Allocating crude oil supply to port and refinery tanks: a simulation-based decision support system

Short communication

Allocating crude oil supply to port and refinery tanks:

a simulation-based decision support system

Massimo Paolucci *, Roberto Sacile, Antonio Boccalatte

Department of Computer, Communication and System Sciences (DIST), University of Genova, Via Opera Pia 13, 16145 Genova, Italy

Received 1 February 2001; received in revised form 1 June 2001; accepted 1 July 2001

Abstract

This work focuses on the problem of allocating the crude oil loads of tanker ships to port and refinery tanks (PRT). Two

discrete scheduling aspects mainly influence this process: the tankers’ arrivals and the sequence of crude lots processed in the

refinery. A simulation-based approach that can be applied as a simulator of the physics of the crude oil flow in the refinery

system, as a learning support for personnel training, and as a decision support system (DSS) is proposed. The results of the

application of the implemented system on a real small–medium-sized refinery system are presented. D 2002 Elsevier Science

B.V. All rights reserved.

Keywords: Crude oil supply; Simulation model; Crude oil allocation; Decision support system

1. Introduction

Refinery management is subject to many global

economic and local practical factors that can alter

previously planned workloads. For example, it is well

known that the crude oil market often shows critical

and quite unpredictable price variations. However,

many critical local and last minute situations—such

as the lack of available jetties or tanks, the delay of the

tanker ship arrival, changes in the production plan that

are again unpredictable—can occur and even play an

important role. In this scenario, for example, after a

previously planned decision ‘‘to buy’’ a tanker load

for distillation just prior to or at the tanker’s arrival, a

decision maker may sell the load to another company

because the delay in the tanker discharge is deemed

too high (e.g., since another tanker is using the only

available jetty) or because demurrage costs may be

considered unacceptable.

Fig. 1 outlines the salient production/distribution

elements of an oil company, with arrows depicting the

material flow associated with the refinery process.

The refinery receives crude oil from tankers at the

port, where usually more than one jetties are available.

A pipeline connects the jetties to a number of crude

storage tanks that are present both at the port and at

the refinery. Depending on the storage and production

policies, the crude oil is pumped to the refinery crude

distillation units to be distilled. At the refinery, other

tanks store distilled oil and a subsequent distribution

network provides the refined products to other re-

tailers.

On a monthly basis (usually over a time span of 3

or 6 months), the refinery management plans the

0167-9236/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

PII: S0167-9236 (01 )00133 -6

* Corresponding author. Tel.: +39-010-353-2996; fax: +39-010-

353-2948.

E-mail address: [email protected] (M. Paolucci).

www.elsevier.com/locate/dsw

Decision Support Systems 33 (2002) 39–54

crude oil supply according to market forecasts, sea-

sonal uses and market trends. This plan determines the

tanker arrival sequence at the ports. Two distinct

scheduling aspects mainly influence the allocation of

crude oil to port and refinery tanks (CO-2-PRT), the

tankers’ arrivals and the sequence of crude lots to be

processed in the refinery. A correct allocation of CO-

2-PRT plays an important role in absorbing the differ-

ent dynamics of these two aspects.

This work focuses on the two grey boxes of Fig. 1,

that is, on the decisional problems relevant to the CO-

2-PRT allocation.

Several constraints hinder the CO-2-PRT allocation

problem. Crude oil loads of different qualities should

generally be segregated. This means that in order to

avoid contamination, different crude oil qualities are

assigned to different tanks. Hence, the PRT are often

partitioned into subsets devoted to the various types of

oil. Changing the assignment of a tank to a different

crude oil type is a long and arduous task for many

reasons: for example, an empty tank always has a

residual that may be left to avoid the use of the oil

residual deposited in the bottom of the tank or imposed

by the floating tank roof system used to prevent the

dangerous development of gas. Thus, a set-up proce-

dure must be performed before storing a different kind

of crude oil into a tank.

In addition, it is not possible to pump oil in and out

of the tank at the same time, nor is it possible to pump

crude oil from a tanker into more than one tank at a

time.

These constraints obviously influence the process-

ing times of oil discharging and transferring opera-

tions with consequent economic effects.

The PRT input and output are respectively repre-

sented by the sequence of tanker arrivals at the port

site and by the transfers from the refinery tanks to the

distilling plant. Both the input and output processes

are defined a priori: the ships arrive following a pre-

set calendar and the distilling plant is fed as dictated

by the production scheduling plans. However, these

processes are generally affected by stochastic distur-

bances that can influence the system behaviour. For

example, ship arrivals are only confirmed about 2

days ahead of time, and the exact arrival time is not

known with certainty; in addition, refinery production

plans may be adapted according to market variations.

For these reasons, the definition of a detailed plan for

oil allocation over a long time span is rather difficult.

