APPLICATION OF RULES ENGINES IN TECHNOLOGY

MANAGEMENT

Barbara Baster, Andrzej Macioł and Bogdan Rębiasz

Faculty of Management, AGH University of Science and Technology, ul. Gramatyka 10, 30-067 Kraków, Poland

Keywords: Rules Engines, Technology Management, SQL.

Abstract: In the paper we present the initial results of our research aimed at development of the tool which will benefit

from virtues of BRMS and will enable support of technological decisions. Our task was focused on

preparation of use cases set along with precise description of rules used for solving specific decision

problems. For this purpose two decision problems were analysed which covered such issues as selection of

feedstock or executive production planning. These problems were analyzed in view of a company producing

cold-rolled strips in a wide dimension range and diversified grades of steel. The general conclusion which is

the answer to the question of the possibility to create a tool similar to BRE but capable of technological

decision supporting is a statement that it is necessary to combine two forms of knowledge presentation:

declarative and procedural. It is also necessary to ensure the possibility of communication between this type

of instrument and external data sources as well as various types of IT tools supporting specific technological

decisions.

1 INTRODUCTION

Nowadays an integrated information system

supports almost every corporation in all fields of

management. Nonetheless, such support is limited to

realization of administrative tasks and does not

influence significantly the decision making

processes. On the other hand, engineering solutions

are applied in the specific fields of management

(such as logistics, technology management,

transportation etc.), however they are not well

integrated with corporate IT systems. In the recent

years there were many attempts aimed at broadening

the functionalities of IT systems as well as their

integration with specialized tools. These work were

conducted in parallel in two fields. On one hand,

sophisticated expert systems for support of

technological decisions were developed, and on the

other the results of operational research were used

for the development of systems which support

specific decisions (e.g. advanced planning and

scheduling systems). Regrettably, no widely

accepted results were obtained in either of these

fields. A similar issue arose in case of management

problems that are not characterized by standard,

repetitive decisions. It was grounded on the fact that

development of individual IT solutions required

participation of specialists from a given domain, the

development process of application was excessively

long and ultimate solutions were hardly flexible. In

the first decade of the century in the domain of

business rules a new opportunity came to existence,

i.e. BRM systems. They allow for considerable

simplification of application development process

and enable to formulate characteristic repositories of

organizational knowledge. Nonetheless, up to now

such solutions were not successfully applied for

support of technological decisions or more complex

decisions related to management of manufacturing

processes.

Support of technological decisions may be

realized through expert systems. Presented in

literature automatic support systems of production

process design, despite different scope as well as the

manner of presenting technological knowledge, are

characterized by a stiff, a priori formulated

structure. In consequence, user has possibility

neither to develop knowledge nor introduce results

of his own research without reformulating

knowledge base – doubtlessly with cooperation of

system designer.

The research, co-funded with the European

resources, is aimed at development of the tool which

will benefit from virtues of BRMS and will enable

Baster B., Macioł A. and RÄ

Zbiasz B. (2010).

APPLICATION OF RULES ENGINES IN TECHNOLOGY MANAGEMENT.

In Proceedings of the 5th International Conference on Software and Data Technologies, pages 83-86

DOI: 10.5220/0002931700830086

 SciTePress

support of technological decisions. In this paper we

present results of our research on capabilities of

BRMS and characteristics of technological

decisions.

In particular our task was focused on preparation

of use cases set along with precise description of

rules used for solving specific decision problems.

For this purpose two decision problems were

analysed which covered such issues as selection of

feedstock or executive production planning. These

problems were analyzed in view of a company

producing cold-rolled strips in a wide dimension

range and diversified grades of steel.

2 RULE BASED TOOL FOR

TECHNOLOGY

MANAGEMENT

As part of our project we developed assumptions of

a new tool for decision support, including

technological decisions.

Main stream of research activities in the area of

knowledge modelling for BRE is connected with

application of description logics for ontology

formulation. Our experiences indicate that the most

effective solution, capable of direct cooperation with

majority of industrial information systems which

simultaneously provides decidability, is a

combination of relational model with inference

system that utilizes attributive logics. Our solution,

named Inference with Queries (IwQ) (Maciol,

2008), has been developed as a knowledge model

and an inference engine for formulation of Business

Rules Management Systems. Knowledge storage is

realized in accordance with principles of Variable

Set Attribute Logic (VSAL) (Ligeza, 2006). An

assumption was made, that in rule-based decisive

system, attributes as well as variables will be stored

in form of relations. Owing to the utilization of

extended selection formula, knowledge definition

process becomes simplified. SQL queries realize a

significant part of inference.

