scholarly journals Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

2018 ◽  
Vol 10 (3) ◽  
pp. 30 ◽  
Author(s):  
Bo Sun ◽  
Ming Wei ◽  
Senlai Zhu
Keyword(s):  
Optimal Design ◽  
Time Windows ◽  
Multiple Time ◽  
Feeder Transit ◽  
Transit Services ◽  
Multiple Time Windows

scholarly journals Maritime inventory routing problem with multiple time windows

2017 ◽  
Vol 13 (5) ◽  
pp. 1-27
Author(s):  
Nurhadi Siswanto ◽  
◽  
Stefanus Eko Wiratno ◽  
Ahmad Rusdiansyah ◽  
Ruhul Sarker ◽  
...  
Keyword(s):  
Time Windows ◽  
Multiple Time ◽  
Inventory Routing ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Multiple Time Windows ◽  
Maritime Inventory Routing

scholarly journals Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018

2019 ◽  
Vol 6 (7) ◽  
pp. 180643 ◽  
Author(s):  
J. C. Gerlach ◽  
G. Demos ◽  
D. Sornette
Keyword(s):  
Time Windows ◽  
Crash Risk ◽  
Multiple Time ◽  
Market Growth ◽  
Time Period ◽  
Quantitative Metrics ◽  
Dollar Price ◽  
Market Regime ◽  
Multiple Time Windows

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power-Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators , defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious ‘present’ times t 2 before the crashes, we employ a clustering method to group the predicted critical times t c of the LPPLS fits over different time scales, where t c is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.

scholarly journals PENERAPAN SIMULASI DAN RELIABILITAS PADA MODEL VEHICLE ROUTING PROBLEM (VRP) DENGAN PERMINTAAN PROBABILISTIK

2018 ◽  
Vol 9 (1) ◽  
pp. 189-204
Author(s):  
Ary Arvianto ◽  
Rizal Luthfi Nartadhi ◽  
Diana Puspita Sari ◽  
Wiwik Budiawan
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Multiple Time ◽  
Routing Problem ◽  
Fleet Size ◽  
Split Delivery ◽  
Multiple Product ◽  
Multiple Time Windows ◽  
Fleet Size And Mix

Vehicle Routing Problem (VRP) memiliki aplikasi yang penting di bidang manajemen distribusi, sehingga  menjadi  salah  satu  contoh  masalah  yang  banyak  dipelajari  dalam  literatur  optimasi kombinatorial dan diakui sebagai salah satu pengalaman tersukses dalam riset operasi. Dalam penelitian ini dilakukan simulasi dari penelitian [1] dengan memperhatikan varian VRP homogeneous fleet  size and mix vehicle routing, multiple trips, multiple product and compartements, split delivery, dan multiple time windows dan permintaan tidak pasti (Probabilistic Demand). Hasil yang didapatkan bahwa model yang dibuat telah mampu merepresentasikan penelitian sebelumnya [1] dengan verifikasi hasil   yang sama. Permintaan tidak pasti   ditunjukkan dengan melakukan pengurangan kapasitas sebesar 5%   dan 10% dengan hasil bahwa  dengan mengurangi  kapasitas sebesar 5% terjadi permintaan pelanggan yang tidak tercukupi di beberapa pos, sedangkan pada 10% semua   permintaan dapat tercukupi. Namun dari segi biaya pada nilai 10% memiliki biaya yang lebih tinggi daripada 5%   hal ini dikarenakan rute   yang dihasilkan lebih banyak sehingga mengakibatkan penggunaan kapal lebih banyak dilakukan.

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