By contrast, refinery processes and operations are

generally slow: for example, either a transfer process or

the production of a specific oil product generally lasts

for hours if not for days. This means that a decision

about a certain kind of production can affect system

processes for a long time. In addition, the distillation

process can follow any one of the several alternatives

during the mixing of crude oil components. This allows

for a certain level of flexibility in plant feeding, even if

the most efficient mix alternative is generally the one

already specified in the production plan.

In the CO-2-PRT allocation problem, there are two

primary objectives:

� to minimise the tanker download time, avoiding

idle time waiting for tank availability; and� to allocate the crude oil supplies to appropriate

tanks in order to minimise the amount of un-

accepted (and hence sold) crude oil.

Fig. 1. The structure of a generic refining oil company.

M. Paolucci et al. / Decision Support Systems 33 (2002) 39–5440

Deciding and unloading without interruption the

quantity of a tanker load that can be accepted and the

selling of what remains usually achieve the first objec-

tive. To reach the second objective, company managers

must take decisions on the basis of their experience,

ultimately exploring alternatives by relaxing certain

constraints. For example, company managers usually

exclude the possibility of changing the destination

(type of stored crude oil) for a tank, but if needed, they

do resort to this approach.

In our opinion, simulation models can be effec-

tively applied to the CO-2-PRT allocation problem

since they allow the comparison of several runs under

different conditions and can be easily understood by

human decision makers [1]. Simulation models also

respond to personnel training needs. On the other

hand, defining an optimization model based on a

mathematical formulation of the problem to support

a decision-making process is neither simple nor suit-

able. The use of the simulation model to provide

decision support is a quite common approach in the

decision support system (DSS) literature (e.g., in water

management [7] and traffic control [8] contexts).

However, using simulation models for optimization

can be inefficient in many cases [2]. The most efficient

way to face a decisional problem is by using a poly-

nomial time optimization algorithm or an approxi-

mated heuristic one. As often happens in this con-

text, it is not always simple to formulate a model that

provides solutions which are considered reliable and

acceptable. Decision makers often like to be directly

involved in the ‘‘physics’’ of the problem: they may be

sceptical of a response provided by a ‘‘black box,’’ as

an optimization algorithm might seem, and they want

to understand the rationale underlying the choices

made while interacting with the support tool during

the decisional processes.

While mathematical programming is suited and

currently used for the long-term production planning

in a refinery in the coming years, rapid decision

processes, especially with quite heavy economic

impact like CO-2-PRT allocation problems, are likely

to be solved by skilled human planners rather than by

computer software. In this respect, a simulation model

is suited for the collection of the knowledge of human

refinery planners, involving them in model validation

and refinement phases, ultimately becoming a tool for

both the training of new staff, as well as for the

analysis of the effects of structural changes on the

system’s performance (e.g., the availability of a new

tank).

Simulation software that can provide trustworthy

decisions and at the same time train new planners is a

real need in a refinery. These are the reasons that led

us to develop a DSS based on a simulation model.

During the simulation, the decision makers can follow

the evolution of the system model, stop it to change

working conditions and directly verify the ‘‘real’’

feasibility of their actions. In this respect, our system

is compliant with the major goal of any DSS as stated

in Ref. [4], that is, to improve the effectiveness of the

decisions.

In Section 2, the CO-2-PRT allocation problem and

the DSS architecture are described. Two allocation

policies are compared and discussed, and the results

yielded by the analysis of some case studies are re-

ported.

2. The definition of the CO-2-PRT allocation

problem

The CO-2-PRT allocation problem can be des-

cribed as a network model (Fig. 2). Each tanker arrival

is described by the quantity, qi, and by the type oi of

the crude oil. The pumping system at the port allows

the transfer of the shipload to the port storage facili-

ties. These have a limited availability and a behaviour

that can change over time: usually, one ship at a time

can be served and the pumping rate depends on the

tanker-pumping device.

The port storage subsystem includes several tanks

that at a fixed time can be partitioned into different

sets depending on the type/quality of the crude oil

currently stored. However, during the whole CO-2-

PRT allocation process, company decision makers

could find it convenient to change the crude oil type

stored in a tank and to perform the needed tank set-up

operations. In order to empty a tank (but even for

different reasons), some transfers between tanks of the

same set may be ordered. Usually, these kinds of

operations precede the arrival of a tanker and are

carried out in order to set up the storage tank system

to more efficiently receive the shipload.

The port storage subsystem is connected to the

refinery subsystem by a pipeline that can transfer only

M. Paolucci et al. / Decision Support Systems 33 (2002) 39–54 41

one type of crude oil at a time. The pipeline usually

allows the transfer of crude oil at a high rate compared

to the other pumping systems, so it should not be

considered a bottleneck of the whole system. An addi-

tional possibility (Fig. 2) is to unload the tanker through

direct pumping into the refinery storage subsystem.

