Self-adaptive Differential Evolution Research Articles

A multi-strategy self-adaptive differential evolution algorithm for assembly hybrid flowshop lot-streaming scheduling with component sharing

Lot-streaming helps to achieve a more balanced utilization of parallel machines and more timely assembly of components, while component sharing increases the flexibility and commonality of assembly operations. Thus, this work addresses an assembly hybrid flowshop lot-streaming scheduling problem with component sharing. A mixed-integer linear programming model is formulated to scrutinize the coupling relations among variables i.e. sub-lot splitting, machine allocation, processing sequencing, and assembly sequencing, and to minimize the maximum completion time and work-in-process inventory lexicographically. To solve the above problem efficiently, a multi-strategy self-adaptive differential evolution (MSDE) algorithm is developed. In MSDE, three problem-specific strategies that consider component integrity and specific requirements of production and assembly are integrated to enhance the initial population in terms of diversity and solution quality. A Q-learning-based selection mechanism is proposed to self-adaptively select an appropriate combination from mutation and crossover operators for achieving a balance between exploration and exploitation. An inventory reduction strategy is appended to largely reduce work-in-process components without extending completion time. Four conclusions are drawn from extensive experiments: (1) The ensemble of three population initialization strategies is superior to each individual one; (2) The Q-learning-based optimizer selection is more effective and robust than the single optimizer-based one; (3) The work-in-process inventory reduction strategy demonstrates remarkable effectiveness for most solutions; (4) MSDE outperforms the existing state-of-the-art algorithms in most cases.

Risk assessment of water inrush from coal floor based on enhanced samples with class distribution

In the risk assessment of water inrush from coal floors, the amount of measured data obtained through on-site testing is small and random, which limits the prediction accuracy and generalizability of a model based on measured data. Using the distribution characteristics of the measured data and mega-trend diffusion theory, we propose a virtual sample enhancement method based on class distribution mega-trend diffusion technology (CDMTD) and introduce constraints on the class distribution of influencing factors. This method was used to generate virtual samples and enhance the measured database. A prediction model of the water inrush risk for the coal seam floor was established using a coupled algorithm of extreme learning machines, self-adaptive differential evolution, and CDMTD (PCA-CDMTD-SaDE-ELM) and was used to evaluate the water inrush risk in the 19,105 working face of the Yunjialing Mine. The CDMTD method could effectively solve the problem of virtual sample distribution variation in the overall trend diffusion theory and enhance the measured database, reducing the impact of small sample sizes. Compared to other optimization models, our model showed the best prediction performance, with an error reduction of 42.95–51.27% and results biased towards safety. Our results support safe and efficient coal mining above Ordovician limestone-confined water.

Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data

Coronary artery disease (CAD) is the main cause of death. It is a complex heart disease that is linked with many risk factors and a variety of symptoms. In the past few years, CAD has experienced a remarkable growth. Prompt risk prediction of CAD would be capable of decreasing the death rate by permitting timely and targeted treatments. Angiography is the most precise CAD diagnosis technique; however, it has several side effects and is expensive. Multi-criteria decision-making approaches can well perceive CAD by analysing main clinical indicators like ChestPain type, ST_Slope, and HeartDisease presence. By assessing and evaluating these factors, the model improves diagnostic accuracy and aids informed clinical decisions for quick CAD detection. Mainly machine learning (ML) and deep learning (DL) use plentiful models and algorithms, which are commonly employed and very useful in exactly detecting the CAD within a short time. Current studies have employed numerous features in gathering data from patients while using dissimilar ML and DL models to attain results with high accuracy and lesser side effects and costs. This study presents a Leveraging Fuzzy Wavelet Neural Network with Decision Making Approach for Coronary Artery Disease Prediction (LFWNNDMA-CADP) technique. The presented LFWNNDMA-CADP technique focuses on the multi-criteria decision-making model for predicting CAD using biomedical data. In the LFWNNDMA-CADP method, the data pre-processing stage utilizes Z-score normalization to convert an input data into a uniform format. Furthermore, the improved ant colony optimization (IACO) method is used for electing an optimum sub-set of features. Furthermore, the classification of CAD is accomplished by utilizing the fuzzy wavelet neural network (FWNN) technique. Finally, the hyperparameter tuning of the FWNN model is accomplished by employing the hybrid crayfish optimization algorithm with the self-adaptive differential evolution (COASaDE) technique. The simulation outcomes of the LFWNNDMA-CADP approach are investigated under a benchmark database. The experimental validation of the LFWNNDMA-CADP approach portrayed a superior accuracy value of 99.49% over existing techniques.

