MULTI-AGENT BASED SCHEDULING

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• MULTI-AGENT BASED SCHEDULING

D. Ouelhadj ASAP (Automated Scheduling
Optimisation and Planning) Research Group School
of Computer Science and IT University of
Nottingham, UK
Open Issues in Grid Scheduling, 2003
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• Contents

1. Introduction 2.
Multi-agent systems 3. Multi-agent
based scheduling 4. Multi-agent
systems for integrated and dynamic
scheduling of steel production 5.
Conclusion
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• Introduction

• Characteristics of most scheduling systems
  developed in manufacturing environments
• ? Centralised or hierarchical.
• ? Tractable.
• ? Stochastic.

Centralised and hierarchical scheduling
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• Introduction

Classical scheduling techniques ? Operational
research-based techniques branch and bound,
etc. ? Artificial intelligence-based techniques
heuristics, meta-heuristics, hyper-heuristics,
knowledge-based systems, case-based reasoning,
fuzzy logic, etc.
Distributed Scheduling systems using MULTI-AGENTS
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• Motivations

• Real-life scheduling problems are usually
  physically or functionally distributed (air
  traffic control, manufacturing systems, health
  care, etc.).
• Complex systems are beyond direct control. They
  operate through the cooperation of many
  interacting subsystems, which may have their
  independent interest, and modes of operation.
• Complexity of real-life scheduling problems
  dictates a local point of view. When the problems
  are too extensive to be analysed as a whole,
  solutions based on local approaches are more
  efficient.
• Centralised structures are difficult to maintain
  and reconfigure, inflexible, inefficient to
  satisfy real-world needs, costly in the presence
  of failures, and the amount of knowledge to
  manage is very large.

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• Motivations

• Need for integration of multiple legacy systems
  and expertise.
• Heterogeneity. Heterogeneous environments may
  use different data and models, and operate in
  different modes.
• Robustness and reliability against failures.
• Scalability and flexibility.
• Computational efficiency. Agents can operate
  asynchronously and in parallel, which can result
  in increased overall speed.
• Clarity of design and reusability.
• Costs. It may be much more cost-effective than a
  centralised system, since it could be composed of
  simple subsystems of low unit cost.

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• What is a multi-agent system

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• Cooperation in multi-agent systems

Task announcement
Task announcement
Contract Net Protocol The contract net protocol
is a high level protocol for achieving efficient
cooperation introduced by Smith (1980) based on a
market-like protocol.


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• Multi-agent-based scheduling

• ? Local autonomy. An agent has the responsibility
  for carrying out local scheduling for one or more
  (functional or physical) components, such as
  machines and jobs.
• ? Agents have the ability to observe their
  environment and to communicate and cooperate with
  other agents in order to ensure that local
  scheduling leads to a globally desirable
  schedule.
• ? Autonomy allows the agents to respond to local
  variations, increasing the flexibility of the
  system.
• ? Concurrency. Negotiation-based decision making
  instead of totally pre-planned scheduling.
• ? Robustness fast detection of and recovery from
  the failures.
• ? Open and dynamic scheduling structures.

Announcement of production requirements

Resource agent Local Scheduling

Local scheduling
Local scheduling
Resource agents
broken down
I am free in that period







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• Multi-agent-based scheduling architectures

• Autonomous.
• Mediator.

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• Multi-agent-based scheduling architectures

Autonomous architectures
Agents representing manufacturing entities
(resources, tasks, etc.) have the ability to
define their local schedules, react locally to
local changes, and cooperate directly with each
other to generate the global optimal and robust
schedules.


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• Multi-agent-based scheduling architectures

Mediator architectures
A mediator architecture has a basic structure of
autonomous cooperating local agents that are
capable of negotiation with each other in order
to achieve production targets.That basic
structure is extended with mediator agents to
coordinate the behaviour of the local agents to
generate the global optimal and robust schedules.



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• A multi-agent system for integrated and dynamic
  scheduling of steel production

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Steel production scheduling
Integration how to integrate the
scheduling systems of
the continuous caster and the
hot strip mill ? Dynamic scheduling
Robustness against failures ?
Use Of MULTI-AGENT SYSTEMS
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• Multi-agent architecture proposed

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• Dynamic scheduling of the HSM and CC agents

• Presence of real-time events
• On the CC agent steel with wrong chemical
  compositions.
• On the HSM agent non availability of slabs.
• Robust predictive-reactive scheduling
• first constructs a predictive schedule and
  then modifies the schedule
• in response to real-time events so as to
  minimise deviation between the performance
  measure values of the realised and predictive
  schedule.

