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The purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.

Different from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.

A case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.

The casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.

Considering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.

(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

Smooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.

Therefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.

During experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.

After literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.

For this, the proposed model introduces a new image equation that is solved by the Runge-Kutta fourth order method, which makes it possible to optimize the sequence prediction function. The novel model can then capture the characteristics of the input data and completely excavate the system's evolution law through a learning procedure.

The new model has a broader applicability range as a result of this technique, as opposed to grey models, which have fixed structures and are sometimes over specified by too strong assumptions. For experimental purposes, the neural differential grey model is implemented on two real samples, namely: production of crude and consumption of Cameroonian petroleum products. For validation of the new model, results are compared with those obtained by competing models. It appears that the precisions of the new neural differential grey model for prediction of petroleum products consumption and production of Cameroonian crude are respectively 16 and 25% higher than competing models, both for simulation and validation samples.

This article also takes an in-depth look at the mechanics of the new model, thereby shedding light on the intrinsic differences between the new model and grey competing models.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The primary aim of this article is to ascertain the modalities of leveraging Lean Six Sigma (LSS) for Industry 4.0 (I4.0) with special reference to the process industries. Moreover, it intends to determine the applicability of simulation-based LSS in the automation of the mineral water industry, with special emphasis on the robust design of the control system to improve productivity and performance.

This study adopts the action research methodology, which is exploratory in nature along with the DMAIC (define-measure-analyze-improve-control) approach to systematically unearth the root causes and to develop robust solutions. The MATLAB simulation software and Minitab statistical software are effectively utilized to draw the inferences.

The root causes of critical to quality characteristic (CTQ) and variation in purity level of water are addressed through the simulation-based LSS approach. All the process parameters and noise parameters of the reverse osmosis (RO) process are optimized to reduce the errors and to improve the purity of the water. The project shows substantial improvement in the sigma rating from 1.14 to 3.88 due to data-based analysis and actions in the process. Eventually, this assists the management to realize an annual saving of 20% of its production and overhead costs. This study indicates that LSS can be applicable even in the advent of I4.0 by reinforcing the existing approach and embracing data analysis through simulation.

The limitation of this research is that the inference is drawn based on a single case study confined to process industry automation. Having said that, the methodology deployed, scientific information related to optimization, and technical base established can be generalized.

This article is the first of its kind in establishing the integration of simulation, LSS, and I4.0 with special reference to automation in the process industry. It also delineates the case study in a phase-wise manner to explore the applicability and relevance of LSS with I4.0. The study is archetype in enabling LSS to a new era, and can act as a benchmark document for academicians, researchers, and practitioners for further research and development.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

In the present era, executives are shifting keenly toward industrial Internet of things (IIoTs) spheres. It is observed that IIoTs spheres become a key for each industry to grow up and bear the largest entrepreneurship opportunities globally and is linked to improve the shifting sphere of publics (SSPs). The core objective of research work is SSPs, which is nexus on secondary objectives. The authors proposed the two DSSs ( decision support systems) to full fill secondary objectives as discussing: In case of first objective, the authors proposed a fuzzy-DSS, which assists the executives to identify the weak and poor performing IIoTs spheres so that performance of IIoTs spheres can be accelerated. In case of second objective, grey-DSS aids the same executives to evaluate and benchmark alternative partner under considered IIoTs spheres so that the best partner can be chosen by company 4.0.

The authors conducted the significant systematic literature review and realistic empirical survey in the context of industry IIoTs spheres and extract the appropriate IIoTs spheres. Next, the authors built a framework by compiling the global standardized IIoTs spheres. The framework is utilized to build the two DSSs such as fuzzy- and grey-DSS (to full fill secondary objectives). The both DSSs are simulated by acting on a case study. The authors implemented the fuzzy set coupled with degree of similarity approach on proposing framework as a part of first case-objective and hybrid technique accompanied with grey set on same framework as a part of second case-objective, respectively.

A South African automobile parts manufacturing company is investigated as a case study company 4.0 for the prototype testing and simulation of DSSs. The performance gaps are computed and measured by subtracting each sphere's weight of functional units (FUs) from evaluated ideal weight. The weak performing spheres and FUs are suggested to be improved in future as a part of first objective. Next, A3 parts supplier/partner is advised as the best alternative by simulating the grey-DSS under IIoTs framework as a part of second case-objective. Both secondary objectives (two DSSs) are framed to attain the core objective (SSPs).

As discussed, the core objective of research work is to attain the SSPs, linked to secondary objectives. The research work integrates the knowledge and thinking of SSPs as well as IIoTs researchers to create the novel mathematical and statistical IIoTs in focusing on advance SSPs networks. The research work is momentous for entire Industry 4.0 companies, which troubles to bear more entrepreneurship opportunities (improving the SSPs) at global standard.

Artificial‐life techniques – specifically, agent‐based models and evolutionary learning algorithms – provide a potentially powerful new approach to understanding some of the fundamental processes of war. This paper introduces a simple artificial‐like “toy model” of combat called Enhanced ISAAC Neural Simulation Tool (EINSTein). EINSTein is designed to illustrate how certain aspects of land combat can be viewed as self‐organized, emergent phenomena resulting from the dynamical web of interactions among notional combatants. EINSTein's bottom‐up, synthesist approach to the modeling of combat stands in stark contrast to the more traditional top‐down, or reductionist approach taken by conventional military models, and represents a step toward developing a complex systems theoretic toolbox for identifying, exploring, and possibly exploiting self‐organized emergent collective patterns of behavior on the real battlefield. A description of the model is provided, along with examples of emergent agent patterns and behaviors.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

The purpose of the present study was to review the thermo-mechanical challenges of reflowed lead-free solder joints in surface mount components (SMCs). The topics of the review include challenges in modelling of the reflow soldering process, optimization and the future challenges in the reflow soldering process. Besides, the numerical approach of lead-free solder reliability is also discussed.

Lead-free reflow soldering is one of the most significant processes in the development of surface mount technology, especially toward the miniaturization of the advanced SMCs package. The challenges lead to more complex thermal responses when the PCB assembly passes through the reflow oven. The virtual modelling tools facilitate the modelling and simulation of the lead-free reflow process, which provide more data and clear visualization on the particular process.

With the growing trend of computer power and software capability, the multidisciplinary simulation, such as the temperature and thermal stress of lead-free SMCs, under the influenced of a specific process atmosphere can be provided. A simulation modelling technique for the thermal response and flow field prediction of a reflow process is cost-effective and has greatly helped the engineer to eliminate guesswork. Besides, simulated-based optimization methods of the reflow process have gained popularity because of them being economical and have reduced time-consumption, and these provide more information compared to the experimental hardware. The advantages and disadvantages of the simulation modelling in the reflow soldering process are also briefly discussed.

This literature review provides the engineers and researchers with a profound understanding of the thermo-mechanical challenges of reflowed lead-free solder joints in SMCs and the challenges of simulation modelling in the reflow process.

The unique challenges in solder joint reliability, and direction of future research in reflow process were identified to clarify the solutions to solve lead-free reliability issues in the electronics manufacturing industry.