This direct transfer is usually rejected since the possible

pumping rate in this case is reduced compared to the

nominal pumping capacity of the pipeline.

With respect to the port subsystem, the refinery

storage subsystem has a symmetrical behaviour. The

outputs here are represented by the feeding system of

the distilling plant, that is, by a sequence of transfers

of quantities, fs, of crude oil of type as, from the

storage tanks of the refinery subsystem to the distill-

ing plant and by the quantities ri of crude oil that must

be sold since they cannot be feasibly stored.

The crude oil scheduling problems described in

previous works [6] and in some aspects also in Refs.

[3,5] are quite similar to the CO-2-PRT allocation

problem, but the approach adopted to solve it is differ-

ent. Among the possible differences from the CO-2-

PRT allocation problem dealt with in this work, in Ref.

[6] no stochastic variations are taken into account, it is

not possible to set-up a tank in order to change the type

of crude oil that it can store, and possible transfers

among tanks aiming at preparing the reservoir system

to better stock new arrivals are not considered. The

approach followed in Ref. [6] for the crude oil sched-

uling problem calls for the formulation and resolution

of a mixed integer programming (MIP) problem. Time

obviously plays a relevant role in the refinery system,

as its state changes continuously with the processing of

the storage operations. Moreover, the ‘‘crude oil seg-

regation’’ constraint and the fact that the pumping

operations must be performed one at a time (generally

separated by set-ups) highlight the discrete (combina-

torial) nature of the decisions. As a result, as also noted

in Refs. [5,6], time discretization and integer (binary)

variables rapidly increase the dimensions of the prob-

lem. To take the formulation to a reasonable size,

simplifying hypotheses should be introduced (e.g.,

rather ample discretization of the time) and the whole

problem should be split into two separate allocation

problems at the refinery and the port sites that are

solved sequentially using the solution of the first

problem as input for the second. Finally, an optimiza-

tion model could very hardly take into account the

changing of the type of oil for a tank, that is, the

modification of a storage tank into another type. The

reader can see how similar difficulties can arise, even

considering a dynamic programming model that could

be adopted to face the dynamic behaviour of the

system.

For all the above reasons and also bearing in mind

that ship arrivals may be subject to stochastic varia-

tions and that, generally speaking, the actual system

behaviour may be disturbed, the analysis of storage

policies by means of a simulation model seems more

appropriate.

Fig. 2. The CO-2-PRT allocation problem network representation.


Although many of the physical processes involved

in the CO-2-PRT problem are continuous in nature,

the decisions are taken according to specific events

(e.g., the tanker’s arrivals, the start or the end of oil

transfers, the tank set-ups, etc.). Let us indicate

t=1,. . .,T as the whole sequence of events, assuming

the events known a priori, and let T be fixed in order

to represent the system behaviour for a given period

(say, 6 months or 1 year). Two sets of information

represent the input and the output of the system.. Information on tanker arrivals. These are the

quantities qt(i) of crude oil of type ot(i), where t(i)

represents the event corresponding to the ith arrival; in

addition, since the pumping speed can depend on the

tanker, a pumping rate, prt(i), must be considered.. Information on the transfers of crude oil to the

distilling units of the refinery. These are the quantities

ft(s) of crude oil of type at(s), where t(s) is the event

corresponding to the beginning of the sth transfer.

The system state is characterised by a number of

variables defining:

� the type of crude oil stocked in a tank; several

tank subsets TSto are defined which include the

indexes of tanks that at the event t are

compatible with the oth crude oil type; and� the quantity vjt of crude oil stocked in the jth

tank at the time of event t;

and by several constant quantities:

� the tank capacity, capj, expressed in m3

(although this quantity may vary slightly as a

function of the temperature, it has been

assumed as a constant);� the pipeline pumping rate, ppr, and the one

among the tanks at the same site, tpr; and� the set-up time, stj, to change the destination of

a tank j (in general, this time may also depend

on the initial and final type of crude oil since in

certain cases, a limited level of blending

between two different oils can be accepted).

The decision variables can be identified as the

following.. The crude oil transfers from the current tanker

to a tank j, ytj, or between a pair of tanks j and k, ytjk,

that begin at the occurrence of event t, that is, if t(i)

corresponds to the arrival of the ith ship, any oil

transfers involving such a ship should have t�t(i).. The quantity, rt, of crude oil that (not being

storable) is sold.. The changes of the type of oil that can be stored

in a tank j at time instant t, xtjo. These are binary

variables that indicate if tank j should perform a set-up

to be compatible with the oth type of oil. Note that the

time instant at which such kind of event should occur

is itself a decision variable.

The system behaviour is ruled by several con-

straints that are relevant to:

� the oil compatibility for the tanks (which

conditions the composition of sets TSto );

� the dynamics of the transfer processes, i.e.,

allowing the computation of the relevant

processing times;� the material balance; and� the feasible use of the resources (i.e., involving

the tank capacity, the minimum duration of the

feeding operation, the impossibility of more

than one loading or unloading operation at a

time).

