In this paper we developed a VRP model for multiple routes, multiple time windows for multiple products and heterogeneous vehicles. The solution were constructed using a heuristic approach, i.e, a sequential insertion algorithm. Additionally, this model is applied to solve fuel distributions for eight customers in East Nusa Tenggara. It needs two tankers with capacity of 4700 kilo liters, so that those distributions can be accomplished with a minimum number of vehicles, total completion time, and range of completion time. The result of this study shows that for a heterogeneous vehicles problem, a vehicle with the largest capacity may not necessarily be the vehicle that provides an optimal solution. Moreover, advance trials should be conducted by providing a limited number of tankers for each tanker capacity, so the description of heterogeneous vehicles becomes more visible. In the future research, the solution will be improved by utilizing relocation techniques.
Purpose This study aims to create the causal relationship between transportation behavior to Karimunjawa, the number of tourists and the amount of CO2 produced; calculate the reduction of CO2 emissions from the transportation to Karimunjawa based on several proposed policy scenarios; and formulate the managerial implication and recommendation to support the implementation of several proposed policy scenarios. Design/methodology/approach This study develops a system dynamics‐based model by using three sub-systems, i.e. “the number of tourist sub-system,” “the switching behavior of tourist travel sub-system” and “the CO2 emission sub-system.” Findings The simulation results have shown that, under the current situation, tourist travel behavior should be changed to maximum condition to get the minimum CO2 emission. Improvement of the behavior of tourist in selecting the mode of transportation and the departure point of mini-tour bus and ferry are an effective way to reduce the CO2 emission. Research limitations/implications This study only considers limited variables as the driver of the level of change of the capacity of Karimunjawa and the road as well as the variables as the driver of tourism growth. This study only focuses on CO2 emission from the direct impacts of tourist travel; this study does not consider the indirect impact of tourism activity on CO2 emissions. International air travel is not included in the present study. Practical implications From a managerial perspective, this study demonstrates that change in the tourist travel behavior is generally not effective in triggering CO2 emission reduction, unless it is accompanied by the strict restriction policy related to the tourist route. Social implications This study has the potential to raise societal awareness that the causality of tourist growth and CO2 emissions should be seen as the impact of tourist travel behavior. In this case, to modify the travel behavior, tourist needs to change their mode of transportation to more sustainable transportation. Originality/value This paper intends to fill the literature gap of the effect of tourism growth from two perspectives, namely, tourist travel behavior and environmental. The modeling of tourist transport and CO2 emission will provide an overview of the selection of the problem-solving mode for tourist transport that can give a significant contribution to the greenhouse gas emissions reduction to the environmental.
The electricity demand share in the household sector will increase from 49% in 2018 to 58% in 2050 as predicted. This issue is particularly caused by the household growth number which may increase from 67 million in 2018 to approximately 80 million in 2050. For the household customer number are increasing, utilizing rooftop as the base of solar power plants can be an effective and efficient solution. In addition, the government regulation supports the acceleration and development of new and renewable energy. This research aims to analyze the technical economic feasibility of rooftop solar power plant system with a household-scale on-grid system in Semarang City. Through PVSyst 6.43 and RetScreen software also equipped with several primary components, this household-scale rooftop solar power plant investment plan is estimated to have an average revenue return estimated in 10 years later.
Indikator adalah ukuran kuantitatif dan kualitatif yang menggambarkan tingkat pencapaian suatu sasaran atau tujuan yang telah ditetapkan. Fakultas Teknik Universitas Diponegoro yang merupakan Fakultas terbesar sekaligus sebagai penyumbang hasil riset terbesar setiap tahunnya telah mempunyai Renstra 2015-2020 sebagai pedoman. Didalam Renstra tersebut terdapat IKFT, yaitu indikator-indikator untuk mengukur kapasitas FT serta pencapaian FT setiap tahunnya. Indikator khusus untuk menilai kapasitas riset dan pencapaian riset masih minim dan masih perlu dikembangkan. Penelitian ini bertujuan mengembangkan indikator kunci untuk menilai kapasitas riset Fakultas Teknik Universitas Diponegoro. Kapasitas riset dapat ditinjau dari empat faktor utama keberlanjutan penelitian yaitu antara lain : faktor keuangan, manajemen organisasi, pendukung riset dan infrastruktur. Dalam penelitian ini terdapat sembilan indikator kunci yang dikembangkan dari jurnal dan sumber literatur lainnya. Terdapat 11 KPI sumber Renstra FT UNDIP, 9 KPI yang dikembangkan. Indikator tersebut digolongkan sesuai faktor keberlanjutan penelitian antara lain : 6 KPI faktor financial, 3 KPI faktor manajemen organisasi, 6 KPI pendukung riset dan 5 KPI infrastructure. Setelah dikembangkan dilakukan penilaian dengan terlebih dahulu melakukan pembobotan KPI menggunakan metode AHP dan kemudian dilakukan scoring dengan metode OMAX. Hasil dari penilaian tersebut didapatkan Indeks Pencapaian setiap kategori antara lain yaitu : financial 7,906, manajemen organisasi 5,628, pendukung riset 4,713 dan infrastruktur 8,171. Berdasarkan penilaian tabel OMAX setiap kategori didapatkan 7 KPI yang posisi lampu merah, 4 KPI yang posisi lampu kuning dan 9 KPI posisi lampu hijau. Tahap selanjutnya dirancang rekomendasi perbaikan dari beberapa literatur untuk meningkatkan kapasitas riset dan kemudian divalidasi dengan menggunakan metode delphi.
A traffic accident was one of the leading cause of death in Indonesia. Toll Road is one of the places where traffic accidents occur. In 2007-2017 there were 501 accidents at Semarang Toll Road. Accident in Semarang Toll Road has a variety of severity. The most severe case is death. A traffic accident can lead to death. One of the ways to decrease the number of the accident was decreased the severity of the accident. This achieved by making a prediction model. The prediction model can predict the severity of the accident based on the attribute affecting the severity of the accident. In this research, Days, Type of Road, Weather, Condition of Road, Time of the accident, Sex of Driver, and Type of Vehicle were chosen as attributes to make prediction model of accident severity. Naive Bayes algorithm was used to make the model which can predict accident severity. The result was an accident prediction model with an accuracy of 39.49% to predict accident severity and the probability of an accident.
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