An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees

This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. To overcome these limitations, other strategies, such as those based on evolutionary computation, have been proposed in the literature. In particular, we evaluate the use of self-adaptive differential evolution variants to evolve a population of oblique decision trees encoded as real-valued vectors. Our proposal includes (1) an alternative initialization strategy that reduces redundant nodes and (2) a fitness function that penalizes excessive leaf nodes, promoting smaller and more accurate decision trees. We perform a comparative performance analysis of these differential evolution variants, showing that while they exhibit similar statistical behavior, the Single-Objective real-parameter optimization (jSO) method produces the most accurate oblique decision trees and is second best in compactness. The findings highlight the potential of self-adaptive differential evolution algorithms to improve the effectiveness of oblique decision trees in machine learning applications.

A Self-Adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids

A Self-Adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids

SaDENAS: A self-adaptive differential evolution algorithm for neural architecture search

Evolutionary neural architecture search (ENAS) and differentiable architecture search (DARTS) are all prominent algorithms in neural architecture search, enabling the automated design of deep neural networks. To leverage the strengths of both methods, there exists a framework called continuous ENAS, which alternates between using gradient descent to optimize the supernet and employing evolutionary algorithms to optimize the architectural encodings. However, in continuous ENAS, there exists a premature convergence issue accompanied by the small model trap, which is a common issue in NAS. To address this issue, this paper proposes a self-adaptive differential evolution algorithm for neural architecture search (SaDENAS), which can reduce the interference caused by small models to other individuals during the optimization process, thereby avoiding premature convergence. Specifically, SaDENAS treats architectures within the search space as architectural encodings, leveraging vector differences between encodings as the basis for evolutionary operators. To achieve a trade-off between exploration and exploitation, we integrate both local and global search strategies with a mutation scaling factor to adaptively balance these two strategies. Empirical findings demonstrate that our proposed algorithm achieves better performance with superior convergence compared to other algorithms.

A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters.

Multi-algorithm based evolutionary strategy with Adaptive Mutation Mechanism for Constraint Engineering Design Problems

This paper proposes a new multi-algorithm based evolution strategy with the addition of adaptive mutation operators for global optimization. The new algorithm namely Kepler meerkat naked (KMN) algorithm is based on Kepler’s optimization algorithm (KOA), meerkat optimization algorithm (MOA), and naked mole-rat algorithm (NMRA), as the core algorithms and, grey wolf optimizer (GWO) and cuckoo search (CS) inspired equations for enhanced exploration and exploitation. The proposed algorithm uses six new mutation operators for parametric enhancements, and follows an iterative division mechanism for a balanced operation. A comparative analysis is done with respect to classical benchmarks, CEC 2014, CEC 2017, CEC 2019 and CEC 2022 benchmark datasets for performance evaluation. Six engineering design problems are also used to test the performance of the proposed KMN algorithm for constraint optimization. Apart from that, a binary version of KMN namely bKMN is also proposed, and ten feature selection datasets are used for performance evaluation. Performance testing of the KMN and bKMN algorithm is done with success history-based DE (SHADE), LSHADE-SPACMA, self-adaptive DE (SaDE), fast opposition-based learning golden jackal optimization (FROBL-GJO), LSHADE-EpSin, jSO, EBOwithCMAR, among others. Experimental and statistical results are performed using Wilcoxon’s and Friedman’s tests, and it has been found that the proposed algorithms are highly competitive in contrast to other algorithms under study.

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Abstract Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array total focusing method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an self-adaptive differential evolution algorithm. Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the root mean square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and TFM method (Ray-based FMC/TFM), the RMS-based full-matrix capture and TFM (RMS-based FMC/TFM), and the phase shift method, the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

Estimation of ultrasonic flight time based on SHADE algorithm and extended kalman filtering

Abstract Traditional methods of using ultrasound to measure oxygen concentration have the issue of inaccuracy and susceptibility to noise interference when measuring the time of flight (TOF) of ultrasonic echo signals. A proposed algorithm for ultrasonic parameter estimation, utilizing an asymmetric Gaussian model, aims to enhance the precision and reliability of TOF estimation, aiming to reduce the influence of noise interference. The algorithm combines an improved Self-Adaptive Differential Evolution (SHADE) algorithm based on successful historical records with an Extended Kalman Filter (EKF). The improved SHADE algorithm controls the degree of mutation strategy greediness during the mutation operation based on the number of iterations, thereby enhancing the algorithm’s iteration speed. Then, the obtained solution is used as the initial value for the EKF to estimate the model parameters, thus addressing the issue of EKF’s dependency on accurate initial values to obtain precise outputs. Through simulation and experimental measurements of ultrasonic oxygen concentration, several commonly used parameter estimation algorithms are compared with the proposed SHADE-EKF algorithm. Results demonstrate that the SHADE-EKF algorithm exhibits superior performance in estimating the TOF of ultrasonic waves.

Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

This study presents the Hybrid COASaDE Optimizer, a novel combination of the Crayfish Optimization Algorithm (COA) and Self-adaptive Differential Evolution (SaDE), designed to address complex optimization challenges and solve engineering design problems. The hybrid approach leverages COA’s efficient exploration mechanisms, inspired by crayfish behaviour, with the symmetry of SaDE’s adaptive exploitation capabilities, characterized by its dynamic parameter adjustment. The balance between these two phases represents a symmetrical relationship wherein both components contribute equally and complementary to the algorithm’s overall performance. This symmetry in design enables the Hybrid COASaDE to maintain consistent and robust performance across a diverse range of optimization problems. Experimental evaluations were conducted using CEC2022 and CEC2017 benchmark functions, demonstrating COASaDE’s superior performance compared to state-of-the-art optimization algorithms. The results and statistical analyses confirm the robustness and efficiency of the Hybrid COASaDE in finding optimal solutions. Furthermore, the applicability of the Hybrid COASaDE was validated through several engineering design problems, where COASaDE outperformed other optimizers in achieving the optimal solution.

A hybrid swarm intelligence algorithm for region-based image fusion

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index (QAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q^{AB/F}$$\\end{document}), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure (NAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{AB/F}$$\\end{document}). The average QAB/F=0.765508\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q^{AB/F} = 0.765508$$\\end{document}, SCD=1.63185\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SCD = 1.63185$$\\end{document}, SSIM=0.726317\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SSIM = 0.726317$$\\end{document}, and NAB/F=0.006617\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{AB/F} = 0.006617$$\\end{document} shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon’s and Friedman’s tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.

Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy

Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy

Using cooperative coevolution in large-scale black-box constraint satisfaction problems

Solving constrained large-scale global optimization problems poses a challenging task. In these problems with constraints, when the number of variables is measured in the thousands, when the constraints are presented in the form of a black box, and neither the size nor the configuration of the feasible region is known, it is very difficult to find at least one feasible solution. In general, such a problem of finding a feasible region is known as a constraint satisfaction problem. In this paper, we have extended a well-known benchmark set based on constrained optimization problems up to 1000 variables. We have evaluated the CC-SHADE performance, to tackle constraints in large-scale search space. CC-SHADE merges the power of cooperative coevolution and self-adaptive differential evolution. Our extensive experimental evaluations on a range of benchmark problems demonstrate the strong dependence of the performance of CC-SHADE on the number of individuals and the subcomponent number. The numerical results emphasize the importance of using a cooperative coevolution framework for evolutionary-based approaches compared to conventional methods. All numerical experiments are proven by the Wilcoxon test.

Self-adaptive Differential Evolution for Multi-objective Optimization with Local Search and Indicator-based Selection

Self-adaptive Differential Evolution for Multi-objective Optimization with Local Search and Indicator-based Selection

Efficiency analysis of thermoelectric generators

The negative environmental and economic impact, derived from the use of fossil fuels – oil, coal, gas, and other non-renewable sources of energy – has stimulated scientific research on clean and economically viable sources. Photovoltaics and wind power are intermittent sources which stimulates the quest for other energy sources. In this sense, a promising source of energy is made up of Thermoelectric Generators (TEGs), which have the property of converting thermal energy into electrical energy, through the well-known Seebeck Effect. A new methodology for the characterization of these devices is presented in this work. For a robust analysis of the measures, the extraction of parameters is done through the meta-heuristic Self-Adaptive differential evolution method. This is made possible by simultaneous fitting of three equations and five parameters in a consistent autonomous fashion. Besides a robust experimental setup which allows the generation and precise control of the temperature variable as electrical measurements, in a controlled environment, and a thorough statistical analysis is carried for eight TEGs, model SP-184827145 which gives the mean performance of the manufactured devices. The results obtained with low uncertainties indicates that this methodology is a reliable and low-cost option for the characterization of thermoelectric devices.

