In the competitive market of apparel manufacturing, lead time for production plays a significant role in the delivery of the produces impacting the entire supply chain. Nowadays, composite manufacturers are leaning towards delivering within the shortest possible time to retain customers in this competitive market. To meet this challenge, proper production planning either using the correct method or the appropriate tools is a prerequisite condition; otherwise, mills will inevitably suffer losses or fail to drain out the maximum possible profit from the produces and may also suffer from promoting more expenses rather than yielding revenues. This study deals with the development of a linear programming model in order to reduce the complexity of the scheduling problem of a Composite Textile Industry in pursuit of maximizing profit or minimizing production costs. The model is developed considering process segmentation, utilization of machines and other resources, with respect to lead time. Four different components of the lead time are derived and an excel solver is used in solving the model.
The COVID-19 pandemic is the defining health crisis of the world in 2020 and the world economy is affected as well. Bangladesh is also one of the impacted countries, which needs to conduct sufficient tests to identify patients and accordingly adopt measures to limit the massive outbreak of this viral infection. But due to economic drawbacks and also unavailability of testing equipment, Bangladesh is lagging critically behind in test numbers. This study shows a pool testing method named Conditional Cluster Sampling (CCS) that utilizes soft computing and data analysis techniques to reduce the expense of total testing equipment. The proposed method also demonstrates its effectiveness compared to the traditional individual testing method. Firstly, according to patients’ symptoms and severity of their conditions, they are classified into four classes- Minor, Moderate, Major, Critical. After that Random Forest Classifier (RFC) is used to predict the class. Then random sampling is done from each class according to CCS. Finally, using Monte Carlo Simulation (MCS) for 100 cycles, the effectiveness of CCS is demonstrated for different probability levels of infection. It is shown that the CCS method can save up to 22% of the test kits that can save a huge amount of money as well as testing time.
Traffic jam is one of the most widespread problems commonly seen all over the world that causes loss of billions of dollars and useful hours per annum. Traffic lights are used at the intersections to manage the traffic flow, but one problem of those traffic lights is that the color changes at constant intervals irrespective of the traffic density or time of the day. As a result, the measure fails to keep the optimal traffic flow throughout the day at every convergence. This study describes a possible and effective method to systemize traffic lights by considering the traffic density for a specific time of the day. The range of idle time or waiting time within the queue is identified as a function of cycle time, effective green time and traffic arrival rate. After plotting the values within the graph, the trend is observed for idle time with reference to these variables. The lower and upper bound for these independent variables like cycle time, effective green time and traffic arrival rate are decided from observed data sets. As the idle time comes in the form of a range, an estimation of traffic congestion at a particular time is yielded from the method. This traffic flow is analyzed using Arena. The result of idle time using Arena is analogous with the previously analyzed model using MATLAB. Also, at the end of the study, a simulation video is generated that gives practical visual experience.
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