The essential functional requirements (solution

features) are as below:

1. User will be able to easily create, delete and

modify knowledge bases.

2. The way of inference (forward and backward)

and process parameters (the start-up fact, the way of

passing messages, the ending conditions) can be set

automatically by the system, the knowledge architect

at the stage of its creation or at the stage of

inference.

3. The system provides a set of tools for acquiring

knowledge of facts from external sources (databases,

procedures, programs, global network).

The inference process in our solution is controled

by variable values of attributes that indicates which

rules will be fired on current step of the reasoning.

3 EXEMPLARY DECISION

PROBLEMS ANALYSIS

In the initial phase of our research we found that the

system must combine the possibilities given by

declarative as well as procedural notation of

knowledge. Our solution gives such possibilities.

Research were carried out in a company

producing cold-rolled strip in a wide dimensional

range and variety of steel grades.

3.1 Feedstock Selection for Actual

Order

Parameters of feedstock selection may be

characterized as follows:

1. Steel grade of feedstock strip and rolled strip

should be identical. There are possible some

departures from this ‘literal’ conformity, especially

in case of low-carbon grades of steel. In many cases

chemical composition of grades of steel resemble

one another. Rules that define changing process of

steel grades may be visualised in the form of

decision grid.

2. The width of the feedstock strip must be greater

than the width of the rolled strip. In order to

minimize ‘unjustified’ loss, different orders are

combined together to prepare a milling from a single

feedstock coil.

3. The width of the feedstock strip should make

allowance for the width of the rolled strip in view of

eventual cutting of the strip into strands.

4. A specific processing rate of feedstock into given

thickness is required.

5. The weight of the feedstock coil should be in a

proper relation to the weight of the order. The

realization of order from the minimum possible

number of different feedstock charges is the most

favourable option in terms of uniformity of produced

strip.

Hereunder we present the exemplary rules stored

in a knowledge base:

ICSOFT 2010 - 5th International Conference on Software and Data Technologies

rule 1

input_material = ‘hot-rolled’

and

rolling_ratio >= 25%

and

reduction_of_thickness >= 0.5 mm

then

treatment_ratio := ‘sufficient’

rule 2

ordered_steel_grade =

current_coil_steel_grade

and

feedstock_coil_width >=

min_adequate_width

and

treatment_ratio := ‘sufficient’

then

feedstock_coil := ‘admissible’

The main task of the rule-based system is to

verify which of the feedstock coils are ‘suitable’, i.e.

whether they can be used as a charge for the

realization of a particular order. Along the inference

process, a given charge is marked as ‘acceptable’ if

it is characterized by the proper grade, width and

‘sufficient’ degree of material reduction. The charge

is ‘suitable’ if some additional conditions are

satisfied, i.e. those related to the mass of the charge,

the size of the crop and the number of strands being

cut. If any of these conditions is not satisfied

(‘acceptable’ charge is not ‘suitable’), possibilities to

combine orders are searched for or the coil is split.

The order which is supposed to be combined

with the one analysed must satisfy the following

conditions:

 grades of materials used in the realized order and

the combined order must agree,

 the width of strip in the combined order must be

greater than the width in the analysed order,

 the thickness of strip in the combined order can

be smaller or equal to the thickness in the

analysed order,

 the difference in weight between the coil and the

analysed order/output must be at least greater or

equal to the half of the combined order.

If there is no possibility to combine orders, the

feedstock coil is divided into two strands of equal

width.

If in this very case the conditions dictated by the

rules of knowledge base are satisfied, the coils is

designated for the realization of order.

If the mass of the coil is too big for the

realization of the analysed order, the coil should be

split into two smaller coils. The first coil is then used

for the realization of the order, and the other is sent

back to the store.

3.2 Production Scheduling

The input data for production schedule is:

1. planned sails quantity in tons,

2. production plan in tons,

3. production cards elaborated by Technology

Division containing:

 parameters of actual input strip coils,

 parameters of final strip;

4. working time,

5. currently active machines,

6. machines productivity and capacity,

7. currently active workers staff,

8. rolling and cutting rates,

9. time priorities of particular order.

