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A Survey of Dynamic Scheduling in
Manufacturing SystemsBy
Djamila Ouelhadj and Sanja Petrovic
Okan Dükkancı02.12.2013
Introduction
Dynamic environments with inevitable unpredictable real time events; Machine failures Arrival of urgent jobs Due date changes
Feasible schedules become infeasible Scheduling Theory vs. Scheduling Practice
Very little correspondence between these two (Shukla and Chen, 1996)
Introduction
Dynamic Scheduling The problem of scheduling in the presence of
real-time events Implementation to the real-world scheduling
problems
Dynamic Scheduling in manufacturing systems Handling the occurrence of real-time events
The Dynamic Scheduling Problem
Several manufacturing systems; Single and Parallel Machines, Flow and Jobs
Shops, Flexible Manufacturing Systems Real time events;
Resource-related; Machine breakdowns, operator illness, unavailability or
tool failures, loading limits, defective materials, etc. Job-related;
Rush jobs, job cancellation, due date changes, change in job priority and processing time, etc.
The Dynamic Scheduling Problem
Dynamic Scheduling
Completely Reactive
Scheduling
Predictive-Reactive
Scheduling
Robust Pro-Active
Scheduling
The Dynamic Scheduling Problem
Completely Reactive Scheduling No firm scheduling in advance Scheduling decisions made locally in real-time Priority dispatching rules
Quick, intuitive and easy to implement Lower shop performances
The Dynamic Scheduling Problem
Predictive-Reactive Scheduling Most common dynamic scheduling approach Schedules are revised after real-time events Deviation from the original schedule affects
other activities Robust predictive-reactive scheduling
Minimize the effect of disruption on the performance measure value
Consider both shop efficiency and deviation from the original schedule (stability) at the same time
The Dynamic Scheduling Problem
Robust Predictive-Reactive Scheduling A bi-criterion robustness measure for single
machine Machine breakdowns Minimize of makespan and impact of the
schedule change (stability) Stability
Deviation from the original job starting time Deviation from the original sequence
Stability can be increased with almost no effect on makespan
The Dynamic Scheduling Problem
Robust pro-active scheduling Predictive schedules Main difficulty is the determination of the
predictability measure Mehta and Uzsoy (1999)
Single machine, machine breakdowns, minimize the max. lateness
The effect of disruption measured by deviation of the job completion time
The deviation is reduced by inserting idle time in the predictive schedule
Significant improvement in predictability with very little effect on the max. lateness
Rescheduling in the Presence of Real Time Events
How to React?
• The Decision of Rescheduling Strategies
When to React?
• The Problem of Rescheduling Time
Rescheduling in the Presence of Real Time Events
Rescheduling Strategies
Schedule Repair
Complete Rescheduling
Rescheduling in the Presence of Real Time Events
Scheduling Strategies Schedule Repair
Local adjustment of the current schedule Potential savings in CPU time and stability of the
system Complete Rescheduling
New schedule from the scratch Optimal solution can be obtained But, rarely practical and very high CPU time Also, instability and shop floor nervousness
Schedule Repair is most common strategy
Rescheduling in the Presence of Real Time Events
Rescheduling Time
Periodic
Event Driven
Hybrid
Rescheduling in the Presence of Real Time Events
Rescheduling Time Periodic Policy
Schedules made at regular intervals Series of static problems More schedule stability and less schedule nervousness A real-time event just after rescheduling can create
some problems Determining the rescheduling period is very important Muhlemann et al. (1982)
Job shop environment with processing time variations and machine breakdowns
At each rescheduling period, a static schedule is generated by using dispatching rules
Increasing the rescheduling period decreases the performance
Rescheduling in the Presence of Real Time Events
Rescheduling Time Event driven Policy
Rescheduling after the real-time events Most common policy Vieria et al. (2000a, 2000b)
Comparison between periodic and event driven policies on single and parallel machines
Lower rescheduling frequency decreases the number of set-ups, but higher rescheduling frequency reacts more quickly to disruptions
Rescheduling in the Presence of Real Time Events
Rescheduling Time Hybrid Policy
Combination of periodic and event driven policy Rescheduling made periodically except the occurrence
of real-time events
Church and Uzsoy (1992) Rescheduling periodically Regular events are ignored After an urgent events, complete rescheduling When the length of rescheduling period increases, the
performance of periodic scheduling decreases. Event driven method works well
Dynamic Scheduling Techniques
Solution Approaches
Heuristics
Meta-Heuris
tics
Multi-Agent Systems
Other Artifici
al Intelligence
Techniques
Dynamic Scheduling Techniques
Heuristics Schedule repair methods, not guarantee the
optimal schedule Most common; right-shift schedule repair, match-
up schedule repair and partial schedule repair Right-shift (RS) schedule repair; the remaining operations
are shifted forwards in time by the amount of disruption time
Match-up (MU) schedule repair; rescheduling approach to match-up with the pre-schedule at some point in the future
Partial schedule repair; rescheduling only the operations in failure
Dispatching rules are heuristics for completely reactive scheduling
Dynamic Scheduling Techniques
Heuristics Yamamoto and Nof (1985)
RS heuristic outperforms dispatching rules with complete rescheduling
Abumaizar and Svetska (1997) Partial Schedule Repair vs. Complete Rescheduling vs.
