Brief presentation fleet of ships optimized programming

Programa de Atualização Profissional
Fleet of Ships Optimized Programming
methodology summary
by Gláucio Bastos, M.B.A., Ch.E.

abstract
 target: from true characteristics of various types of fleets of
ships and service vessels, cargoes and ports, it is generated
a large number of ships scheduling initial solutions to serve
loads in their ports, which will be refined in several
optimization steps until achievement of a viable solution for
greater financial return; this solution is then refined to
produce the best 'timing’ decision for shipments starting,
considering constraints in ports, docks availability, and stock
levels in case of bulk cargo

neeed
 during the last decades there has been an increase in
competition among shipping companies, squeezing profit
margins to a minimum
 this situation is caused and amplified by the consolidation of
the manufacturing sector, giving greater market power for
cargo owners and shippers
 to reduce this imbalance, there have been many mergers
among shipping companies in the pursuit of scale
economies gains

neeed
 this trend of mergers and groupings leads to larger fleets,
distributed geographically in extensive areas which require
more efficient management
 this picture of high complexity and dynamism makes
uncertainties and delays inevitable phenomena in the
operation of shipping, caused by factors such as adverse
weather conditions, mechanical breakdowns, strikes and
insufficient infrastructure, both in port and on board

neeed
 in this context and considering the long lead times involved,
it is imperative to shipping companies ensure efficiency by
generating quality schedules based on advanced methods
for decision support in terms of reliability and punctuality,
which currently are the shortest and most effective way for
achieving cost savings and generating higher profit margins

fleet characteristics
 in shipping can be distinguished 03 different modes of
operations:
 industrial - the cargo owner also owns a fleet of vessels
to transport it, the load is a certain amount of product to
be shipped from one port to another within time
windows, all charges are mandatory and must be met by
the fleet available, no optional spot cargoes, the goal is
to minimize costs

fleet characteristics
 in shipping can be distinguished 03 different modes of
operations:
 tramp – operations that are similar to a taxi service,
where ships carry available loads, is normal to an
operator having some contractual charges and other
options that can be met if considered profitable, the goal
is to maximize profit; and
 liner – operations that are similar to regular shipping
lines, where the boats sail by pre-booked and published
itineraries seeking to maximize their profit

issue description
 the problem of ship schedule as analyzed here, applies to
any type of fleet and can be a maximization problem of cost
or profit and may involve contracts of affreightment (COA),
signed to transport certain amounts or types of cargo
between specified ports or among specific operations
within a certain period, for an agreed payment per tonne or
per service
 there are cases where the fleet does not have enough
capacity to meet all COAs during the planning horizon, so
some loads can be met by spot charters, chartered for just a
trip

issue description
 time windows are imposed for loading and sometimes also
for unloading
 depending on the operation, the ships can serve single or
multiple loads simultaneously with a new charging port
being played and some cargo remaining on board
 operations are considered with existing vessels without
changes in fleet size for the short term, out of such changes
are not interesting

issue description
 fixed costs are disregarded, since do not influence the
planning of optimum routes and schedules
 variable costs of travel are considered mainly from
consumption of fuel oil and diesel
 it is considered a heterogeneous fleet of ships with special
features, including different cost structures and load
capacities
 fees are charged for use of the port and channel, depending
on the size of the vessel

related data
 the planning horizon is 3,600 hours or 150 days
 freight value (US$ / knot / m3)
 general data:
 buffer–times for trip and port (hr) – slack time in case of
possible delays
 IFO price – interm.(trip) and MDO (port) (US$ / ton)
 time window for standard operation at port (hr)
 cargo data:
 identification, volum (m3), weight (ton)
 load port, unload port
 cargo time availability at loading port (hr), cargo time of
arrival at unloading port (hr)

related data
 ship data:
 identification, volumetric capacity (m3), dead-weight
tonnage - dwt (ton)
 mean speed (knot / hr)
 IFO consumption (ton / day), MDO consumption idle at
port (ton / day)
 port data:
 identification, distance between ports of trip (knot)
 produtivity (m3 / hr), port fee (US$), cargo operation fee
(US$ / m3)
 available ETAs for arrivings (hr)