Time priorities of order realization arise mainly

from:

 confirmation date of order realization,

 importance of the client for the company,

 declaration of fast realization time,

 acquisition of new recipients;

The executive production plan is prepared for a

given monthly production rate on the basis of annual

production plans as well as the set of orders

confirmed for the current month.

An algorithm for preparation of the executive

production plan with the use of knowledge base

must be executed in multiple phases and use both

declarative and procedural knowledge.

In the first stage the rolling mills are assigned to

realization of particular orders.

In the next phase, the individual tasks are

arranged. Formulas that are stored in the knowledge

base allow to determine whether two consecutive

orders are optimally arranged from the point of view

of criteria that define urgency of orders. Those

formulas classify current arrangement of two orders

(i and j) as ‘wrong’ or ‘correct’. If the arrangement

is ‘wrong’, a swap in their order should be

conducted. Exemplary arrangement rules are

presented below:

rule 1

distance_i_j = ‘long’

and

customer_i = ‘important’

and

customer_j = ‘important’

then

APPLICATION OF RULES ENGINES IN TECHNOLOGY MANAGEMENT

sequence := ‘correct’

rule 2

distance_i_j = ‘long’

and

customer_i = ‘important’

and

customer_j = ‘new’

then

sequence := ‘wrong’

where distance_i_j is the distance between time

of i order confirmation and j order confirmation.

The rules listed above are used for verification of

arrangement of two orders in a milling process. They

are applied in the algorithm presented below, which

sorts the orders from the point of view of their

urgency.

For i = 1 to OrdersCount - 1

For j = i + 1 to OrdersCount

If not Sequence(Queue(i),_

Queue(j)) then

X = Queue(i)

Queue(i) = Queue(j)

Queue(j) = X

End If

Next j

Next i

The sequence statement is the call to inference

engine that verifies if the order i should by processed

before the order j.

The successive formulas and procedures assign

the cutting operations to a specific cutting unit,

determine the number of work mode rotations for

individual rolling mills, delegate the tasks prescribed

to a given rolling mill for execution in individual

weeks, and compute a working time of the cutting

units for each week of the month. In every case

mentioned above we deal with a smooth

combination of typical procedures utilizing

numerical formulas with complex logical blocks that

can be effectively implemented by means of

declarative knowledge.

4 CONCLUSIONS

The two problems presented above are very complex

problems of decision-making. This statement

concerns especially the development of a monthly

action plan. Due to the high complexity of this

decision problems the test data will be perfectly

suitable for the verification of the inference engine

model which has been created.

The devised database compatible with the con-

cept of a knowledge base with various attribute

values will be used to verify the assumed

conceptions at the further stages of the project. The

works confirmed that it is necessary for the solution

of complex decision problems in the area of

production technology to apply hybrid systems – the

combination of a rule system and procedural

calculations. It appeared practically in each of the

analyzed problems. The rule systems check the

fulfillment of certain technological requirements

(whether the condition of an adequate degree of

processing of feedstock is fulfilled, whether

reduction of strip is sufficient, whether the

parameters of cutting unit allow to cut a strip of

defined parameters in a set number of strips etc.).

The checking of these conditions is a part of the

algorithms which support analyzed decision-making

processes.

Ultimately, the verification of the proposed

solutions will therefore need to develop programs

realizing procedural calculations and using devised

inference engine.

The general conclusion which is the answer to

the question of the possibility to create a tool similar

to BRE but capable of technological decision

supporting is a statement that it is necessary to

combine two forms of knowledge presentation:

declarative and procedural. It is also necessary to

ensure the possibility of communication between

this type of instrument and external data sources as

well as various types of IT tools supporting specific

technological decisions.

None of the currently available BRE does not

meet the requirements arising from the need to

combine declarative and procedural knowledge

while maintaining the logical consistency of the

knowledge model.

ACKNOWLEDGEMENTS

This research has been partially supported by the

Innovative Economy Operational Programme EU-

founded project (UDA-POIG.01.03.01-12-163/08-

01).

REFERENCES

Ligeza, A. (2006). Logical Foundations for Rule-Based

Systems. Berlin, Heidelberg: Springer-Verlag.

Maciol, A. (2008). An application of rule-based tool in

attributive logic for business. Expert Systems with

Applications. 34, 1825–1836.

ICSOFT 2010 - 5th International Conference on Software and Data Technologies