The objectives and the criteria of the CO-2-PRT

allocation problem are many, so it becomes a multi-

criteria optimization problem. However, the objective

of minimising the tanker service time is certainly the

most critical one. Decisions that distribute the crude oil

among the tanks even optimally, which avoid selling

any excess of an arrived quantity but require to stop a

ship longer than strictly necessary for discharging, are

always rejected. Similarly, stopping the crude oil flow

to the refinery distillation units should definitely be

avoided. Other decision objectives that must be con-

sidered, in qualitative order of importance assigned by

the company managers, are:

1. minimising the whole quantity of sold oil

during the observed period (O1);

2. minimising the number of set-ups due to

changes of the type of oil allowed for a tank

(O2);

3. minimising the number of simultaneous trans-

fers among the tanks (O3);

4. maximising the quantity of crude oil stored in

each tank that is not completely filled (O4);


5. minimising the number of tanks used to

discharge a ship (O5); and

6. minimising the number of tanks used to feed

the refinery (O6).

Objective 4 aims at reducing the number of tanks

used for discharging a ship and at filling as much as

possible the tanks currently used. In this case, two

different indexes, O4a and O4b, can be defined, namely

O4a ¼

XNS

i¼1

wiQi

N

where N represents the total number of tanks, Qi is the

quantity of crude oil allocated into the ith tank, and wi

is the weight computed as

wi ¼capi

maxj

capj

and

O4b ¼

XNS

i¼1

Qi

NS

where NS is the number of the tanks used only to

discharge a tanker and, in this case, Qi represents the

quantity of crude oil allocated into each of them.

Index O4a favours the fact that larger tanks are filled

before smaller ones. Greater values of index O4b are

associated with situations for which a smaller number

of tanks have been used to store the same quantity of

crude oil.

3. The decision support system

We have implemented a DSS allowing the decision

makers to determine a feasible allocation policy by

simulating different scenarios such as different

sequences of tanker arrivals, different tank planning,

strategic allocations and allowing system reconfigur-

ability and flexibility.

Typical applications of the system are:

� to test the monthly plan with special reference

to critical physical aspects, such as tanker

arrivals and tank allocations;� to test the marketing strategies, such as ‘‘sell-

ing’’ a tanker or buying a ‘‘new’’ unplanned

one; and� to train personnel behaviour to face simulated

unpredictable events.

In order to satisfy the previous requirements, we

have implemented the system on a PC platform using

a tool with a friendly graphical user interface (GUI),

allowing users to understand immediately the evolu-

tion of the system. In addition, we needed to develop a

set of modules defining parts of the refinery, which

can be repeated in the same plant or in other applica-

tions in a sort of an object-oriented approach.

In this section, we show the detailed problem

definition, the DSS architecture and its modules.

3.1. The DSS architecture

As with any DSS tool [4], three main components

characterise our system architecture: the user inter-

face, the simulation model and the data repository.

The first two components have been developed with

Extend V4 (ImagineThat, http://www.imaginethat.-

com), a simulator development tool, while the third

one has been implemented using a relational database

management system (RDBMS) (Ms SQL Server ver-

sion 7.0).

The user interface is a graphic interface that allows

the definition of the layout of the refinery plants

according to predefined (both system and custom)

modules and the definition of the system state. After

its start, the DSS can evolve to new states that are

graphically displayed. For example, the crude oil of a

tanker has been divided into lots (represented by

barrel-shaped icons), which correspond to the crude

oil quantity that is transferred during an animation

time step, previously defined by the user.

Decision makers can configure the evolution of the

DSS into the two following modalities.. Manual: the user can decide new strategies at

each single step of the system evolution. This config-

uration is very useful for training purposes.


. Automatic: the user can choose a strategy for

the allocation of the tanks among the ones defined a

priori. If vjt is the level of tanker j at the occurrence of

event t and TSto is the set of available tanks which can

be used for the oth type of crude oil, two simple

heuristics have been implemented in the current ver-

sion of the system:

� Maximum Available Capacity First (Max ACF),

which aims at favouring the use of the minimum

number of tanks to stock a shipload. Such a

policy selects the next tank that should be used

as:

j� ¼ arg maxjaTSot

½capj � vjt�

� Minimum Available Capacity First (Min ACF),

which gives priority to completely fill the

maximum number of tanks, i.e., it aims at

reducing the ‘‘fragmentation’’ of the free

capacity of the storage system:

j� ¼ arg minjaTSot

½capj � vjt�:

In both modalities, each event occurring while the

DSS is running is stored in a series of tables defined in

the RDBMS. This is useful for many applications

such as, for example, further statistical analysis, train-

ing of personnel by cases and the definition of specific

system behaviours.

