Bunker fuel cost and freight revenue optimization for a single liner shipping service

This paper shows the results of an in-depth techno-economic analysis of the public transport sector in a small to midsize city and its surrounding area. Public battery-electric and hydrogen fuel cell buses are comparatively evaluated by means of a total cost of ownership (TCO) model building on historical data and a projection of market prices. Additionally, a structural analysis of the public transport system of a specific city is performed, assessing best fitting bus lines for the use of electric or hydrogen busses, which is supported by a brief acceptance evaluation of the local citizens. The TCO results for electric buses show a strong cost decrease until the year 2030, reaching 23.5% lower TCOs compared to the conventional diesel bus. The optimal electric bus charging system will be the opportunity (pantograph) charging infrastructure. However, the opportunity charging method is applicable under the assumption that several buses share the same station and there is a “hotspot” where as many as possible bus lines converge. In the case of electric buses for the year 2020, the parameter which influenced the most on the TCO was the battery cost, opposite to the year 2030 in where the bus body cost and fuel cost parameters are the ones that dominate the TCO, due to the learning rate of the batteries. For H2 buses, finding a hotspot is not crucial because they have a similar range to the diesel ones as well as a similar refueling time. H2 buses until 2030 still have 15.4% higher TCO than the diesel bus system. Considering the benefits of a hypothetical scaling-up effect of hydrogen infrastructures in the region, the hydrogen cost could drop to 5 €/kg. In this case, the overall TCO of the hydrogen solution would drop to a slightly lower TCO than the diesel solution in 2030. Therefore, hydrogen buses can be competitive in small to midsize cities, even with limited routes. For hydrogen buses, the bus body and fuel cost make up a large part of the TCO. Reducing the fuel cost will be an important aspect to reduce the total TCO of the hydrogen bus.

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Research on Revenue Optimization of Service Composition for Large-Scale Service Requests

2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) ◽

10.1109/icccbda51879.2021.9442607 ◽

2021 ◽

Author(s):

Xun Yuan ◽

Liping Huang

Keyword(s):

Service Composition ◽

Large Scale ◽

Revenue Optimization

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Revenue optimization in energy networks involving self-scheduled demand and a smart grid

Computers & Operations Research ◽

10.1016/j.cor.2021.105366 ◽

2021 ◽

pp. 105366

Author(s):

Sezin Afşar ◽

Luce Brotcorne ◽

Patrice Marcotte ◽

Gilles Savard

Keyword(s):

Smart Grid ◽

Revenue Optimization ◽

Energy Networks

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Economic Development Based on a Mathematical Model: An Optimal Solution Method for the Fuel Supply of International Road Transport Activity

Energies ◽

10.3390/en14102963 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2963

Author(s):

Melinda Timea Fülöp ◽

Miklós Gubán ◽

György Kovács ◽

Mihály Avornicului

Keyword(s):

Decision Making ◽

Ad Hoc ◽

Market Competition ◽

Optimal Solution ◽

Cost Savings ◽

Cost Effective ◽

Optimization Method ◽

Optimal Decision ◽

Environmental Damage ◽

Fuel Cost

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.

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