michele Vetrugno, Nicola OrzalesiAbstract:Aim: To evaluate the Learning Effect of short -wavelength automated perimetry (SWAP) on a group of patients with ocular hypertension experienced with standard automated perimetry (SAP). Methods: Thirty patients with ocular hypertension underwent 5 full-threshold SWAP tests at intervals of 7 +/- 2 days. The parameters investigated to detect a Learning Effect were duration, the perimetric indices, and the number of points with a P of < 5% and 1% in the total and pattern deviation maps. Differences in Learning Effect were also evaluated by comparing the sensitivities of central, paracentral, and peripheral areas, hemifields and quadrants. Results: Learning Effects were demonstrated for mean defect (P < 0.0001, analysis of variance), duration (P = 0.0001), the number of points with P < 5% in the pattern deviation map (P = 0.003), and short fluctuations (P = 0.03). The Effect was greater in the peripheral than in central areas (P = 0.04). Mean defect was the most sensitive parameter, for which the Learning Effect was statistically significant between the first and the fifth test. Conclusions: The results of this study demonstrate a significant Learning Effect at full-threshold SWAP. This may limit the efficacy of this kind of perimetry in detecting early glaucoma , and should therefore be carefully considered when creating normative databases for new SWAP strategies.
Nicola Orzalesi - One of the best experts on this subject based on the ideXlab platform.
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Mild Learning Effect of short-wavelength automated perimetry using SITA program.
Journal of glaucoma, 2010Co-Authors: Paolo Fogagnolo, Luca Rossetti, Nicola Orzalesi, Francesco Oddone, Gianluca Manni, Lucia Tanga, Marco CentofantiAbstract:PURPOSE: To evaluate the Learning Effect at Short-wavelength Automated Perimetry (SWAP) using the Swedish Interactive Threshold Algorithm (SITA) program over the central 24 degrees on patients with ocular hypertension experienced with standard automated perimetry. METHODS: Twenty-seven patients underwent 5 SITA SWAP tests at intervals of 5+/-2 days in a randomized eye. Learning Effect was defined as an improvement in results for duration, perimetric indices, foveal sensitivity, Glaucoma Hemifield Test, number of points with a P
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Learning Effect of humphrey matrix frequency doubling technology perimetry in patients with ocular hypertension.
Journal of glaucoma, 2008Co-Authors: Marco Centofanti, Paolo Fogagnolo, Michele Vetrugno, Nicola Orzalesi, Francesco Oddone, Gianluca Manni, Luca RossettiAbstract:Aim: To evaluate the Learning Effect of Frequency Doubling Technology (FDT) perimetry using the Humphrey Matrix-FDT perimetry (Matrix) 24-2 full-threshold program on patients with 7 ocular hypertension experienced with standard automated perimetry. Methods: Twenty-four patients with Ocular hypertension underwent 5 full-threshold Matrix tests at intervals of 5 2 days. Learning Effect was defined as an improvement at results for duration, perimetric indices, foveal sensitivity, Glaucoma Hemifield Test, and the number of points with a P < 5% and < 1% in the total and pattern deviation maps. Eccentricity, hemifield, and quadrant sensitivities were also addressed as Sources of differences in Learning Effect. Test-retest variability was also calculated for each repetition as the mean of the point-to-point interindividual standard deviations. Results: A Learning Effect was demonstrated for mean defect (P = 0.031, analysis of variance) and foveal sensitivity (P = 0.009) and it only affected the first test for both parameters. All the other parameters did not show any significant Learning Effect. The Effect was independent From eccentricity and quadrant or hemifield sensitivities. Conclusions: The results of this study demonstrate that the Learning Effect for Matrix-FDT is mild and it may affect only the first test. Caution is needed in the analysis of the first Matrix-FDT examination and retest may be advisable in the presence of low mean defect.
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Learning Effect of short-wavelength automated perimetry in patients with ocular hypertension.