3.2. The DSS subsystems

The DSS user interface and simulation model have

been developed using Extend V4, which allows the

development of simulators through visual composing

the relevant system structure by connecting the blocks

associated with the various system components. A

block can correspond to an elementary system com-

ponent or to a ‘‘hierarchical block,’’ that is, a sub-

system that is, in turn, defined by other blocks (both

elementary and hierarchical). Each block is made up

of four main elements: code, graphic aspect, dialogue

window and input/output connectors. The block code

is a C-language-like function defining the block

behaviour that is invoked on the occurrence of events

relevant for the block (i.e., the arrival of an item that

must be processed). The connectors are the links that

make the block communicate with other blocks. Two

classes of connectors are defined in Extend: the value

connectors devoted to the information flow in the

system and the item connectors used to represent the

flow of physical items.

Several hierarchical blocks associated with the

higher level refinery system components, which have

been designed by dividing the model complexity on

other hierarchical sub-levels, make up the DSS sim-

ulation model.

In the following paragraphs, we introduce the main

subsystems of the CO-2-PRT allocation problem des-

cribed as Extend blocks.

3.2.1. The harbour subsystem

Four modules constitute a harbour subsystem

(Fig. 3).

3.2.1.1. Ship Generator block. It generates the entity

associated to a tanker arrival and it is made up of four

main sub-blocks.

Fig. 3. The harbour subsystem.


. A standard Item Generator Extend block allow-

ing the definition of an a priori schedule for the item

arrivals. This solution has been adopted since we

assume to know, for the period of analysis, the planned

arrivals, which could, however, be stochastically mo-

dified. The a priori arrival schedule is an item table

reporting the expected arrival time and other relevant

tanker attributes such as the crude oil type, the quan-

tity of crude oil loaded and the maximum pumping

capacity.. A standard Random Generator Extend block

which is devoted to the generation of a random

disturbance of the tanker arrivals. This disturbance

corresponds to a delay that is represented by a positive

truncated normal distribution.. A standard processing/delay block which

delays the tanker arrival by the time provided by the

random generator block.

3.2.1.2. Port block. It represents the physical area

where the download of the crude oil from the tanker is

performed. Made of standard Extend blocks, it is a

block that performs various simple operations, namely

keeping track of tanker arrivals as a First In First Out

(FIFO) queue, generating the flow of crude oil lots to

make the system animation evolve smoothly accord-

ing to the tanker load, the pump flow capacity and the

user-defined animation step. In other words, the port

block receives an item corresponding to a tanker in the

input and produces a sequence of items corresponding

to crude oil lots in the output in order to allow to the

user the desired visual control over the discrete event

system evolution.

3.2.1.3. End block. This block is responsible for the

release of the item associated with the tanker when the

downloading operations are completed.

3.2.1.4. Router block. This is one of the most im-

portant blocks of the system. According to the crude

oil type, this block was able to route it to the assigned

tank. The automatic/manual configuration is set up in

the dialog of this block, and whenever the automatic

mode is selected, the allocation strategy that must be

used should be specified.

3.2.2. The tank storage subsystems

The system includes two tank storage subsystems,

one at the port and the other at the refinery site, each

of which is made up of a number of tank blocks that

have been implemented as new elementary blocks.

The main parameters that characterise a tank block

(Fig. 4) are:

� the crude oil type assigned to the tank,� the crude oil level and

Fig. 4. The tank block.


� the maximum and minimum capacities (a tank

cannot be safely emptied without an appropri-

ate set-up procedure).

Different colours are associated with the tanks to

highlight the tank state: green if crude oil is being

pumped from the tank, red if crude oil is being pumped

to the tank and grey for temporarily inactive tanks.

3.2.3. The refinery plant subsystem

Taking crude oil from tanks as input, the refinery

subsystem generates distilled oil that is the output of

the system. A maximum of three tanks can be assigned

as input, and for each of them, the definition of the

crude oil flow rate has to be specified. The mix of

different crude oil types can be automatically assigned

by choosing the production of a specific distilled pro-

duct (Fig. 5).

3.2.4. Virtual repository of unstored crude oil block

The unstored crude oil—that represents the amount

of crude oil that could not be stored and, for example,

must be sold to another company—plays an important

role in the evaluation of the system’s performance and

is thus virtually stored in a repository. This virtual

repository ismodelled as a virtual tank coloured in blue.

3.2.5. Control panel block

Although the model is hierarchically structured and

all blocks can be browsed to inspect their sub-blocks,

reaching the structure of a specific block can be quite

complex. The control panel provides a dialogue box,

which is divided into zones with a direct access to the

blocks, which are important for the evolution and the

customization of the DSS. This block is the core of the

DSS user interface.

