Covid-19 outbreak has caused economic policy uncertainties. The first
COVID-19 case was reported in Wuhan, China at the end of 2019. The virus
spread escalated in volume during the Chinese New Year festival. World
Health Organisation declared a global health emergency on January 30,
2020. The global financial market was badly hit when oil price slumped to
over 30% and oil price war also occurred between Saudi and Russia. The
stock market, too displayed signs of being impacted by the virus outbreak. It
is very important to determine if COVID-19 has affected economic
uncertainty and oil prices or the oil price fall has affected the economic
instability and stock market volatility Three models were used in this study
to analyse the relationship between the recent spread of COVID-19 in
Malaysia, Malaysia stock market, oil prices in Malaysia and Global
Economic Policy Uncertainty (GEPU) in time-frequency domain. The
coherence wavelet methods were used to analyse the movement of each
variable and to evaluate the interactions between the selected variables from
January 25, 2020 to May 25, 2020. The Wavelet-based Granger Causality
were applied to test the robustness of the coherence wavelets. Three main
conclusions from this study were i) oil prices were influenced by stock market
and GEPU index, ii) stock markets and GEPU index had interactions with
the pandemic and iii) short term effects existed between the pandemic and
oil prices. More accurate results concerning the volatility of GEPU, stock
market and oil price, can be obtained in future research works in this area if
Malaysia Economic Policy Uncertainty index is used.
The Morning Shift (MS) flow of passengers on Thursday is similar to any other day in the week at an urban train station. However, passenger congestion occurring in the Evening Shift (ES) affects the system behaviour. The system has been showing this characteristic over the years. However,
only one counter is opened. This counter also sells different types of tickets. Thus, the system is not cost-effective. The study aims to determine the optimal number of counters that should be opened on Thursday. In order to solve this problem, the study has applied Poisson Queuing Simulation (PQ) to the MS and ES data. Findings indicate that running one or two counters in MS
maintains the mean of wait time in-queue and in-system at less than one minute, while the mean of in-queue and in-system passengers is also at one person. Extra cost has to be incurred to hire another teller; thus, one counter is optimal. For ES, the service rate was only slightly higher than the arrival rate. Based on the mean number of in-queue and in-system passengers, there was no way that one counter can efficiently service the queue that was formed. A simulation was done to determine if there was a need to set up a two-counter or a three-counter system. Although a two-counter system will be idle 47% of the time, it was not cost-effective for the management to two
extra tellers for a three-counter system. The management must take future corrective measures. Based on the findings, one counter is optimal for MS, but cost-effectiveness tests must confirm that two counters are optimal for ES. Besides, the management should also identify actions that can be taken during the 47% idle system time in ES.