Journal of glaucoma, 2006Co-Authors: Luca Rossetti, Paolo Fogagnolo, Stefano Miglior, Marco Centofanti, Michele Vetrugno, Nicola OrzalesiAbstract:Aim: To evaluate the Learning Effect of short -wavelength automated perimetry (SWAP) on a group of patients with ocular hypertension experienced with standard automated perimetry (SAP). Methods: Thirty patients with ocular hypertension underwent 5 full-threshold SWAP tests at intervals of 7 +/- 2 days. The parameters investigated to detect a Learning Effect were duration, the perimetric indices, and the number of points with a P of < 5% and 1% in the total and pattern deviation maps. Differences in Learning Effect were also evaluated by comparing the sensitivities of central, paracentral, and peripheral areas, hemifields and quadrants. Results: Learning Effects were demonstrated for mean defect (P < 0.0001, analysis of variance), duration (P = 0.0001), the number of points with P < 5% in the pattern deviation map (P = 0.003), and short fluctuations (P = 0.03). The Effect was greater in the peripheral than in central areas (P = 0.04). Mean defect was the most sensitive parameter, for which the Learning Effect was statistically significant between the first and the fifth test. Conclusions: The results of this study demonstrate a significant Learning Effect at full-threshold SWAP. This may limit the efficacy of this kind of perimetry in detecting early glaucoma , and should therefore be carefully considered when creating normative databases for new SWAP strategies.
Luca Rossetti - One of the best experts on this subject based on the ideXlab platform.
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Mild Learning Effect of short-wavelength automated perimetry using SITA program.
Journal of glaucoma, 2010Co-Authors: Paolo Fogagnolo, Luca Rossetti, Nicola Orzalesi, Francesco Oddone, Gianluca Manni, Lucia Tanga, Marco CentofantiAbstract:PURPOSE: To evaluate the Learning Effect at Short-wavelength Automated Perimetry (SWAP) using the Swedish Interactive Threshold Algorithm (SITA) program over the central 24 degrees on patients with ocular hypertension experienced with standard automated perimetry. METHODS: Twenty-seven patients underwent 5 SITA SWAP tests at intervals of 5+/-2 days in a randomized eye. Learning Effect was defined as an improvement in results for duration, perimetric indices, foveal sensitivity, Glaucoma Hemifield Test, number of points with a P
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Learning Effect of humphrey matrix frequency doubling technology perimetry in patients with ocular hypertension.
Journal of glaucoma, 2008Co-Authors: Marco Centofanti, Paolo Fogagnolo, Michele Vetrugno, Nicola Orzalesi, Francesco Oddone, Gianluca Manni, Luca RossettiAbstract:Aim: To evaluate the Learning Effect of Frequency Doubling Technology (FDT) perimetry using the Humphrey Matrix-FDT perimetry (Matrix) 24-2 full-threshold program on patients with 7 ocular hypertension experienced with standard automated perimetry. Methods: Twenty-four patients with Ocular hypertension underwent 5 full-threshold Matrix tests at intervals of 5 2 days. Learning Effect was defined as an improvement at results for duration, perimetric indices, foveal sensitivity, Glaucoma Hemifield Test, and the number of points with a P < 5% and < 1% in the total and pattern deviation maps. Eccentricity, hemifield, and quadrant sensitivities were also addressed as Sources of differences in Learning Effect. Test-retest variability was also calculated for each repetition as the mean of the point-to-point interindividual standard deviations. Results: A Learning Effect was demonstrated for mean defect (P = 0.031, analysis of variance) and foveal sensitivity (P = 0.009) and it only affected the first test for both parameters. All the other parameters did not show any significant Learning Effect. The Effect was independent From eccentricity and quadrant or hemifield sensitivities. Conclusions: The results of this study demonstrate that the Learning Effect for Matrix-FDT is mild and it may affect only the first test. Caution is needed in the analysis of the first Matrix-FDT examination and retest may be advisable in the presence of low mean defect.
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Learning Effect of short-wavelength automated perimetry in patients with ocular hypertension.
