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Mathematical approach for fleet planning under complicated circumstances

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Abstract

In order to facilitate the scientific management of large-sized shipping companies, fleet planning under complicated circumstances has been studied. Based on multiple influencing factors such as the techno-economic status of ships, the investment capacity of company, the possible purchase of new ships, the buying/selling of second-hand vessels and the chartering/renting of ships, a mixed-integer programming model for fleet planning has been established. A large-sized shipping company is utilized to make an empirical study, and Benders decomposition algorithm is employed to test the applicability of the proposed model. The result shows that the model is capable for multi-route, multi-ship and large-scaled fleet planning and thus helpful to support the decision making of large-sized shipping companies.

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Abbreviations

A jb :

The number of ships of type j built in year b before entering research period

B 0 :

Building year of the oldest ship in the fleet at the beginning of the research period, B 0 ⩽ 0

D j :

Designed capacity of ships of type j

E jbt :

Selling price of ships of type j in year t which are built in year b

F jbt :

Idle costs of ships of type j in year t which are built in year b

G :

The total number of routes

K :

The total number of ship types

i 0 :

Discount rate of the time value

M t :

Maximum amount for ship financing in year t

N :

The number of years within the planning period

N jbt :

The number of ships of type j built in year b and available in the charter market in year t

NT:

Lifetime of ship

o jbt :

The number of ships of type j built in year b and laid-up in year t

P jbdt :

Rent of ships of type j built in year b and chartered in year t during the chartering period d

Q jbt :

The number of ships of type j built in year b which chartered in before research period and hired off at the end of year t

R jbht :

Average voyage gross profit of ships of type j built in year b and assigned to route h in year t

R t :

The full set of routes in year t

s jbt :

The number of ships of type j built in year b and purchased in year t

t jh :

Round voyage time needed by the ships of type j on the route h

T j :

Service days of ships of type j each year

u jbdt :

The number of ships of type j built in year b and chartered in year t during the chartering period d

υ jbdt :

The number of ships of type j built in year b and chartered out in year t during the chartering period d

w jbt :

The number of ships of type j built in year b and sold in year t

W jb :

Remaining value of ships of type j built in year b at the end of research period

WT ht :

The maximum transport demand of route h in year t

x jbht :

The number of ships of type j built in year b and assigned to route h in year t

y jbht :

The voyage number of ships of type j built in year b and assigned to route h in year t

α :

The percentage of broker fees to ships’ purchasing price

β :

Coefficient reflecting the importance that the researcher gives to the physical value of the fleet at the end of the research period, 0 ⩽ β ⩽ 1

θ jht :

Deadweight utilization of ships of type j on the route h in year t

μ :

The percentage of broker’s commissions to rents for chartering out ships

Φ 4ht :

The full set of ships of all types on route h in year t

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Authors and Affiliations

  1. China Waterborne Transport Research Institute, Beijing, 100088, China

    Qiu-ping Yang  (杨秋平)

  2. Transportation Management College, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Hao Zhang  (张 皞), Hui-yun Sang  (桑惠云) & Xin-lian Xie  (谢新连)

Authors
  1. Qiu-ping Yang  (杨秋平)
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  2. Hao Zhang  (张 皞)
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  3. Hui-yun Sang  (桑惠云)
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  4. Xin-lian Xie  (谢新连)
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Corresponding author

Correspondence to Qiu-ping Yang  (杨秋平).

Additional information

Foundation item: the Doctoral Programs Foundation of Ministry of Education of China (No. 20102125110002)

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Yang, Qp., Zhang, H., Sang, Hy. et al. Mathematical approach for fleet planning under complicated circumstances. J. Shanghai Jiaotong Univ. (Sci.) 19, 241–250 (2014). https://doi.org/10.1007/s12204-014-1495-5

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