A survey on new generation metaheuristic algorithms

Mathematical programming

Machine learning models show improved bankruptcy prediction accuracy over traditional models.Various models were tested using different accuracy metrics.Boosting, bagging, and random forest models provide better results. There has been intensive research from academics and practitioners regarding models for predicting bankruptcy and default events, for credit risk management. Seminal academic research has evaluated bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks). In this study, we test machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event, and compare their performance with results from discriminant analysis, logistic regression, and neural networks. We use data from 1985 to 2013 on North American firms, integrating information from the Salomon Center database and Compustat, analysing more than 10,000 firm-year observations. The key insight of the study is a substantial improvement in prediction accuracy using machine learning techniques especially when, in addition to the original Altmans Z-score variables, we include six complementary financial indicators. Based on Carton and Hofer (2006), we use new variables, such as the operating margin, change in return-on-equity, change in price-to-book, and growth measures related to assets, sales, and number of employees, as predictive variables. Machine learning models show, on average, approximately 10% more accuracy in relation to traditional models. Comparing the best models, with all predictive variables, the machine learning technique related to random forest led to 87% accuracy, whereas logistic regression and linear discriminant analysis led to 69% and 50% accuracy, respectively, in the testing sample. We find that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. Our research adds to the discussion of the continuing debate about superiority of computational methods over statistical techniques such as in Tsai, Hsu, and Yen (2014) and Yeh, Chi, and Lin (2014). In particular, for machine learning mechanisms, we do not find SVM to lead to higher accuracy rates than other models. This result contradicts outcomes from Danenas and Garsva (2015) and Cleofas-Sanchez, Garcia, Marques, and Senchez (2016), but corroborates, for instance, Wang, Ma, and Yang (2014), Liang, Lu, Tsai, and Shih (2016), and Cano etal. (2017). Our study supports the applicability of the expert systems by practitioners as in Heo and Yang (2014), Kim, Kang, and Kim (2015) and Xiao, Xiao, and Wang (2016).

Machine learning models and bankruptcy prediction

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analy...

A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0

A mayfly optimization algorithm

This paper presents a new constructive heuristic, based on the principle of job insertion, for minimizing makespan in no-wait permutation flow shop scheduling problems. Empirical results demonstrate the superiority of the proposed approach over four of the best-known methods in the literature. Analytical expressions for the total number of partial and complete sequences generated by the algorithms are derived. Statistical tests of significance substantiate the improvement in solution quality.

A constructive heuristic for minimizing makespan in no-wait flow shop scheduling

In this paper, we address an n-job, m-machine permutation flow shop scheduling problem for the objective of minimizing the total flow time We propose a modification of the best-known method of Framinan and Leisten [An efficient constructive heuristic for flowtime minimization in permutation flow shops Omega 2003;31:311–7] for this problem We show, through computational experimentation, that this modification significantly improves its performance while not affecting its time-complexity

A heuristic to minimize total flow time in permutation flow shop

Machine learning methods on open-hearth steel making process prediction.Predicted yield based on 4 machine learning methods has very low errors.Best performance is achieved by SVR. Monitoring and control of the output yield of steel in a steelmaking shop plays a critical role in steel industry. The yield of steel determines how much percentage of hot metal, scrap, and iron ore are being converted into steel ingots. It represents the operational efficiency of the steelmaking shop and is considered as an important performance measure for producing a specific quantity of steel. Due to complexity of the steelmaking process and nonlinear relationship between the process parameters, modeling the input-output process parameters and accurately predicting the output yield in the steelmaking shop is very difficult and has been a major research issue. Statistical models and artificial neural networks (ANN) have been extensively studied by researchers and practitioners to model a variety of complex processes. In the present study, we consider random forests (RF), ANN, dynamic evolving neuro-fuzzy inference system (DENFIS) and support vector regression (SVR) as competitive learning tools to verify the suitability of applications of these approaches and investigate their comparative predictive ability. In the present investigation, 0.00001 of MSE is set as a goal of learning during modeling. Based on real-life data, the computational results depict that the training and testing MSE values of SVR and DENFIS are close to 0.00001 indicating that they have higher prediction ability than ANN and RF. Also, mean absolute percentage prediction errors of the proposed models confirm that the predicted yield based on each method is in good agreement with the testing datasets. Overall, SVR performs best and DENFIS the next best followed by ANN and RF methods respectively. The results suggest that the prediction precision given by SVR can meet the requirement for the actual production of steel.

Modeling of steelmaking process with effective machine learning techniques

During the last two decades, computer and information technologies have forced great changes in the ways businesses manage operations in meeting the desired quality of products and services, customer demands, competition, and other challenges. The Handbook of Computational Intelligence in Manufacturing and Production Management focuses on new developments in computational intelligence in areas such as forecasting, scheduling, production planning, inventory control, and aggregate planning, among others. This comprehensive collection of research provides cutting-edge knowledge on information technology developments for both researchers and professionals in fields such as operations and production management, Web engineering, artificial intelligence, and information resources management.

Handbook of Computational Intelligence in Manufacturing and Production Management

The paper addresses the problem of 2-machine robotic cell scheduling of one-unit cycle with sequence-dependent setup times and different loading/unloading times of the parts. As an alternative metaheuristic algorithm, the cuckoo search algorithm has recently attracted growing interests of researchers. It has the capability to search globally as well as locally to converge to the global optimality by exploring the search space more efficiently due to its global random walk governed by Levy flights, rather than standard isotropic random walk. In this study, a discrete cuckoo search algorithm is proposed to determine the sequence of robot moves along with the sequence of parts so that the cycle time is minimized. In the proposed algorithm, the fractional scaling factor based procedure is presented to determine the step length of Levy flights distribution in discrete from and then, using this step length, two neighborhood search techniques, interchange and cyclical shift methods are applied to the current solution to obtain improved solution. A response surface methodology based on desirability function is used to enhance the convergence speed of the proposed algorithm. Also, a design of experiment is employed to tune the operating parameters of the algorithm. Finally, empirical results with a large number of randomly generated problem instances involving large part sizes varying from 200 to 500 under different operating conditions are compared with two well-known algorithms in the literature and demonstrate the effectiveness of the proposed algorithm.

Dipak Laha

Papers

A constructive heuristic for minimizing makespan in no-wait flow shop scheduling

A heuristic to minimize total flow time in permutation flow shop

Modeling of steelmaking process with effective machine learning techniques

Handbook of Computational Intelligence in Manufacturing and Production Management

A new cuckoo search algorithm for 2-machine robotic cell scheduling problem with sequence-dependent setup times