Statistical Performance Comparison Research Articles

Real Customization or Just Marketing: Are Customized Versions of Generative AI Useful?

Large Language Models (LLMs), as in the case of OpenAI TM ChatGPT-4 TM Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalised through a fine-tuning process to meet the student demands on every particular subject, like statistics. Recently, OpenAI launched the possibility of fine-tuning their model with a natural language web interface, enabling the creation of customised GPT versions deliberately conditioned to meet the demands of a specific task. This preliminary research aims to assess the potential of the customised GPTs. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behaviour was evaluated and compared with that of ChatGPT-4 Turbo. Firstly, each professor collected 15-30 genuine student questions from "Statistics and Probability" and "Business Statistics" courses across seven degrees, primarily from second-year courses. These questions, often ambiguous and imprecise, were posed to ChatGPT-4 Turbo and BSVP, with their initial responses recorded without follow-ups. In the third stage, professors blindly evaluated the responses on a 0-10 scale, considering quality, depth, and personalization. Finally, a statistical comparison of the systems' performance was conducted. The results lead to several conclusions. Firstly, a substantial modification in the style of communication was observed. Following the instructions it was trained with, BSVP responded in a more relatable and friendly tone, even incorporating a few minor jokes. Secondly, when explicitly asked for something like, "I would like to practice a programming exercise similar to those in R practice 4," BSVP could provide a far superior response. Lastly, regarding overall performance, quality, depth, and alignment with the specific content of the course, no statistically significant differences were observed in the responses between BSVP and ChatGPT-4 Turbo. It appears that customised assistants trained with prompts present advantages as virtual aids for students, yet they do not constitute a substantial improvement over ChatGPT-4 Turbo.

Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener

Hyperspectral images are widely used for land use/cover analysis in remote sensing due to their rich spectral information. However, these data often suffer from noise caused by various factors such as random and systematic errors, making them less useful for end-users. In this study, denoising methods (i.e., DnCNN, NGM, CSF, BM3D, and Wiener) for hyperspectral images were compared using the Pavia University hyperspectral dataset with four different noise types: Gaussian, Salt & Pepper, Poisson, and Speckle. After denoising, the k-nearest neighbor method was used to classify the image, and statistical and visual performance comparisons were performed on the classified data. Six performance metrics -Accuracy, Sensitivity, Specificity, Precision, F-Score, and G-Mean- were employed to compare the outcomes qualitatively. The findings demonstrate that DnCNN and BM3D have the best outcome performance for all four noise types. Due to their lack of sensitivity and specificity, the CSF and Wiener approaches had low performance for particular noise sources. For all noise types, the NGM approach had the worst results. The validated instruments not provide effective results when it came to denoising Salt & Pepper noise, but they managed to produce outstanding results when it came to denoising Poisson noise. In order to enhance the quality and usability of hyperspectral images for land use/cover analysis, this study emphasizes the significance of choosing an effective denoising technique.

Cranial reconstruction evaluation - comparison of European statistical shape model performance on Chinese dataset

PurposeMorphological variability of the skull is an important consideration for cranioplasty and implant design. Differences in morphology of the skull based on the ethnicity are known. In a previous study we could show the accuracy and benefits of virtual reconstructions based on a statistical shape model (SSM) for neurocranial defects. As the SSM is trained on European data, the question arises how well this model fares when dealing with patients with a different ethnic background. In this study we aim to evaluate the accuracy and applicability of our proposed method when deploying a cranial SSM generated from European data to estimate missing parts of the neurocranium in a Chinese population. MethodsWe used the same data and methods as in our previous study and compared the outcomes when applied to Chinese individuals. A large unilateral defect on the right side and a bilateral defect were created. The outer surface of the cranial table was reconstructed from CT scans, meshed with triangular elements, and registered to a template. Principal component analysis together with Thin Plate Spines (TPS) deformation was applied to quantify modes of variation. The mesh to mesh distances between the original defects´ surfaces and the reconstructed surface were computed. ResultsComparing the Chinese test group with the European control group, regarding the entire defect the analysis shows no significant difference for unilateral defects (test vs. control group/0.46 mm ± vs. 0.44 mm). Reconstruction of bilateral defects exhibited only in slightly higher prediction errors than those of unilateral defects (0.49 mm ± vs. 0.45 mm). ConclusionThe proposed method shows a high accuracy that seems to be ethnical independent - with low error margins for virtual skull reconstruction and implant design.Clinical relevance: Metallic objects may severely impact image quality in several CBCT devices.