4. Results

The DSS has been tested on an actual small–

medium crude oil refinery system (about 1000000

m3 of crude oil refined per year) with rather critical

logistic constraints due to the environmental and geo-

graphic characteristics of the area. The refinery is

located inland, about 50 km from the harbour area.

A first set of five tanks is located in the refinery area,

playing the role of direct input to distillation. Another

set of four tanks is located in the harbour area and is

used as the main repository of the crude oil discharged

from the tankers. The second tank set is generally fed

by the first one via an oil pipeline, which allows the

transfer of one kind of crude oil at a time, thus

requiring the identification of a suitable sequence of

transfers that should be physically separated. How-

ever, a direct connection between the port jetty and the

remote set of tanks is also possible and can be used

only if strictly necessary as in this case where the

pumping rate is reduced. The refinery production plant

is always fed by the local set of tanks. Fig. 6 shows the

Fig. 5. The refinery plant subsystem.


Extend system layout on which we have tested our

DSS. In this figure, the six main system components

can be recognized. These are (in left to right and top–

down order) the harbour, the tank storage, the refinery

plant, the control panel, the virtual repository and the

two tank storage subsystems.

Three different crude oil types, low pour point,

sweet and sour, can arrive to the refinery plant exclu-

sively by ships (with a load capacity between 15000

and 80000 tons). These types should be kept segre-

gated, but they can be transferred at the same rate.

In this context, we have tested the system under

several different conditions. Here, we report the

most significant results obtained, validating the sys-

tem as:

� a simulator of the physical system, where we

have calibrated and verified the accuracy of the

model, comparing it with real historical data;

� a learning support, where experts have tested

the possibility to follow different strategies and

to compare them with previously made real

decisions; and� a DSS, where we have tested the possibility to

implement automatic decision policies in the

system.

4.1. Model validation as a simulator of the physical

system

We first validated the accuracy of the model on the

basis of a set of historical data collected by the com-

pany. The purpose of the validation tests was to verify

whether the model was able to follow the evolution of

the real system.

The CO-2-PRT allocation model was tested upon 1

year of historical data, which included the arrival of

25 tankers. These data include the level of the tanks

Fig. 6. The small–medium-sized refinery layout on which we have tested the system.


and the crude oil types at the beginning of each

working day and the transfer operations, which cor-

respond to about 500 crude oil main transfers among

the tanks.

In correspondence to each tanker arrival, we repro-

duced the same real sequence of operations, both

transfers and refinery feedings, in order to force the

model to follow the evolution of the real system. The

results obtained at the end of the unloading of each

tanker were quite accurate since a mean difference in

the tank levels of about 1% was measured between the

simulated and the real systems. This difference was

considered as acceptable and it could be yielded by

several factors: in the real system, there are some

temporal latencies between consecutive operations

which were not taken into account in the model, such

Table 1

Initial state of the tanks

Tank ID Site Crude oil type Available oil [m3] Available capacity [m3]

S1 Refinery Sour 12764 16111

S2 Refinery Sweet 8386 16214




S102 Port Sweet 1132 14595

S105 Port Sweet 9068 14605

S109 Port Low pour point 56812 60979

S112 Port Sour 60500 61871

Table 2

State of the tanks at the end of the discharge of tanker 1



S2 Refinery Low pour point 16124 16214







S112 Port Sour 28259 61871

Sold low pour point 21703

Table 3

State of the tanks at the arrival of tanker 2








S105 Port Sour 317 14605


S112 Port Sour 11448 61871



as the difference in the starting time of the pumping

operations due to human factors.

4.2. Model validation as a learning support

We validated our system as a learning tool. Ac-

tually, in the considered context, an exhaustive set of

predefined rules that enables decision makers to take

the most suitable decisions in different situations

cannot be identified. The skill acquired by decision

makers during a long daily experience cannot always

be easily transferred to inexperienced personnel as a

set of formal rules, and a long time on field training is

usually needed. Furthermore, a direct learning from

possible mistakes is obviously undesirable. For these

reasons, the possibility of generating different scenar-

ios corresponding to different sequences of crude oil

arrivals and allowing personnel training are greatly

appreciated. Specifically, the characteristics allowing

personnel training are the following.

. Learning by previous errors: to analyse the

decisions really taken in the past in order to test

possible alternatives with better performances.. Learning by hypothesis: to test different what-if

scenarios both in the current structure of the industrial

plant (for example, the effects of the introduction of a

new tank) and in the current planning of tanker arrivals

in the short to mid-term (for example, the purchase of a

new tanker).

We tested several possible configurations and the

users particularly appreciated the training features. A

meaningful example is presented here, namely the

simulation of a real situation where a tanker load was

completely sold. This could have been avoided with a

different solution to the CO-2-PRT allocation problem.

