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A Survey of Dynamic Scheduling in Manufacturing Systems By Djamila Ouelhadj and Sanja Petrovic. Okan Dükkancı 02.12.2013. Introduction. Dynamic environments with inevitable unpredictable real time events ; Machine failures Arrival of urgent jobs Due date changes
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A Survey of Dynamic Scheduling in Manufacturing Systems By DjamilaOuelhadj and SanjaPetrovic Okan Dükkancı 02.12.2013
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 • Completely Reactive Scheduling • No firmscheduling 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 • 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 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) • Jobshopenvironmentwithprocessing time variationsandmachinebreakdowns • At eachreschedulingperiod, a staticschedule is generatedbyusingdispatchingrules • Increasingthereschedulingperioddecreasestheperformance
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 • 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 • 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 systemsare 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