A critique of a variety of “memory-based” process monitoring methods

Many extensions and modifications have been made to standard process monitoring methods such as the exponentially weighted moving average (EWMA) chart and the cumulative sum (CUSUM) chart. In addition, new schemes have been proposed based on alternative weighting of past data, usually to put greater emphasis on past data and less weight on current and recent data. In other cases, the output of one process monitoring method, such as the EWMA statistic, is used as the input to another method, such as the CUSUM chart. Often the recursive formula for a control chart statistic is itself used recursively to form a new control chart statistic. We find the use of these ad hoc methods to be unjustified. Statistical performance comparisons justifying the use of these methods have been either flawed by focusing only on zero-state run length metrics or by making comparisons to an unnecessarily weak competitor.

HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.

Comprehensive Performance Calibration Guidance for Perovskites and Other Emerging Solar Cells

AbstractEmerging photovoltaic (PV) technologies (e.g., organic, perovskite, and solution processed quantum dot) have attracted remarkable attention with the rapid growth of their efficiencies, and their transition toward commercialization. Accurate and reliable efficiency measurements of these PV technologies are crucial, yet much more complicated than for conventional PV technologies due to the former's pronounced dynamic responses to changes in measurement conditions (e.g., current–voltage (I–V) scan rate and preconditioning) and their susceptibility to degradation. Adjustments to the measurement procedures are therefore necessary so that a reproducible “steady state” is reached during measurement. Furthermore, given the small size of many emerging cells, inappropriate device area definition and solar simulator setup can lead to measurement errors. Here, comprehensive efficiency calibration guidance is offered for emerging solar cells, including area measurement; spectral irradiance translation to standard test conditions; and steady‐state electrical performance. The necessity of reporting steady‐state efficiency is justified with a statistical performance comparison between conventional and steady‐state I–V scans over hundreds of cells the group has received globally for efficiency certifications. The procedures described here do not require specialized measurement instrumentation; what matters most are changes to the measurement protocols. These described changes aim to enable better comparisons between reported efficiencies.

Harnessing Augmented Reality and CT to Teach First-Year Medical Students Head and Neck Anatomy

Three-dimensional (3D) visualization has been shown to benefit new generations of medical students and physicians-in-training in a variety of contexts. However, there is limited research directly comparing student performance after using 3D tools to those using two-dimensional (2D) screens. A CT was performed on a donated cadaver and a 3D CT hologram was created. A total of 30 first-year medical students were randomly assigned into two groups to review head and neck anatomy in a teaching session that incorporated CT. The first group used an augmented reality headset, while the second group used a laptop screen. The students were administered a five-question anatomy test before and after the session. Two-tailed t-tests were used for statistical comparison of pretest and posttest performance within and between groups. A feedback survey was distributed for qualitative data. Pretest vs. posttest comparison of average percentage of questions answered correctly demonstrated both groups showing significant in-group improvement (p < 0.05), from 59% to 95% in the augmented reality group, and from 57% to 80% in the screen group. Between-group analysis indicated that posttest performance was significantly better in the augmented reality group (p = 0.022, effect size = 0.73). Immersive 3D visualization has the potential to improve short-term anatomic recall in the head and neck compared to traditional 2D screen-based review, as well as engage millennial learners to learn better in anatomy laboratory. Our findings may reflect additional benefit gained from the stereoscopic depth cues present in augmented reality-based visualization.