An evolutionary multi-algorithm based framework for the parametric estimation of proton exchange membrane fuel cell

This paper proposes a multi-strategy, multiple algorithms based hybrid strategy using flower pollination algorithm (FPA), grey wolf optimizer (GWO), INFO, and naked mole rat algorithm (NMRA). The proposed algorithm, named the Flower Grey INFO Naked (FGIN) algorithm, incorporates the most effective equations from the FPA, GWO, INFO, and NMRA. Here FPA’s basic structure following global and local search is used, GWO is meant for providing extensive exploration, whereas INFO and NMRA both contribute towards exploitative search. Dynamic iterative search and population segmentation strategies are incorporated for enhanced performance of FGIN algorithm. For enhanced self-adaptivity, six mutation weight operators are applied to the three parameters of the proposed strategy. FGIN is also subjected to higher dimensional and variable population analysis, and has been found to be highly effective. A deeper analysis using CEC 2005, CEC 2017, CEC 2019 and CEC 2022 benchmark data set is also performed to validate the superiority of the proposed algorithm with respect to success history based differential evolution (SHADE), self-adaptive DE (SaDE), NL-LSHADE-LBC, DE with active archive (JADE), LSHADE-SPACMA, evolutionary algorithms with eigen crossover (EA4eig), extended GWO (GWO-E), NL-LSHADE-RSP-MID, jDE100, and others. The proposed FIGN algorithm is then used for the parametric identification of proton exchange membranes in fuel cells (PEMFC). The optimization challenge of PEMFC is to minimize the sum of squared error (SSE) between the experimental and measured voltage. And also determine, the optimal values of seven unknown parameters for the PEMFC stack’s. To illustrate the potential of the FGIN algorithm is validated by utilizing five well-known commercial PEMFCs, namely NedStack PS6, BCS 500 W, Stack 250 W, Ballard Mark V, and Horizon H-12 Stack. In order to check the effectiveness of the FGIN algorithm, both parametric and non-parametric statistical tests have been conducted, and it has been found that the proposed algorithm performs significantly better.

Structural Determination and Hierarchical Evolution of Transition Metal Clusters Based on an Improved Self-Adaptive Differential Evolution with Neighborhood Search Algorithm.

Determining the optimal structures and clarifying the corresponding hierarchical evolution of transition metal clusters are of fundamental importance for their applications. The global optimization of clusters containing a large number of atoms, however, is a vastly challenging task encountered in many fields of physics and chemistry. In this work, a high-efficiency self-adaptive differential evolution with neighborhood search (SaNSDE) algorithm, which introduced an optimized cross-operation and an improved Basin Hopping module, was employed to search the lowest-energy structures of CoN, PtN, and FeN (N = 3-200) clusters. The performance of the SaNSDE algorithm was first evaluated by comparing our results with the parallel results collected in the Cambridge Cluster Database (CCD). Subsequently, different analytical methods were introduced to investigate the structural and energetic properties of these clusters systematically, and special attention was paid to elucidating the structural evolution with cluster size by exploring their overall shape, atomic arrangement, structural similarity, and growth pattern. By comparison with those results listed in the CCD, 13 lower-energy structures of FeN clusters were discovered. Moreover, our results reveal that the clusters of three metals had different magic numbers with superior stable structures, most of which possessed high symmetry. The structural evolution of Co, Pt, and Fe clusters could be, respectively, considered as predominantly closed-shell icosahedral, Marks decahedral, and disordered icosahedral-ring growth. Further, the formation of shell structures was discovered, and the clusters with hcp-, fcc-, and bcc-like configurations were ascertained. Nevertheless, the growth of the clusters was not simply atom-to-atom piling up on a given cluster despite gradual saturation of the coordination number toward its bulk limit. Our work identifies the general growth trends for such a wide region of cluster sizes, which would be unbearably expensive in first-principles calculations, and advances the development of global optimization algorithms for the structural prediction of clusters.

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

PurposeThe Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.Design/methodology/approachThe idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.FindingsFrom the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.Originality/valueCompared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

Preventive and corrective maintenance-workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation-Optimization framework to solve this challenging large-scale maintenance-workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians’ types, skills and levels, and employment life-cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self-Adaptive Differential Evolution assisted with Chance Constraints (SA-DE-CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model’s sensitivity analysis and managerial insights are also discussed.