Journal of glaucoma, 2006Co-Authors: Luca Rossetti, Paolo Fogagnolo, Stefano Miglior, Marco Centofanti, Michele Vetrugno, Nicola OrzalesiAbstract:Aim: To evaluate the Learning Effect of short -wavelength automated perimetry (SWAP) on a group of patients with ocular hypertension experienced with standard automated perimetry (SAP). Methods: Thirty patients with ocular hypertension underwent 5 full-threshold SWAP tests at intervals of 7 +/- 2 days. The parameters investigated to detect a Learning Effect were duration, the perimetric indices, and the number of points with a P of < 5% and 1% in the total and pattern deviation maps. Differences in Learning Effect were also evaluated by comparing the sensitivities of central, paracentral, and peripheral areas, hemifields and quadrants. Results: Learning Effects were demonstrated for mean defect (P < 0.0001, analysis of variance), duration (P = 0.0001), the number of points with P < 5% in the pattern deviation map (P = 0.003), and short fluctuations (P = 0.03). The Effect was greater in the peripheral than in central areas (P = 0.04). Mean defect was the most sensitive parameter, for which the Learning Effect was statistically significant between the first and the fifth test. Conclusions: The results of this study demonstrate a significant Learning Effect at full-threshold SWAP. This may limit the efficacy of this kind of perimetry in detecting early glaucoma , and should therefore be carefully considered when creating normative databases for new SWAP strategies.
Wen-chiung Lee - One of the best experts on this subject based on the ideXlab platform.
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A note on single-machine group scheduling problems with position-based Learning Effect
Applied Mathematical Modelling, 2009Co-Authors: Wen-chiung LeeAbstract:Abstract In many situations, the skills of workers continuously improve when repeating the same or similar tasks. This phenomenon is known as the “Learning Effect” in the literature. However, most studies considering the Learning Effect ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as “group technology” in the literature. In this paper, we propose a new group scheduling Learning model where the Learning Effect not only depends on the job position, but also depends on the group position. We then show that the makespan and the total completion time problems remain polynomially solvable under the proposed model.
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A note on the total completion time problem in a permutation flowshop with a Learning Effect
European Journal of Operational Research, 2009Co-Authors: Wen-chiung LeeAbstract:The concept of Learning process plays a key role in production environments. However, it is relatively unexplored in the flowshop setting. In this short note, we consider a permutation flowshop scheduling problem with a Learning Effect where the objective is to minimize the sum of completion times or flowtime. A dominance rule and several lower bounds are established to speed up the search for the optimal solution. In addition, the performances of several well-known heuristics are evaluated when the Learning Effect is present.
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A two-machine flowshop maximum tardiness scheduling problem with a Learning Effect.
The International Journal of Advanced Manufacturing Technology, 2006Co-Authors: Wen-chiung Lee, Wei-chieh WangAbstract:The primary objective of this paper is to study a two-machine flowshop scheduling problem with a Learning Effect where the goal is to find a sequence that minimizes the maximum tardiness. We employ a branch-and-bound method and a simulated annealing (SA) method to search for the optimal solution and a near-optimal solution, respectively. Computational results, using Fisher’s (Math Program 11:229–251 1971) framework, show that the mean and maximum number of nodes for the branch-and-bound algorithm decrease when the Learning Effect is stronger, the value of the tardiness factor is smaller, or the value of the due date range is larger. In addition, comparisons between the SA method and the earliest due date first (EDD) rule are provided for large-job sizes. Results indicate that the percentage of time that the SA solution outperforms the EDD solution decreases as the job size increases and the Learning Effect becomes greater. Additionally, the SA solution is never worse than the EDD solution.
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Minimizing total completion time in a two-machine flowshop with a Learning Effect
International Journal of Production Economics, 2004Co-Authors: Wen-chiung LeeAbstract:Abstract In many situations, a worker's ability improves as a result of repeating the same or similar tasks; this phenomenon is known as the “Learning Effect”. In this paper, the Learning Effect is considered in a two-machine flowshop. The objective is to find a sequence that minimizes the total completion time. Several dominance properties and the lower bounds are derived to speed up the elimination process of the branch-and-bound algorithm. A heuristic algorithm is also proposed to overcome the inefficiency of the branch-and-bound algorithm. In the simulation, the proposed heuristic algorithm is shown to perform consistently better than the previous one.