Improving Patient Access to Diabetic Retinopathy Screening Through Telemedicine

Background The LifeBridge Health (LBH) Accountable Care Organization (ACO) serves approximately 20,000 Medicare beneficiaries, many of whom have type I or type II diabetes. Diabetic retinopathy (DR) screening is extremely important in helping to preserve patient’s eyesight and overall functional status. However, like many other organizations, LBH has struggled with low compliance rates for DR screening. As result, LBH searched for a solution to improve DR screening care and improve ACO quality and financial performance. Objective LifeBridge sought a telemedicine diagnostic solution that was easy for our physicians and clinic teams to use that would enable improved management of patients with diabetes. A pilot was initiated at three large primary care practice locations in the last quarter of 2017. Two of the locations received table top cameras, while the other location received a more mobile, hand held unit. Working with a dedicated LBH IRIS team, the practices created and implemented workflows, documented processes, and instilled best practices. Methods We used a pre-post test design to measure whether implementation of this tool enabled providers to better meet the diabetic retinopathy screening measure. We included the final months of 2017 in the preperiod to account for any operational changes required to implement the new workflow. Manual chart abstraction of patients seen in the previous 4/6 weeks who were eligible to determine the proportion of patients who met the measure. This was done quarterly in every primary care practice throughout the organization. One of the three practice sites was changed halfway through 2018 and switched to another; however, both practices were included in the analysis. We also compared the number of diabetes patients in the populations of each of the four practices. A two sample z test with a P value of .05 was used to test for statistical significance. Results As of April 2019, 810 patients were screened for diabetic retinopathy. Of these, 33.1% (282 patients) were diagnosed with pathology. Approximately 15.6% (n=133) were diagnosed with DR. We also identified 87 patients who are considered “IRIS saves” patients who had pathology identified that was serious enough to put them at imminent risk of losing their sight. For all patients requiring follow up, direct referrals were made to our in-network ophthalmologists at Krieger Eye Institute for treatment that these patients would not have otherwise received. Statistical comparison of DR screening performance of practices pre and post implementation showed mean screening rates of 38.5% and 47.2%, respectively, with P=.01. Conclusions IRIS screenings allowed our primary care providers to provide more comprehensive care to patients with diabetes, eliminating the need for additional office visits. Having IRIS available in the practice was able to demonstrably improve performance in the diabetic retinopathy screening measure. As a result, primary care providerss with IRIS helped facilitate access to care, thus making it easier for patients make better choices related to their health outcomes. We hope to further use the data to study HbA1c control, medication adherence, and cost/utilization in those diagnosed with retinopathy compared to those with a negative screening.

Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints

Recently, machine learning algorithms have been extensively utilized in resistance spot welding (RSW) applications to develop non-destructive weldability assessment systems to predict nugget width of RSW joints. However, different predictive models have different prediction performance that can be highly inconsistent. It is critical to compare predictive models and determine the efficient model(s). To the best of our knowledge, a comprehensive analysis and systematic prediction performance comparison of RSW nugget width prediction models have not been performed. This paper presents a statistical performance comparison methodology based on bootstrapping and hypothesis testing techniques to systematically compare the prediction performance of predictive models and determine the efficient model(s). Also, a deep neural net (DNN) nugget width prediction model is developed, analyzed, and compared with prior models. Bootstrapping is applied to generate sampling distributions for each predictive model, and statistical comparison tests are employed to analyze and compare the performance of each predictive model and identify statistically significant performance differences. Results of this analysis indicate that DNN, developed for RSW nugget width prediction in this paper, outperforms previous models.

