Rescheduling Manufacturing Sytsems: A framework of strategies, policies, and methods Rescheduling Manufacturing Sytsems: A framework of strategies, policies, and methods G. Vieira, J.W. Herrmann, E. Lin Salih ÖZTOP

Rescheduling manufacturing systems 2/22 Context 1)Introduction 2)Some Definitions 3)Rescheduling Environment 4)Performance Measures 5)Rescheduling Strategies 6)Rescheduling Methods 7)Results of papers

Rescheduling manufacturing systems 3/22 1. Introduction Aim of the paper A survey type article to summarize and group the articles about rescheduling and combine the definitions in each paper into the same structure.

Rescheduling manufacturing systems 4/22 Rescheduling The process of updating an existing production schedule in response to disruptions or other changes.

Rescheduling manufacturing systems 5/22 Disruptions/Changes Arrival of new/urgent jobs Machine failers/repairs Cancellation of jobs Due date change (to an earlier or a later date) Delay in the arrival of materials Shortage of materials Absent operator Change in job priority Rework Over or underestimateion of process time

Rescheduling manufacturing systems 6/22 2.Some Definitions Manufacturing System: organizes equipment, people and information to fabricate and assemble finished goods for customers. It includes order release, shop floor control and material handling.

Rescheduling manufacturing systems 7/22 2.Some Definitions Order Release: Which jobs should be moved into production. Shop Floor Control: Which operation each worker and equipment should do and when they should do. Production Schedule: planned start and end time of each job

Rescheduling manufacturing systems 8/22 2.Some Definitions Rescheduling Environment: set of jobs that need to be scheduled. Rescheduling Strategy: whether or not to genarate production schedules. Rescheduling Policy: when and how rescheduling is done. Rescheduling Methods: generate and update production schedules

Rescheduling manufacturing systems 9/22 3.Rescheduling Environment 1-Static (finite set of jobs) 2-Dynamic (infinite set of jobs) (continue to arrive) -Deterministic(no uncertainty about future) -Stochastic(some information uncertain) -No Arrival Variability (cyclic production) -Arrival Variability (flow shop) -Proces Flow Variability(job shop) (set of jobs that need to be scheduled). (schedule is repetead) (uncertainty in arrival, but same route) (uncertainty in arrival and route)

Rescheduling manufacturing systems 10/22 4.Performance Measures Scheduling Point: point in time when a scheduling decision is made. Rescheduling Period: time betweentwo consecutive scheduling points. Scheduling Stability: number of revisions or changes of a schedule Scheduling Nervousness: Opposite of scheduling stability Scheduling Robustness: measure changes to system-level performance.

Rescheduling manufacturing systems 11/22 4.Performance Measures Schedule Efficiency: –Time based measures: makespan,mean tardiness, mean flowtime,max latenss... Schedule Stability –Starting time deviance –Sequence difference Cost –Economic performance measure

Rescheduling manufacturing systems 12/22 4.Performance Measures Cost of rescheduling: –Computational Effort (human & computer) –Disruption of existing plans (nervousness) Setup costs Transportation costs

Rescheduling manufacturing systems 13/22 5.Rescheduling Strategies 1-Dynamic ( does not create schedule ) 2-Predictive-reactive( generate&update ) (whether or not to genarate production schedules)

Rescheduling manufacturing systems 14/22 5.Rescheduling Strategies 1-Dynamic ( does not create schedule ) -Dispatching rules ( prioritize jobs ) ex. SPT,EDD -Control-theoretic: Used to develop rules for which action to take and when to response the disruptions

Rescheduling manufacturing systems 15/22 5.Rescheduling Strategies 2-Predictive-reactive( generate&update ) Rescheduling Policies -Periodic (periodically schedule on a rolling time horizon) -Event-driven (reschedule after machine breakdown) -Hybrid (reschedule periodically and after an event) Iterative process: i) Evaluation/Planning ii) Solution/Control ii) Revision/Rescheduling

Rescheduling manufacturing systems 16/22 6.Rescheduling Methods 1-Schedule Generation 2-Schedule Repair -Nominal schedules -Robust schedules -Right-shift rescheduling -Partial rescheduling -Complete rescheduling

Rescheduling manufacturing systems 17/22 6.Rescheduling Methods 1-Schedule Generation -Nominal schedules -Robust schedules (To maintain good system performance with simple schedule adjustments)

Rescheduling manufacturing systems 18/22 6.Rescheduling Methods 2-Schedule Repair -Right-shift rescheduling (postpone each remainig operation with delay time amount) -Partial rescheduling (reschedule only the operations affected by the disruption) -Complete(Regeneration)rescheduling (reschedule all the jobs not processed before, including those not affected by disruption.)

Rescheduling manufacturing systems 19/22 7.Results of papers Rescheduling periods: Longer: less number of setups but more WIP Shorter: react more quickly to disruptions, but increase number of setups Rescheduling periods affects system performance more when there is a greater uncertainty. In other words frequent rescheduling becomes more effective as the level of uncertainty is high.

Rescheduling manufacturing systems 20/22 7.Results of papers A robust,partial schedule leads to better system performance than dispatching rules As processing time variability increases, dispatching rules give better performance Too long and too frequent monitoring periods resulted in worse system performance

Rescheduling manufacturing systems 21/22 7.Results of papers Under loose due date condition, performance is not sensitive to changes in rescheduling interval.At tight due date conditions,it has a much more significant effect on performance.

Rescheduling manufacturing systems 22/22 References Rescheduling manufacturing systems: a fremawork of strategies, policies, and methods. G. Vieira, J.W. Herrmann, E. Lin Using real time information for effecive dynamic scheduling- P. Cowling, M. Johanson European Journal of Operational Research 139 (2002)