RS Schedule Repair in terms of efficiency and stability Partial Schedule Repair decreases deviation and
computational complexity compared to complete rescheduling and right shifting
Bean et al. (1991) MU Schedule Repair provides near optimal solutions
and higher predictability than complete rescheduling
Dynamic Scheduling Techniques
Heuristics Nof and Grant (1991)
Rerouting the jobs to alternative machines, job-splitting Dispatching Rules
No rule performs well for all criteria Ramasesh (1990) and Rajendran and Holthaus (1999)
Classified these rules as; rules involving processing times, rules involving due dates, simple rules involving neither processing times
nor due dates, rules involving shop floor conditions, rules involving two or more of the first four
categories
Dynamic Scheduling Techniques
Meta-Heuristics High level heuristics that guide the local search
heuristic to escape from local optima Tabu search (TS), Simulated Annealing (SA) and
Genetic Algorithms (GA) Dorn et al. (1995)
Tabu search to repair a schedule Zweben et al. (1994)
Simulated annealing to repair schedules
Dynamic Scheduling Techniques
Meta-Heuristics Chryssolouris and Subramaniam (2001)
Genetic algorithms for dynamic scheduling of manufacturing job shops
Two performance measures; mean job tardiness and mean job cost
Performance of genetic algorithm is better than the common dispatching rules
Wu et al. (1991, 1993) Genetic Algorithms vs. Local Search Heuristics to
generate robust schedules Genetic algorithm outperforms local search heuristic
in terms of makespan and stability.
Dynamic Scheduling Techniques
Multi-Agent Based Dynamic Scheduling Centralized Scheduling System Hierarchical Scheduling System
Scheduling decision made centrally at the supervisor level and executed at the resource level
Central computer has responsibility for; scheduling, dispatching resources, monitoring any deviation dispatching corrective actions
Dynamic Scheduling Techniques
Drawbacks of Centralized and Hierarchical Scheduling Systems Existence of one central computer; bottleneck of the
system Modification of configuration is expensive and time
consuming Latency time of decision-making; late response to the
real-time events In highly dynamic environment, centralized and
hierarchical scheduling systems are inefficient Decentralize the control of the manufacturing system
Reducing complexity and cost Increasing Flexibility Enhancing Fault Tolerance
Dynamic Scheduling Techniques
Multi-Agent Systems in Dynamic Scheduling Local autonomous agents carry out local
schedules that increases the robustness and flexibility
Dynamic interaction and cooperation between agents
Shorter and simpler software compared to centralized approach
Dynamic Scheduling Techniques
Multi-Agent Scheduling Architectures
Autonomous Architecture
Mediator Architecture
Dynamic Scheduling Techniques
Autonomous Architectures Agents representing manufacturing entities
such as resource and jobs Generating local schedules and react locally to
local disruptions Cooperating with each other for global optimal
and robust schedules
Dynamic Scheduling Techniques
Goldsmith and Interrante (1998), Oeulhadj et al. (1998, 1999, 2000) Simple multi-agent architecture with only
resource agents Agents are responsible for dynamic local
scheduling of the resources They negotiate with each other via “contract net
protocol” to generate global schedule Each agent performs;
Scheduling Detection Diagnosis Error Handling
Dynamic Scheduling Techniques
Sousa and Ramos (1999) Multi-agent architecture with job and resource agents Job agents negotiate with resource agents for the
operation of job via “contract net protocol” When a disruption occurs;
Resource agent sends a machine fault message to job agents Job agents renegotiate the other resource agents in order to
process the operations in failure
Sandholm (2000) Instead of “contract net protocol”, “levelled