A benchmarking study of classification techniques for behavioral data

The predictive power of increasingly common large-scale, behavioral data has been demonstrated by previous research. Such data capture human behavior through the actions and/or interactions of people. Their sparsity and ultra-high dimensionality pose significant challenges to state-of-the-art classification techniques. Moreover, no prior work has systematically explored the choice of methods with respect to the trade-off between classification performance and computational expense. This paper provides a contribution in this direction through a benchmarking study. Eleven classification models are compared on forty-one fine-grained behavioral data sets. Statistical performance comparisons enriched with learning curve analyses demonstrate two important findings. First, there is an inherent generalization performance versus time trade-off, rendering the choice of an appropriate classifier dependent on computation constraints and data set characteristics. Well-regularized logistic regression achieves the best AUC; however, it takes the longest time to train. L2 regularization performs better than sparse L1 regularization. An attractive generalization/time trade-off is achieved by a similarity-based technique. Second, although the data sets used are large, the learning curve results illustrate that as a direct consequence of their high dimensionality and sparseness, significant value lies in collecting and analyzing even more data. This finding is observed both in the instance and in the feature dimensions, contrasting with learning curve studies on traditional data. The results of this study provide guidance for researchers and practitioners for the selection of appropriate classification techniques, sample sizes and data features, while also providing focus in scalable algorithm design in the face of large, behavioral data.

An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network.

This paper presents a new learning algorithm developed for a three layered spiking neural network for pattern classification problems. The learning algorithm maximizes the interclass margin and is referred to as the two stage margin maximization spiking neural network (TMM-SNN). In the structure learning stage, the learning algorithm completely evolves the hidden layer neurons in the first epoch. Further, TMM-SNN updates the weights of the hidden neurons for multiple epochs using the newly developed normalized membrane potential learning rule such that the interclass margins (based on the response of hidden neurons) are maximized. The normalized membrane potential learning rule considers both the local information in the spike train generated by a presynaptic neuron and the existing knowledge (synaptic weights) stored in the network to update the synaptic weights. After the first stage, the number of hidden neurons and their parameters are not updated. In the output weights learning stage, TMM-SNN updates the weights of the output layer neurons for multiple epochs to maximize the interclass margins (based on the response of output neurons). Performance of TMM-SNN is evaluated using ten benchmark data sets from the UCI machine learning repository. Statistical performance comparison of TMM-SNN with other existing learning algorithms for SNNs is conducted using the nonparametric Friedman test followed by a pairwise comparison using the Fisher's least significant difference method. The results clearly indicate that TMM-SNN achieves better generalization performance in comparison to other algorithms.

An improved water wave optimization algorithm with the single wave mechanism for the no-wait flow-shop scheduling problem

ABSTRACTIn this article, a water wave optimization algorithm with a single wave mechanism, called single water wave optimization (SWWO), is proposed to solve the no-wait flow-shop scheduling problem (NWFSP) with the objective of minimizing the makespan. In the proposed SWWO, an improved Nawaz–Enscore–Ham (NEH) heuristic is applied to construct a high-quality initial candidate. In the propagation operation, a self-adaptive block-shift operation is employed. In the breaking operation, a variable neighbourhood search operation is utilized to explore the local optimal solution. According to the schema theory as presented in genetic algorithms, a crossover operation is adopted as the refraction operation. Finally, the computational results based on several benchmarks and statistical performance comparisons are presented. The experimental results demonstrate the effectiveness and efficiency of the proposed SWWO for solving the NWFSP.

Statistical analysis and performance comparison of improved and optimized CSC using different chaotic maps under different fading channels

Spread spectrum technique becomes one of the most important secure communication techniques in the recent decades. One of the most important parameter in spread spectrum is the spreading sequence, especially in the direct sequence spread spectrum. Due to the huge increase in the applications and the demanded users, the traditional spreading codes become not sufficient to fulfill the development requirements. As an alternative solution, the chaotic codes appeared to solve most problems of the traditional spreading codes. Recently, many researches focused on the chaotic codes, due to its attractive security properties, in addition to its availability to generate a huge number of spreading codes,which is very useful in the multi-access applications. However, as the number of the multiple access interference is increased, the performance of the chaotic codes is degraded. One of the degradation reasons is the assigning of the initial condition of the chaotic map. This paper presents a comprehensive statistical analysis of an improved and optimized chaotic sequence code. In addition to the traditional chaotic code; the analysis is performed over improved codes such as the self-balanced, the zero mean, and the self-balanced zero mean chaotic code. The spreading chaotic codes are generated from optimized maps with optimum initial conditions. The analysis consists of the balance, orthogonality, the normalized maximum autocorrelation side lobe, and the normalized average cross correlation property. In addition, the performance of the mentioned code is evaluated and compared with the Gold code for different code lengths over different fading channel.

