A Survey of Dynamic Scheduling in Manufacturing Systems By

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.


Dynamic Scheduling

Completely Reactive

Scheduling

Predictive-Reactive

Scheduling

Robust Pro-Active

Scheduling


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


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


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


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 Strategies

Schedule Repair

Complete Rescheduling


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 Time

Periodic

Event Driven

Hybrid


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 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 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 ApproachesHeuris

ticsMeta-Heuris

tics

Multi-Agent Systems

Other Artifici

al Intelligence

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


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


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


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


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.


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


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


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


Multi-Agent Scheduling Architectures

Autonomous Architecture

Mediator Architecture


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


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


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


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


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


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


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


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


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


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


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?

A Survey of Dynamic Scheduling in Manufacturing Systems By

Documents

Transcript of A Survey of Dynamic Scheduling in Manufacturing Systems By