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This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

This study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement).

Simulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist.

The demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research.

This study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Patient satisfaction with the Emergency Department (ED) in a hospital is related to the length of stay, and especially to the amount of waiting time for medical treatments. ED overcrowding decreases quality and efficiency, therefore affecting hospitals’ profitability. This paper presents a forecasting and simulation model for resource management of the ED at Moses H. Cone Memorial Hospital. A linear regression forecasting model is proposed to predict the number of ED patient arrivals, and then a simulation model is provided to estimate the length of stay of ED patients, system throughput, and the utilization of resources such as triage nurses, patient beds, registered nurses, and medical doctors. The near future load level of each resource is presented using the proposed models.

The purpose of this paper is to test the effectiveness of fuzzy time series (FTS) forecasting system in a supply chain experiencing disruptions and also to examine the changes in performance as the authors move across different tiers.

A discrete event simulation based on the popular beer game model is used for these tests. A popular ordering management system is used to emulate the behavior of the system when the game is played with human players.

FTS is tested against some other well-known forecasting systems and it proves to be the best of the lot. It is also shown that it is better to go for higher order FTS for higher tiers, to match auto regressive integrated moving average.

This study fills an important research gap by proving that FTS forecasting system is the best for a supply chain during disruption scenarios. This is important because the forecasting performance deteriorates significantly and the effect is more pronounced in the upstream tiers because of bullwhip effect.

Having a system which works best in all scenarios and also across the tiers in a chain simplifies things for the practitioners. The costs related to acquiring and training comes down significantly.

This study contributes by suggesting a forecasting system which works best for all the tiers and also for every scenario tested and simultaneously significantly improves on the previous studies available in this area.

Although the Nash nonlinear grey Bernoulli model (NNGBM(1, 1)) is incomparable with respect to its flexibility over traditional grey models, errors are still inevitable in forecasting. The purpose of this paper is to propose a Fourier residual modified Nash nonlinear grey Bernoulli model (FNNGBM(1, 1)) and use it to forecast the nonlinear time series of the international trade of Chinese high-tech products.

A Fourier series is used to modify the forecasting residual of the NNGBM(1, 1) model, so as to improve its forecasting ability. The parameters optimization of FNNGBM(1, 1) is formulated as a combinatorial optimization problem and is solved collectively using the concept of Nash equilibrium.

The simulation and practical application to fluctuation data both prove that FNNGBM(1, 1) could offer a more precise forecast than NNGBM(1, 1) and the Fourier residual GM(1, 1) (FGM(1, 1)). The import/export data of Chinese high-tech products will maintain rapid growth, with corresponding trade balance enlargement; however, there will be a concomitant decrease in the trade specialization coefficient.

This study is deliberately general in its scope and outlook: its focus is mainly on the overall condition of Chinese high-tech products trade. Future research is recommended to analyze specific industrial trade sectors and extraneous influencing factors.

An effective method is proposed to enhance the accuracy of NNGBM(1, 1) model in forecasting a small sample, nonlinear time series.