2 DESCRIPTION OF THE
PROBLEM AND THE
PROPOSED APPROACH TO ITS
SOLUTION
The problem of forming schedules of the aggregate
and final assembly lines includes:
• formation of production schedules using
various criteria, preferences and constraints;
• forecast of the possibility of performing
schedules with available resources;
• adaptive re-scheduling of assembly in case of
unforeseen events in real time;
• identification of bottlenecks and resource re-
allocation between workshops;
• optimization of production schedule by
comparing options and initial conditions;
• control of implementation of production
schedules, etc.
Complexity of this processes is caused by NP-
hard nature of combinatorial search, nonlinearity of
decision making space, interdependences of
operations in technological processes, specific
individual features of matching rules for assigning
operations to resources, including competencies of
workers, and a number of other features.
Ramp-up stage is bringing additional complexity
by high dynamics of production, when various
events are constantly taking place: new orders are
emerging, composition of products is changing,
technological processes are being refined, supply
terms are broken, work centers fail or defects are
detected.
This complexity and high dynamics of
scheduling process, which is event-driven in this
case by definition, leads to the fact that traditional,
centralized, hierarchically organized, sequential
methods and algorithms of combinatorial type
cannot effectively solve this problem with
acceptable quality and within the required time for
practical applications in the workshops.
For practical solution of the problem the paper
proposes new distributed solution based on models,
methods and algorithms for adaptive scheduling
(Rzevski, 2014).
First of all, in this solution, instead of one "large"
central scheduler for aggregate and final assembly
shops, a distributed "system of systems" is proposed.
It is built as a multi-level network of "small" stand-
alone operational planners for individual workshops
with a plan horizon of up to a month, working in
coordination with the end-to-end scheduler of
aggregate and final assembly workshops with a
horizon of up to 3-6 months.
Secondly, to solve the problem of ramp-up
management, it is suggested to use multi-agent
technology which utilizes the concept of an "agent"
– an autonomous software object capable of
perceiving the situation, making decisions and
interacting with others (Skobelev, 2014). Solution of
any complex task in the multi-agent system is
formed by self-organization of agents through
interaction of dozens and thousands of agents of
demand-and-resource network (DR-network),
continuously competing and cooperating with each
other (Vittikh, 2003).
The schedule of workshops is created as a self-
organizing network of orders and resources,
adaptively changing depending on events in real
time. In the process of self-organization, software
agents of orders and resources search for each other,
at first choosing the best free options, and then
resolving conflicts until the system is balanced and
none of the options can improve the overall
performance of the system. Then the calculation
process stops, and the solution is given to the user.
This process more naturally describes the way
experienced managers and dispatchers usually form
schedules, finding a balance of interests among all
concerned parties. The transition from combinatorial
search for the optimal schedule to the search for an
"acceptable" (reasonable) schedule corresponding to
the current situation (reflecting the balance of
interests of orders and resources at the given
moment) allows to create quality schedules.
Thirdly, vitally important knowledge is need to
be accumulated when such systems are implemented
and used. However, this knowledge is often difficult
to formalize. It can be used to improve the quality of
planning, for example, information on the possibility
of parallelizing part of technological operations or
reinforcing them by adding more workers in order to
accelerate the work, or knowledge about the
competencies of workers, their compatibility in
shifts, etc.
For this purpose, the paper proposes using the
Knowledge Base (KB) for accumulation,
formalization and use of these parts of knowledge,
as storage of such knowledge is currently not
provided by any of the corporate systems. Such a
KB can be based on domain ontology as a semantic
network of classes of concepts and relations – a
technology that is actively developed within the
Semantic Web to describe the content of Internet
resources (Skobelev, 2012).
Designing Distributed Multi-Agent System for Aggregate and Final Assembly of Complex Technical Objects on Ramp-up Stage
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