The situation considered includes three tanker

arrivals within 1 month. Tanker 1 is loaded with

53938 m3 of low pour point crude oil. Tanker 2 is

loaded with 91639 m3 of sour crude oil. Tanker 3 is

loaded with 11835 m3 of sweet crude oil. Table 1

Table 4









S105 Port Sour 14605 14605


S112 Port Sour 61871 61871


Table 5

State of the tanks at the end of the discharge of tanker 3 (the final state of the simulation)








S105 Port Sour 14605 14605


S112 Port Sour 61871 61871


Sold sour 0

Sold sweet 11835


reports the state of the reservoirs at the beginning of

the period of observation, whereas Table 2 reports the

state of the reservoirs at the end of the discharge of

tanker 1 in the real system. Table 3 reports the state at

the instant of tanker 2 arrival: this table has been

included to show that the state may be different from

the one in Table 2 due to several reasons (for example,

the plant has refined some of the oil stored in the

tanks, some of the oil has been transferred between

the tanks, etc.). Tables 4 and 5, respectively, report the

state at the end of discharging tanker 2 and tanker 3

(corresponding to the final system state). This simu-

lation effectively reproduced the real system where

the oil load of tanker 1 was partially sold (40%),

tanker 2 was completely discharged and tanker 3 was

completely sold.

Considering the same initial system state, sequence

of arrivals and requirements of crude oil feeding in

the same time interval, we tested different sequences

of operations in order to enhance the objective of

reducing the amount of sold crude oil (that is, the

most relevant criteria considered in this context).

Here, we report the alternative operation sequence

that was judged to be the most effective by domain

experts. In particular, Tables 6–8 describe the state of

the tanks after the discharge of tankers 1–3, respec-

tively.

As can be observed, the improvement achieved

with the alternative sequence can be quantified in the

following way.

� With the real sequence, 21% of the total crude

oil supplied was sold, whereas only 11% of the

total was sold with the simulated sequence.� The amount of the sold crude oil of tanker 1

was reduced from 40% to 9%; for tanker 2, it

was raised from 0% to 14%; and for tanker 3, it

was discharged in full instead of being sold.

Table 6











S112 Port Sour 28259 61871


Table 7











S112 Port Sour 61871 61871


Sold sour 13070

Sold sweet 0


4.3. Model validation as a DSS

One of the main advantages of the support system

is that it can provide guidance to the decision makers

by proposing the CO-2-PRT allocation plans gener-

ated by heuristic decision policies. Since end-users

always like to have full control of the system, the use

of fully automatic policies seems more appealing for

applications of personnel training rather than as an

operative tool. In our opinion, using a ‘‘black box’’

approach for short/mid-term decisions is useless for

both an expert and a young planner. On the other

hand, if the DSS policies can be easily described, they

can be useful to analyse their effects.

This is the case of the two policies that were

introduced in Section 3.1. We verified them using the

same testing conditions as for the validation described

in Section 4.2 and by comparing the results to those

previously reported. The quantity of sold oil at the end

of both policy simulations (Max and Min ACF) was

49771 m3 (92% of Low Poor Point of tanker 1) and

was greater than the two previous simulations even

though only one type of oil has been sold.

Table 9 compares the different strategies with the

results obtained for the real operation sequence and

the alternative one whose effects were shown in

Section 4.2, reporting the values obtained in the real

case for the six different objectives indicated in

Section 2, as well as the values of the simulated cases.

More specifically, the results obtained in correspond-

ence of the discharge of the three ships are shown for

objectives 4 and 5 and are indicated by the ship

number in brackets. In Table 9, the values of O4b

and O5 for ship 3 are not computed since the ship’s

load was completely sold. Finally, the average values

for objectives O4a and O4b are reported.

As can be observed from Table 9, the alternative

strategy generally shows better performances than the

one really used, whereas the Max and Min ACF

strategies both had poorer performances. These two

latter automatic strategies performed quite similarly

for the considered case and, in particular, they pro-

vided the same value for the most important objective

O1. However, the performance of the Max and Min

ACF strategies may ultimately depend on the se-

Table 8

State of the tanks at the end of the discharge of tanker 3 (the final state of the simulation)










S112 Port Sour 61871 61871


Sold sour 13070

Sold sweet 0

Table 9

The evaluation of the different strategies according to the objective

defined in Section 2

Real Alternative Max ACF Min ACF

Sold oil O1 33538 17664 49771 49771

Setups O2 7 6 0 0

Transfers O3 8 8 3 3

O4a (1) 11848 12559 13397 13482

O4a (2) 15687 15769 14293 14058

O4a (3) 16415 16111 14638 14368

O4b (1) 57616 47552 140713 143927

O4b (2) 40332 51424 34718 34718

O4b (3) / 217534 185427 185427

Tanks�ship (i) O5 (1) 3 4 1 1

O5 (2) 5 4 5 5

O5 (3) / 1 1 1

Feeding tanks O6 12 11 9 10

Average O4a 14650 14813 14109 13969

Average O4b 48974 105503 120286 121357


quence of load arrivals: for example, we verified that

by changing the sequence used for all the previous

validation tests so that tanker 2 with a larger load

arrives first, the Max ACF strategy yields a better O1

performance.

