SCHEDULING A FLEXIBLE MANUFACTURING SYSTEM WITH TOOLING CONSTRAINT: AN ACTUAL CASE STUDY presented by Ağcagül YILMAZ

Content w Problem Statement w Description of the FMS w A Detailed Simulation Model w Selection of an Optimization Approach w Results of the Simulation Model w The Real Production Scheduling Problem w Conclusions and Recommendations

Problem Statement w An ideal scheduling problem: w w - FMS is under consideration, is used to manufacture a repeated ensemble of iron or aluminum parts each day by the company. w w - After reviewing the performance statistics for the FMS and previous simulation studies, it is seen that FMS was underutilized. w

Description of the FMS w The FMS under consideration in Figure-1. w The system consists of ; w - Seven numerically controlled milling and drilling machines w - One fixturing station w - One tooling station w - Four automated guided vehicles (AGVs)

Description of the FMS w - Several different transmission bell housings made of iron or aluminum are produced, two of seven milling machines for aluminum parts, rest for iron parts production. w - A typical part requires three distinct fixturing steps,for each step several machining instructions executed by milling machine using standard ensemble of tools. w - Parts and tools are delivered by AGVs, each can carry only one part pallet or tool carrier at a time.

Description of the FMS w Tool Delivery: w -Tool setter places tools to tool carrier (16 tools by a carrier).Only 3 tool carriers are available to the system. w - Tool carrier sent to milling machine by AGVs w - Tools placed in machine`s collet, next carrier withdrawn w - Tool exchanger unloads the tool from the collet to the machine`s tool magazine (store up 80 tools) w

Description of the FMS w Reverse Tool Delivery: w * Worn tools from tool magazine to tool carrier w * Tool carrier then sent by AGVs to tool station w * Refurbishing and storing tools in tool station w - Each tool has different maximum life w - AGV tracks are one way only, travel distances are direction dependent. w

Description of the FMS w - Each machine has two on-line queuing positions, for parts to be processed, finished parts, tool carrier ; two off- line buffer positions for parts waiting for processing. w - A pallet employs a custom fixture to hold a part, 24 pallets available to the system, can carry one fixture or multiple fixtures. w - Pallets enter the system through the fixturing station, parts are put on it. After process, sent to fixturing station to be refixtured or exit the system as finished parts. w

Problem Statement w As a result; w *** The goal is to determine a schedule to maximize the availability of FMS for the production of additional parts while insuring that the ensemble of required parts is produced each day. w - Minimizing the Makespan, maximizing the utilization of the mainly iron machines, taken as performance criteria. w - Due to the milling machine`s interchangeability, balancing the utilization of the milling machines will be achieved w - Total Available Production Time- Min Daily Makespan= Max. Slack Time w

Developing a Detailed Simulation Model w - To determine the makespan a detailed simulation model is developed using SLAM, experience from SIMAN. w - Tool deliveries decrease both the availability of a given machine for production and availability of the AGV system to transfer parts.So, tool management and handling are primary constraints due to frequent orders for replacement tools. w

Developing a Detailed Simulation Model w -In the Model; w * Detailed consideration of processing plans for each part type accounting the remaining tool life. w * Detailed mechanics of loading replacement tools from the tool carrier into the machine and removing worn tools. w * Dynamics of the AGV system including the control of AGV assignment.( but limited documentation) w * Dispatching Mechanism to maintain the scheduled processing order and machine assignments and to prevent pallets from being transferred to their assigned milling machines until the needed tools are available. w

SELECTION OF AN OPTIMIZATION APPROACH w * Mathematical programming approach for scheduling FMS requires too many simplified assumptions and has difficulty in finding the optimal solution w * Here, genetic optimization is applied to define the optimal schedule.But, as the generated populations of schedules are simulated it is seen that simple set of heuristic rules could generate set of alternative schedules with a nearly optimal makespan. w

Results of the Simulation Model w *Machining aluminum parts did not constrain the overall makespan. w *Whenever any part is fixtured a machine is likely to be available to process it. w *Pallets capable of carrying multiple fixtured parts should be filled whenever possible to minimize the number of their trips. w *Keep the machines of producing steel parts busy by giving priority to fixturing a steel part over fixturing an aluminum part. w *Balance the process utilizations across the iron machines.

Results of the Simulation Model w *** Using the simple heuristic rules, many schedules requiring a makespan of approximately min are generated.(88.3% of available daily production time, 85% average utilization for the iron machines).

The Real Production Scheduling Problem *** So, Why the Company has problems in meeting the minimum daily requirement? - Due to the Simulation models lacks, breakdowns and deadlocks, not assessed lost production time during the shift changes. (12% of the available production time to cover these omissions) - The main reason is no basis for assigning remaining life of the tools at each machine at the beginning of the simulation run. (However, complete set of new tools for each simulation run was assumed.) To test the effect of this assumption 20-day simulation run seen in Figure-2.

Developing a Detailed Simulation Model w LACKS OF THE MODEL; w *** Machine breakdowns and the occurence of system deadlock are omitted in the model. w - Several modes of system deadlock.ex. AGV w/low battery charging in the middle of track block passage of other AGVs or let`s say no on-line buffer positions available for a part to be unloaded in one of them. w

The Real Production Scheduling Problem w In this 20-day simulation run the followings are assumed; w -Slack time could be used to produce additional parts. w - No way to account for the tool wear resulting from assigning slack production time to the production of other parts. w - No manual transfers.

The Real Production Scheduling Problem w *** Most significant factor declining the slack time is the tooling replacements ordered by the machines. In Figure-3, as the number of replacement tools increases the number of orders requesting multiple tools also increases. w *** In Figure-4, as the number of orders requesting the multiple tools increases the time required to fill the tool orders increases.

The Real Production Scheduling Problem w *** In 20-day period, it is found that eight tools constituted almost 60% of the tool orders.Another simulation is done by putting additional two of these tools in the tool magazines.It is seen that the system run for a longer period but the number of tool orders still increased beyond the capacity of the system.

Conclusions and Recommendations w *** Has this exercise helped the owner of the FMS? The answer is no! w * True bottleneck to the production mainly tool handling constraints are emphasized, but not provide any solutions.

Conclusions and Recommendations w * In the real world, the system does not become unstable. Because, company may suspend overtime production on the weekends. w

Conclusions and Recommendations w *Other things except tooling that should be considered in general; w -Dynamics of part arrival w -The demands of the downstream assembly areas. w -Variability of the daily demand w -Not a steady state operating a system. w - Extended horizon

Conclusions and Recommendations * In general, most scheduling approaches focus upon job flow only. * It is concluded that any detailed scheduling must be performed on a near-term basis.So, real-time discrete-event simulation must be used. * Additional concerns in the modeling, scheduling and control of FMSs, solution approaches of these can be found in Davis et al, second reference in the paper.

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