solution
• using MSLS heuiístics *
 solution steps:
 1ª: large amount (no_init) of initial solution are
generated by a partially random insertion heuristic of
cargo on ships schedules
 2ª: a certain amount (no_quick) of the best (by profit or
cost criterion) initial solutions are improved by a fast
local search heuristic
 3ª: it is selected a certain amount (no_extended) of the
best quick local search solutions to be enhanced by a
extended local search heuristic

results
 from data of:
 13 ships
 50 cargoes
 9 ports located among southeast USA and west Europe
 the following developments in profit fleet were obtained:
 to no_init = 100, no_quick = 6, no_extended = 1
 init. sol.: worst = $ 628.638 / best = $ 1.127.639
 fast sol.: worst = $ 1.109.300 / best = $ 1.168.316
 extended sol.: $ 1.295.348
 to no_init = 1000, no_quick = 14, no_extended = 4
 init. sol.: worst = $ 576.334 / best = $ 1.334.752
 fast sol.: worst = $ 1.183.747 / best = $ 1.334.752
 extend.sol.: worst = $ 1.237.389 / best = $ 1.334.752

results
 conclusions:
 to a smaller number of initial solutions, it is evident the
effective contribution of each optimization level to
achieve the best final solution
 for a large number of initial solutions, the optimization
included in his generation algorithm is sufficient to reach
the best solution, even before the next two optimization
levels
 so as was hit the nearest optimal solution, it can
hardly be optimized and is better than that achieved
with a smaller number of initial solutions, featuring
the robustness of the results

results
optimal solution – cargoes data: volums

results
optimum solution – cargoes data: times
1.ETA load port
2.trip time
3.load port time
4.unload port time
5.reduced trip time

results
optimum solution – scheduling result: cargoes through ships and ports
1.ship
2.load port
3.unload port

results
optimum solution– scheduling result: routes of loaded vessels
Port
2
Port
3
Port
1
Port
9
Port
5
Port
6
Port
7
Port
4
Port
8
Ship 9
Ship 10
Ship 12
Ship 1
Ship 3
Cargo
3
Cargo
5
Cargo
14
Cargo
10
Cargo
15
Ship 11
Cargo
16
Ship 2
Cargo
22
Ship 7
Cargo
24
Ship 5
Cargo
25
Ship 13
Cargo
27
Cargo
29
Cargo
31
Ship 6
Cargo
32
Ship 8
Cargo
42
Ship 4
Cargo
46

issue extension
 if the fleet is designed to bulk transport, e.g. LNG, between
liquefaction port (load) and regasification port (unload), it is
considered availability of docks and stock (between
maximum and minimum levels) at liquefaction port
 if production exceeds contractual obligations for LNG
delivery spot cargoes are sold on the market, including
through other shipping company’s ships, considered as a
means of controlling the inventory level without profit
associated

issue extension
 customers specify certain dates or periods - windows of
time - when may or may not accept deliveries
 from the time window for supplying is determined the time
window for loading
 delivery should occur within the time windows:
 internal – periods of customer preference, and
 external – besides internal comprises additional periods
of a few days before and after the internal
 delivery must necessarily take place inside the external
window which comprises also the inner window
 if delivery occurs in periods between internal and external
window, there will be contractual penalties, as exemplified
in the following figure

issue extension
example of penalty cost function proportional to the square of the number of days
before or after the inner window on which delivery is made - inner window is 7 days
and outer window also contains 7 more days before and after the inner window

issue extension results
 considering the same data and the solution given for the 1st
part of the problem, in the extension through application of
MILP method, came out the solution for minimization of
penalty cost, including not only the docks restrictions but
also the LNG stock in liquefaction port
 in next figure are presented the optimal time partitions
defined for boarding each LNG cargo within a time window
with the total of 21 continuous partitions: 07 partitions of
the inner window with more 07 before and 07 after
completing the external window

issue extension results
optimum solution: cargoes and their time partitions for loading start
time partitions:
from 01 to 21 inside
external time
window

Brief presentation fleet of ships optimized programming

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