scholarly journals Optimizing Ship Speed to Minimize Total Fuel Consumption with Multiple Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Jae-Gon Kim ◽  
Hwa-Joong Kim ◽  
Hong Bae Jun ◽  
Chong-Man Kim
Keyword(s):  
Fuel Consumption ◽  
Time Windows ◽  
Optimal Solution ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Multiple Time ◽  
Total Fuel Consumption ◽  
Multiple Time Windows ◽  
Ship Speed

We study the ship speed optimization problem with the objective of minimizing the total fuel consumption. We consider multiple time windows for each port call as constraints and formulate the problem as a nonlinear mixed integer program. We derive intrinsic properties of the problem and develop an exact algorithm based on the properties. Computational experiments show that the suggested algorithm is very efficient in finding an optimal solution.

scholarly journals Location Routing Problem with Consideration of CO2 Emissions Cost: A Case Study

2020 ◽  
Vol 21 (2) ◽  
pp. 225-234
Author(s):  
Ananda Noor Sholichah ◽  
Y Yuniaristanto ◽  
I Wayan Suletra
Keyword(s):  
Co2 Emissions ◽  
Time Windows ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Study Results

Location and routing are the main critical problems investigated in a logistic. Location-Routing Problem (LRP) involves determining the location of facilities and vehicle routes to supply customer's demands. Determination of depots as distribution centers is one of the problems in LRP.  In LRP, carbon emissions need to be considered because these problems cause global warming and climate change. In this paper, a new mathematical model for LRP considering CO2 emissions minimization is proposed. This study developed a new  Mixed Integer Linear Programming (MILP)  model for LRP with time windows and considered the environmental impacts.  Finally, a case study was conducted in the province of Central Java, Indonesia. In this case study, there are three depot candidates. The study results indicated that using this method in existing conditions and constraints provides a more optimal solution than the company's actual route. A sensitivity analysis was also carried out in this case study.

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 Liner Shipping Schedule Design for Near-Sea Routes Considering Big Customers’ Preferences on Ship Arrival Time

2020 ◽  
Vol 12 (18) ◽  
pp. 7828 ◽  
Author(s):  
Xi Jiang ◽  
Haijun Mao ◽  
Yadong Wang ◽  
Hao Zhang
Keyword(s):  
Time Windows ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Time Preferences ◽  
Mixed Integer ◽  
Shipping Industry ◽  
Fleet Size ◽  
Arrival Times ◽  
Soft Time Windows ◽  
Mixed Integer Linear Optimization

There usually exist a few big customers at ports of near-sea container shipping routes who have preferences on the weekly ship arrival times due to their own production and sale schedules. Therefore, in practice, when designing ship schedules, carriers must consider such customers’ time preferences, regarded as weekly soft-time windows, to improve customer retention, thereby achieving sustainable development during a depression in the shipping industry. In this regard, this study explores how to balance the tradeoff between the ship total operating costs and penalty costs from the violation of the weekly soft-time windows. A mixed-integer nonlinear nonconvex model is proposed and is further transformed into a mixed-integer linear optimization model that can be efficiently solved by extant solvers to provide a global optimal solution. The proposed model is applied to a near-sea service route from China to Southeast Asia. The results demonstrate that the time preferences of big customers affect the total cost, optimal sailing speeds, and optimal ship arrival times. Moreover, the voyage along a near-sea route is generally short, leaving carriers little room for adjusting the fleet size.

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