Gene expression programming model to predict driven pipe piles set-up

In this paper, Gene Expression Programming (GEP) is developed to predict the increase in pipe piles axial capacity with time known as set-up. Pile set-up is significant and continues to increase with time after installation. Analysis of the collected data identified pipe pile diameter, time after installation, soil type, effective vertical effective stress at the pile tip and the initial axial capacity as playing a substantial role in predicting pile set-up. The results indicated that GEP could predict the pipe pile set-up with a remarkable accuracy (R 2 = 0.95, MAE = 377.5 and RMSE = 484.25) for the training data-set. The developed GEP model is provided in the pattern of expression tree and transformed into a mathematical equation. A statistical performance comparison is presented between the developed GEP model and previously published empirical formulas. Based on the results, the GEP model is substantially superior the traditional empirical relationships and has been identified to provide better solutions to predict set-up.

Doppler Stretch and Delay Statistical Performance Comparison for Wideband and Narrowband Signal Model

Narrowband approximation of Doppler effect by a frequency shift is inappropriate for some problems of radiolocation, hydro acoustics or passive location. In this case it is necessary to use Doppler stretch (time-scaling) as a signal parameter. Statistical performance of joint Doppler stretch and delay for arbitrary spectral width signal without narrowband approximations is derived in this paper.Narrowband and wideband parameters relations are investigated for triangle impulse with linear spectral modulation. Besides, Fisher Information Matrices (FIM) differences are analyzed. In the narrowband limit consideration of results in well-known FIM of Doppler shift and delay estimation are proposed. Another feature of wideband FIM is reference time dependence on time delay variance. Transition from Doppler stretch to Doppler frequency shift considering narrowband limitations results in translation invariant FIM.The article shows that reference time variance of delay estimation is related to estimated parameter modification and velocity influence on delay. Also, estimation variances in narrowband signal model differ from wideband parameter variances by magnitude of spectrum width to central frequency ratio.

A Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN)

Neural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSO-NN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets.

A discrete Water Wave Optimization algorithm for no-wait flow shop scheduling problem

In this paper, a discrete Water Wave Optimization algorithm (DWWO) is proposed to solve the no-wait flowshop scheduling problem (NWFSP) with respect to the makespan criterion. Inspired by the shallow water wave theory, the original Water Wave Optimization (WWO) is constructed for global optimization problems with propagation, refraction and breaking operators. The operators to adapt to the combinatorial optimization problems are redefined. A dynamic iterated greedy algorithm with a changing removing size is employed as the propagation operator to enhance the exploration ability. In refraction operator, a crossover strategy is employed by DWWO to avoid the algorithm falling into local optima. To improve the exploitation ability of local search, an insertion-based local search scheme which is utilized as breaking operator, is applied to search for a better solution around the current optimal solution. A ruling out inferior solution operator is also introduced to improve the convergence speed. The global convergence performance of the DWWO is analyzed with the Markov model. In addition, the computational results based on well-known benchmarks and statistical performance comparisons are presented. Experimental results demonstrate the effectiveness and efficiency of the proposed DWWO algorithm for solving NWFSP.

A stillbirth calculator: Development and internal validation of a clinical prediction model to quantify stillbirth risk.

