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stress that the comparisons we made are based on
normal operational conditions. When more severe
and unexpected disturbances in the material flow
occur, we expect generic control to outperform
current practice as generic control reacts directly to
problems in material flow and takes actions to avoid
possible congestions and imbalances.
In the following, we list some of the lessons
learned, which are useful when trying to implement
the generic control architecture to other MHSs:
Only use generic modules, e.g., we model any
type of build workstations by the generic
workstation module. Customized modules
hamper the generic structure.
Maintain standard interfaces between different
controllers.
System size and layout characteristics should not
affect the implementation of generic scheduling
processes. This is shown by our application of
the generic dynamic routing in the screening area
of the BHS.
The planning level of control is generic, but may
include system-specific business rules in making
decisions, since it is the interface to the users’
processes. This level can also include some
algorithms to make decisions within certain
controllers. For example, the storage controller
may use an algorithm to make decisions on
which bags in particular are to be assigned to a
certain ULD.
A distributed decision-making structure is
necessary, as it supports the modularity,
robustness, and generic nature of the control
architecture. For example, if a central controller
executes routing decisions, then it would not be
able to easily handle different system layouts.
Specific algorithms can be used for local traffic
control, they can be easily integrated as add-ons
to the control architecture and do not affect
communication at the higher levels of control.
7 CONCLUSIONS
In this paper, we provided a proof-of-concept for the
applicability of generic control for MHSs in
different sectors. One of the advantages is generic
modeling of workstations, being laterals or robots.
This resulted in a simpler control software for the
retrieval process. Moreover, we implemented a
dynamic routing strategy that uses the dashboard
logic to make routing decisions and to react to
breakdowns and congestion. These control methods
have a modular and generic structure, which allows
them to be implemented in different BHSs and
different MHSs in other industrial sectors.
In future research, we will return to the concept
of generic control and apply the generic control
architecture to a large MHS in the distribution
sector. In this MHS, we will study a large ASRS and
a large order picking area. The latter consists of four
order picking stations and a network of conveyors.
For this future implementation, we will test the
applicability of the generic control architecture and
analyze the extent to which we maintain the
structure of standardized control procedures. In
particular, we are interested in studying the
applicability of the generic routing method to a
MHS in the distribution sector, and in comparing the
implementation in the distribution sector with the
implementation in the baggage handling sector.
REFERENCES
Babiceanu, R. F., Chen, F. F., and R. H. Sturges (2004)
Framework for the control of automated material-
handling systems using the holonic manufacturing
approach. International Journal of Production
Research. 42(17): p. 3551-3564.
Hallenborg, K. and Demazeau Y. (2006) Dynamical
Control in Large-Scale Material Handling Systems
through Agent Technology, in Intelligent Agent
Technology, 2006. IAT '06. IEEE/WIC/ACM
International Conference on, pp. 637-645.
Haneyah, S. W. A., Schutten, J. M. J., Schuur, P. C., Zijm,
W. H. M. (2013a) Generic Planning and Control of
Automated Material Handling Systems. Computers in
Industry, 64(3): p. 177-190
Haneyah, S.W.A., Schutten, J. M. J., Schuur, P. C., Zijm,
W. H. M. (2013b) A generic material flow control
model applied in two industrial sectors. Computers in
Industry, in press (DOI: 10.1016/j.compind.2013.03.
007)
Haneyah, S. W. A., Hurink, J. L., Schutten, J. M. J., Zijm,
W. H. M., and Schuur, P. C. (2011) Planning and
Control of Automated Material Handling Systems:
The Merge Module. In: B. Hu, K. Morasch, S. Pickl,
and M. Siegle, editors. Operations Research
Proceedings 2010, Part 8, pages 281-286, Springer
Heidelberg Dordrecht London New York, 2011. ISBN
978-3-642-20008-3.
Johnstone, M., Creighton, D. and Nahavandi, S. (2010)
Status-based routing in baggage handling systems:
Searching verses learning. IEEE Transactions on
Systems, Man and Cybernetics Part C: Applications
and Reviews. 40(2): p. 189-200.
Tařau, A., De Schutter B., and Hellendoorn H. (2009a)
Centralized versus decentralized route choice control
in DCV-based baggage handling systems. In:
Proceedings of the IEEE International Conference on
AGenericControlArchitectureforMaterialHandlingSystemsAppliedtoaBaggageHandlingSystem
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