The effectiveness on solid waste management is a major importance to societies. Numerous generation of solid waste from our daily activities has risked for our communities. These due to rapid population grow and advance in economic development. Moreover, the complexity of solid waste management is inherently involved large scale, diverse and element of uncertainties that must assist stakeholders with deviating objectives. In this paper, we proposed a system dynamics simulation by developing a stock flow diagram to illustrate the solid waste generation process and waste recycle process. The analysis highlights the impact on increasing the number of population toward the amount of solid waste generated and the amount of recycled waste. The results show an increment in the number of population as well as the amount of recycled waste will decrease the amount of waste generated. It is positively represent the achievement of government aim to minimize the amount of waste to be disposed by year 2020.
Designing a conference scheduling involves various factors that need to be considered in order to fulfill the participants' preferences. Capacity planning problem is a main focus in this study in order to assign the papers that are to be presented into time slots based on paper of interest. All the presentation papers are assigned efficiently to avoid too many participants in a particular session. Goal Programming model has been developed to produce a schedule that maximizes the participants' satisfaction in terms of capacity planning subject to the capacity constraints. The proposed schedule shows that almost 93% of the time slots are fully utilized and fulfill the capacity constraint. Moreover, it could help the conference committee to better understand the preferences of participant prior to construct the schedule in future.
Solid waste management and environmental care are essential in all countries to ensure sustainability at all times. But awareness on the importance of both is not yet satisfactory. This is evidenced by the increasing amounts of solid waste generated and the lack of oversight due to public community involvement. The purpose of this study is to investigate the sustainability of public awareness on solid waste management and environmental care among the population of Malaysia. This study uses an online questionnaire for the data collection process. The logistic regression method is used to analyze the data. A total of 422 respondents represented by 20.9% were male while 79.1% were female. The results showed that only 37.91% of respondents were aware of solid waste management and concern for the environment. In fact, more than 50% of respondents are still less aware of solid waste management and environmental concerns. This shows that most of the communities in Malaysia had a lack awareness on the importance of waste management and environmental care. The culture and values of individual responsibility play an important role in ensuring that environmental sustainability is maintained. Solid waste management and environmental care coalitions need to have broad coverage across the country so that all communities are aware and understand the shared responsibility for waste management and environmental care. This awareness will help achieve the national agenda through Joint Prosperity as well as the Sustainable Development Goals by 2030.
This study aims at analyzing, comparing and selecting the best predictive model based on organic waste generation at the UiTM Tapah Campus administration cafe. The data collection period begins Monday through Friday from 4th Mac 2019 until 20th April 2020. There are 2 bins that are labeled as Bin 1 and Bin 2 at the Administration Cafe on the UiTM Tapah Campus. Using the Risk Simulator Software, the Moving Average Model, Exponential Smoothing Model, and Holt-Winters Model were adapted and analyzed. The best model is chosen based on error compare meaning. There are three statistical errors in this study which are used for comparison purposes as RMSE, MSE, and MAD. As a result, the lowest error values for both Bin 1 and Bin 2 are shown on the Moving Average model. Therefore, the Moving Average Model can be concluded at Administration Cafe in UiTM Tapah Campus as the best equipped model based on current organic waste generation. The best model will be used in future at Administration Cafe in UiTM Tapah Campus to predict organic waste generation.
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