commitment contracts” are used Decommiting from the contract by paying the
penalty
Dynamic Scheduling Techniques
Mediator Architectures With large number of agents, autonomous
architectures have some difficulties; Providing globally optimal schedules Predictability
Mediator architecture combine; Robustness Optimality Predictability
Mediator outperforms autonomous due to ability to plan further in the future ability to react disturbances
Dynamic Scheduling Techniques
Mediator Architectures Additional to local agents of autonomous
architecture, mediator agent Coordinate the local agents Contribute to same decision making process Overview of the entire system
Local agents deals with the reaction to disruption Mediator agents improve the global performance
Dynamic Scheduling Techniques
Ramos (1994) Mediator architecture consists of;
Task Agents Task Manager Agents, Resource Agents Resource Mediator Agents
Task manager agent creates task agents The resource mediator agent negotiates with
resource agents for execution of tasks via “contract net protocol”
When a disruption occurs; Messages are sent to the resource mediator agent The resource mediator agent renegotiates with other
resource agents
Dynamic Scheduling Techniques
Sun and Xue (2001) Mediator reactive scheduling architecture Two mediators;
Facility Mediator Personnel Mediator
Match-up rescheduling strategy and agent based mechanism are used to repair only part of the schedule
Dynamic Scheduling Techniques
Other Artificial Intelligence Techniques Knowledge-based systems, neural networks, case-
based reasoning, fuzzy logic, Petri nets, etc. Knowledge-based systems
Variety of technical expertise on the corrective action to undertake
La Pape (1994) SONIA; a knowledge-based job-shop predictive-reactive
scheduling system Schedule repair heuristics;
Relaxing due dates Extending work shifts Operation postponed until the next shift Reduction of idle times of resources by permuting
operations
Dynamic Scheduling Techniques
Hybrid Systems combines various artificial intelligence techniques
Dorn (1995) Case-based reasoning and fuzzy logic for
reactive scheduling Garetti and Taisch (1995) and Garner and Ridley
(1994) Knowledge-based systems and neural networks
in reactive scheduling
Comparison of Solution Techniques
Heuristics; Widely used due to their simplicity Can be stuck in poor local optima
Meta-heuristics; SA and TS are more efficient to find a near-
optimal solutions in a reasonable time compared to GA
Knowledge-based systems are limited by the quality and integrity of the specific domain knowledge
Comparison of Solution Techniques
Centralized and Hierarchical Manufacturing Systems Globally better schedules Problems with the reactivity to disturbance
Multi-agent Systems Decentralize the control of manufacturing
system Localize the scheduling decisions Sandholm (2000):
Agents can locally react to local changes faster than centralized system could
Providing an architecture that is reliable, maintainable, flexible, robust and stable
Comparison of Solution Techniques
Autonomous vs. Mediator Architectures Autonomous; cost-efficient, flexible and robust
against disturbances Suitable for system with a small number of agents But, providing globally optimized performance is
questionable The behaviour of the system is unpredictable with
a large number of agents
Mediator; improve performance compared to autonomous in complex manufacturing systems
Combining robustness against disturbances with global performance optimization and predictability
Conclusion
Most manufacturing systems operate in dynamic environment
Dynamic scheduling; Predictive-reactive scheduling
Robustness Schedule Repair
Local adjustments Savings in CPU time and the stability of the system
Multi-agent Systems Very promising
Integrated Systems; OR and AI for robustness and flexibility
Any Questions/Comments?