ObjectiveTo generate a clinical prediction tool for stillbirth that combines maternal risk factors to provide an evidence based approach for the identification of women who will benefit most from antenatal testing for stillbirth prevention.DesignRetrospective cohort studySettingMidwestern United States quaternary referral centerPopulationSingleton pregnancies undergoing second trimester anatomic survey from 1999–2009. Pregnancies with incomplete follow-up were excluded.MethodsCandidate predictors were identified from the literature and univariate analysis. Backward stepwise logistic regression with statistical comparison of model discrimination, calibration and clinical performance was used to generate final models for the prediction of stillbirth. Internal validation was performed using bootstrapping with 1,000 repetitions. A stillbirth risk calculator and stillbirth risk score were developed for the prediction of stillbirth at or beyond 32 weeks excluding fetal anomalies and aneuploidy. Statistical and clinical cut-points were identified and the tools compared using the Integrated Discrimination Improvement.Main outcome measuresAntepartum stillbirthResults64,173 women met inclusion criteria. The final stillbirth risk calculator and score included maternal age, black race, nulliparity, body mass index, smoking, chronic hypertension and pre-gestational diabetes. The stillbirth calculator and simple risk score demonstrated modest discrimination but clinically significant performance with no difference in overall performance between the tools [(AUC 0.66 95% CI 0.60–0.72) and (AUC 0.64 95% CI 0.58–0.70), (p = 0.25)].ConclusionA stillbirth risk score was developed incorporating maternal risk factors easily ascertained during prenatal care to determine an individual woman’s risk for stillbirth and provide an evidenced based approach to the initiation of antenatal testing for the prediction and prevention of stillbirth.

Multi-objective inverse scheduling optimization of single-machine shop system with uncertain due-dates and processing times

Generally, ideal manufacturing system environments are assumed before determining effective scheduling. However, the original schedule is no longer optimal or even to be infeasible due to many uncertain events. This paper investigates a multi-objective inverse scheduling problem in single-machine shop system with due-dates and uncertain processing parameters. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing parameters are considered to be uncertain stochastic parameters. First, a comprehensive mathematical model for multi-objective single-machine inverse scheduling problem (MSMISP) is addressed. Second, an effective hybrid multi-objective evolutionary algorithm (HMNL) is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In HMNL, using an effective decimal system encoding scheme and genetic operators, the non-dominated sorting based on NSGA-II is adapted for the MSMISP. In addition, hybrid HMNL are proposed by incorporating an adaptive local search scheme into the well-known NSGA-II, where applies a separate local search process, total six strategies, to improve quality of solutions. Furthermore, an on-demand layered strategy is embedded into the elitism strategy to keep the population diversity. Afterwards, an external archive set is dynamically updated, where a non-dominated solution is selected to participate in the creation of the new population. Finally, 36 public problem instances with different scales and statistical performance comparisons are provided for the HMNL algorithm. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve MSMISP in inverse scheduling domain.

An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems

Generally, in handling traditional scheduling problems, ideal manufacturing system environments are assumed before determining effective scheduling. Unfortunately, “ideal environments” are not always possible. Real systems often encounter some uncertainties which will change the status of manufacturing systems. These may cause the original schedule to no longer to be optimal or even feasible. Traditional scheduling methods are not effective in coping with these cases. Therefore, a new scheduling strategy called “inverse scheduling” has been proposed to handle these problems. To the best of our knowledge, this research is the first to provide a comprehensive mathematical model for multi-objective permutation flow-shop inverse scheduling problem (PFISP). In this paper, first, a PFISP mathematical model is devised and an effective hybrid multi-objective evolutionary algorithm is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In the proposed algorithm, we take an insert method NEH-based (Nawaz–Enscore–Ham) as a local improving procedure and propose several adaptations including efficient initialization, decimal system encoding, elitism and population diversity. Finally, 119 public problem instances with different scales and statistical performance comparisons are provided for the proposed algorithm. The results show that the proposed algorithm performs better than the traditional multi-objective evolution algorithm (MOEA) in terms of searching quality, diversity level and efficiency. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve PFISP in inverse scheduling domain.