5. Conclusions

In this paper, the main characteristics of a system

for modelling, testing and supporting the decisions for

the allocation of the crude oil to the port and refinery

tanks of an oil company have been presented. As

discussed, several reasons motivate the use of a sim-

ulation model for the proposed system, one of which is

the possibility to use it for different goals. For exam-

ple, the off-line use of the DSS as a learning/training

support tool (e.g., to test different what-if scenarios

and decision rules)—an aspect often neglected in the

DSS literature—has been particularly appreciated. In

addition, the opportunity to model the CO-2-PRT

allocation problem as an MIP problem has been

considered and discussed. However, the simplifica-

tions that must be introduced to yield a workable

model and the difficulties that decision makers must

interact with, prompted us to consider a simulation-

based DSS that is more likely to be accepted by the

managers of oil companies.

The proposed DSS allows the verification of the

consequences of decisions about the CO-2-PRT allo-

cation problem before the actual arrival of tanker

ships in order to optimise several performance factors

that have a direct influence on the production and

management of the oil company. Specifically, the

system aims at reducing the quantity of crude oil that

the company must sell because it is unable to store it.

Taking into account the sequence of tanker arrivals,

the DSS provides company managers with a tool for

defining an allocation policy for time intervals that

exceed a day-by-day policy.

In its current version, the DSS includes two simple

heuristic policies to select the tanks to be used to

discharge a tanker, and it is possible to improve it by

introducing new, more complex, discharging and

allocation rules.

In the paper, some results have been provided from

the test of the DSS on a small–medium refinery

company with limited storage facilities. As the results

highlight, the DSS actually allows the determination

of different solutions for the CO-2-PRT allocation

problem that outperform the solution identified by

the company decision makers. This fact is particularly

true whenever the operational conditions become

critical as for the cases in which the company man-

agers decide to sell a tanker load in order to avoid a

drastic reduction of the storage capability of the

system: it is not rare that in such critical situations,

a load is entirely sold to avoid decisions that could

become too constraining for the management of future

arrivals. The possibility to experiment more strategies

in advance, extending the effect of decisions to the

future, is a utility that was greatly appreciated by

company managers.

As already noted, the two simple tank selection

policies used to automatically generate the solutions

are quite far from actually assisting decision makers,

and they can currently be considered as a support tool

for the management of ordinary situations instead of

critical ones. However, these policies represent a

starting point for the study of more complex strategies

that can provide decision makers with solutions that

are both feasible and acceptable.

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Massimo Paolucci received his PhD in

Electronic and Computer Science at the

Department of Communications, Computer

and Systems Sciences (DIST) of the Uni-

versity of Genoa in 1990. Currently, he is

working as an assistant professor at DIST.

He worked in the fields of knowledge

representation, in particular dealing with

the problem of the treatment uncertainty

in expert systems, decision support systems

and multi-attribute decision making. His

main research interests are focused on operational research problems

relevant to the logistics and industrial automation as well as the

environment. In addition, he is also involved in research activities on

information systems and database. His professional experiences have

been mainly carried out in the field of database management and

design.

Roberto Sacile received a Laurea degree in

Electronics Engineering in 1990 at the Uni-

versity of Genova and a PhD in 1994 at

Politecnico of Milan. He is currently an

assistant professor at the Faculty of Engi-

neering of the University of Genova, han-

dling the courses of ‘‘Fundamentals of

Computer Science’’ and ‘‘Geographic Infor-

mation Systems.’’ His research interests

focus on decision support systems and agent

technology with special reference to their

integration and application to territorial and geographic information

systems, environmental monitoring and protection, manufacturing

information systems and health care systems. He is the author of more

than 100 technical papers in refereed journals and conferences.

Antonio Boccalatte is a professor of

«Database Management Systems» at the

Faculty of Engineering. He is the author

of more than 70 scientific papers presented

at international congress or published on

international revues. His research interests

include the following.. System architecture and artificial

intelligence: Particularly related to the

study of data flow and multiprocessor

architectures. He had also published international papers on sched-

uling algorithms, fault-tolerant architectures and fault-tolerant oper-

ating systems. He also worked on natural languages and scene

recognition.. Decision support systems and database: Design and devel-

opment of graphical user interface for decision support systems and

on the integration of relational database with DSS. Scheduling

algorithms for medium and short-term production in a production

environment based on orders. Use of object-oriented programming

and Petri Nets for information system modelling and development.


Documents

Allocating crude oil supply to port and refinery tanks: a simulation-based decision support system