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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 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 PredictiveReactive Scheduling Robust ProActive 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 Periodic Rescheduling Time 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 MetaHeuristics MultiAgent Systems Other Artificial 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) Abumaizar and Svetska (1997) RS heuristic outperforms dispatching rules with complete rescheduling 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 realtime 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; Mediator architecture combine; Providing globally optimal schedules Predictability 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 Schedule Repair Local adjustments Savings in CPU time and the stability of the system Multi-agent Systems Robustness Very promising Integrated Systems; OR and AI for robustness and flexibility Any Questions/Comments?