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• Dynamic scheduling of the HSM and CC agents

• Predictive schedules are generated using tabu
  search.
• Robust predictive-reactive schedules are
  generated using
• Utility, stability, and robustness measures.
• Rescheduling strategies complete rescheduling
  and
• schedule repair.

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• Dynamic scheduling of the HSM and CC agents

Utility, stability and robustness measure the
effect of real-time events, and are used to
select the best rescheduling strategy (schedule
repair or complete rescheduling) to react to
real-time events. Utility measures the change in
the value of the schedule objective function
following the schedule revision. Stability
measures the deviation from the original
predictive schedule caused by schedule revision.
Robustness combines the maximisation of utility
and the minimisation of stability.
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• Rescheduling strategies

Schedule repair and complete
rescheduling strategies

• On the HSM agent
• Do-nothing (DON)
• Simple Replacement (SR)
• Closed Schedule Repair (CSR)
• Open Schedule Repair (OSR)
• Hybrid Closed Schedule Repair (HCSR)
• Hybrid Open Schedule Repair (HOSR)
• Partial Reschedule (PR)
• Complete Rescheduling (CR)

• On the CC agent
• Insert- at- end Schedule repair (IESR)
• Insert-Heat Schedule Repair (IHSR)
• Shift Schedule Repair (SHSR)
• Swap Schedule Repair (SWSR)
• Hybrid Schedule Repair (HBSR)
• Complete Rescheduling (CR)

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• Negotiation protocol for inter-agent cooperation

The negotiation protocol is a two-level bidding
mechanism based on the contract net protocol
involving negotiation at HSMA-SYA and SYA-CCA(s)
levels. At the HSMA-SYA negotiation level, the
HSMA requests the supply of slabs from the SYA.
At the SYA-CCA (s) negotiation level, the SYA
requests the production of slabs not available in
the slabyard from the CCA(s). A commitment
duration is attached to the the negotiation
messages to specify the time windows by which the
agents must respond to a given negotiation
message.
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• Negotiation protocol for inter-agent cooperation

The negotiation protocol incorporates a
decommitment mechanism to allow the agents to
decommit by specifying appropriate contracts
alternatives in response to future real-time
events.
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• Negotiation protocol for inter-agent cooperation

Steps of the negotiation protocol
Announcing
Bidding
2. SYA-Announcement for the production of slabs
not available in the SY.
CC-1 agent
CC-1 agent
3. CCA-bid(s)
SY agent
HSM agent
SY agent
HSM agent
4. SYA-bid
CC-n agent
CC-n agent
1.HSMA-announcement for the supply of the slabs
for the current turn.
6. Forward of the contract, or renegotiation of
the non-satisfied slabs.
CC-1 agent
Contracting or renegotiating
HSM agent
SY agent
5. Establishment of a contract, or renegotiation
of the non-satisfied slabs.
CC-n agent
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Prototype developed in simulation

• A prototype has been developed in Microsoft
  Visual C/MFC.
• Cooperation between the agents is done with the
  exchange of asynchronous messages formatted in
  XML using MSMQ.

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Prototype developed in simulation
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Evaluation of the performance of the local and
global predictive schedules
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Average frequency of schedule repair and complete
rescheduling strategies
On the HSM agent
On the CC agent
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Performance of the utility and stability measures
On the HSM agent
On the CC agent
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Conclusion

• Dynamic and autonomous distributed scheduling.
  The dynamic scheduling problem is distributed
  across a set of agents.
• Local autonomy allows the agents to respond to
  local variations and self-adaptation to real-time
  events , increasing the robustness and
  flexibility of the system.
• The cooperation protocol allows the agents to
  cooperate and coordinate their local tasks in
  order to generate desirable globally predictive
  and robust schedules.
• Dynamic task allocation.

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Conclusion

• Natural load-balancing as busy agents do not
  need to bid.
• Increased Flexibility.
• Robustness against failures.
• Heterogeneity.
• Open and extensible scheduling architectures
  Agents can be introduced and removed dynamically.
• Reduced complexity.
• Reduced costs.

Open Issues in Grid Scheduling, 2003