Operational Research & ManagementOperations Scheduling Flow Shop Scheduling 1.Flexible Flow Shop 2.Flexible Assembly Systems (unpaced) 3.Paced Assembly

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<ul><li> Slide 1 </li> <li> Operational Research &amp; ManagementOperations Scheduling Flow Shop Scheduling 1.Flexible Flow Shop 2.Flexible Assembly Systems (unpaced) 3.Paced Assembly Systems 4.Flexible Flow Systems </li> <li> Slide 2 </li> <li> Operational Research &amp; ManagementOperations Scheduling Topic 1 Special Case of Job Shops: Flexible Flow Shops </li> <li> Slide 3 </li> <li> Operational Research &amp; ManagementOperations Scheduling3 A Flexible Flow Shop with Setups Stage 1 Stage 2 Stage 3 Stage 4 </li> <li> Slide 4 </li> <li> Operational Research &amp; ManagementOperations Scheduling4 Applications Very common in applications: Paper mills Steel lines Bottling lines Food processing lines </li> <li> Slide 5 </li> <li> Operational Research &amp; ManagementOperations Scheduling5 Setting for job j on Machine i Objectives Multiple objectives usual Meet due dates Maximize throughput Minimize work-in-process (WIP) </li> <li> Slide 6 </li> <li> Operational Research &amp; ManagementOperations Scheduling6 Generating Schedules 1. Identify bottlenecks 2. Compute time windows at bottleneck stage (release / due dates) 3. Compute machine capacity at bottleneck 4. Schedule bottleneck stage 5. Schedule non-bottlenecks </li> <li> Slide 7 </li> <li> Operational Research &amp; ManagementOperations Scheduling7 1: Identifying Bottlenecks In practice usually known Schedule downstream bottleneck first Determining the bottleneck loading number of shifts downtime due to setups Bottleneck assumed fixed </li> <li> Slide 8 </li> <li> Operational Research &amp; ManagementOperations Scheduling8 2: Identifying Time Window Local Due date Shipping day Multiply remaining processing times with a safety factor Local Release date Status s j of job j (# completed stages) Release date if s j = l Decreasing function - determined empirically </li> <li> Slide 9 </li> <li> Operational Research &amp; ManagementOperations Scheduling9 3: Computing Capacity Capacity of each machine at bottleneck Speed Number of shifts Setups Two cases: Identical machines Non-identical machines </li> <li> Slide 10 </li> <li> Operational Research &amp; ManagementOperations Scheduling10 4: Scheduling Bottleneck Jobs selected one at a time Setup time Due date Capacity For example ATCS rule </li> <li> Slide 11 </li> <li> Operational Research &amp; ManagementOperations Scheduling11 5: Schedule Remaining Jobs Determined by sequence at bottleneck stage Minor adjustments Adjacent pairwise interchanges to reduce setup </li> <li> Slide 12 </li> <li> Operational Research &amp; ManagementOperations Scheduling Topic 2 Flexible Assembly Systems </li> <li> Slide 13 </li> <li> Operational Research &amp; ManagementOperations Scheduling13 Classical Literature Exact solutions Simple flow shop with makespan criterion Two machine case (Johnsons rule) Realistic problems require heuristic approaches </li> <li> Slide 14 </li> <li> Operational Research &amp; ManagementOperations Scheduling14 Job Shops Flexible Assembly Each job has an unique identity Make to order, low volume environment Possibly complicated route through system Very difficult Limited number of product types Given quantity of each type Mass production High degree of automation Even more difficult! </li> <li> Slide 15 </li> <li> Operational Research &amp; ManagementOperations Scheduling15 Flexible Assembly Systems Sequencing Unpaced Assembly Systems Simple flow line with finite buffers Application: assembly of copiers Sequencing Paced Assembly Systems Conveyor belt moves at a fixed speed Application: automobile assembly Scheduling Flexible Flow Systems Flow lines with finite buffers and bypass Application: producing printed circuit board </li> <li> Slide 16 </li> <li> Operational Research &amp; ManagementOperations Scheduling16 Sequencing Unpaced Assembly Systems Number of machines in series No buffers Material handling system When a job finishes moves to next station No bypassing Blocking Can model any finite buffer situation (p ij = 0) </li> <li> Slide 17 </li> <li> Operational Research &amp; ManagementOperations Scheduling17 Cyclic Schedules Schedules often cyclic or periodic: Given set of jobs scheduled in certain order Contains all product types May contain multiple jobs of same type Second identical set scheduled, etc. Practical if insignificant setup time Low inventory cost Easy to implement </li> <li> Slide 18 </li> <li> Operational Research &amp; ManagementOperations Scheduling18 Minimum Part Set Suppose L product types Let N k be target number of jobs of type k Let z be the greatest common divisor Then is the smallest set with correct proportions Called the minimum part set (MPS) </li> <li> Slide 19 </li> <li> Operational Research &amp; ManagementOperations Scheduling19 Defining a Cyclic Schedule Consider the jobs in the MPS as n jobs Let p ij be as before A cyclic schedule is determined by sequencing the job in the MPS Maximizing TP = Minimizing cycle time </li> <li> Slide 20 </li> <li> Operational Research &amp; ManagementOperations Scheduling20 Minimizing Cycle Time Profile Fitting (PF) heuristic: Select first job j1 Arbitrarily Largest amount of processing Generate profile: Determine which job goes next </li> <li> Slide 21 </li> <li> Operational Research &amp; ManagementOperations Scheduling21 PF: Next Job Compute for each candidate job Time machines are idle Time job is blocked Start with departure times: </li> <li> Slide 22 </li> <li> Operational Research &amp; ManagementOperations Scheduling22 Nonproductive Time Calculate sum of idle and blocked time Repeat for all remaining jobs in the MPS Select job with smallest number Calculate new profile and repeat </li> <li> Slide 23 </li> <li> Operational Research &amp; ManagementOperations Scheduling23 Discussion: PF Heuristic PF heuristic performs well in practice Refinement: Nonproductive time is not equally bad on all machines Bottleneck machine are more important (OPT philosophy) Use weight in the sum (see example) </li> <li> Slide 24 </li> <li> Operational Research &amp; ManagementOperations Scheduling24 Discussion: PF Heuristic Basic assumptions Setup is not important Low WIP is important Cyclic schedules good Want to maximize throughput Minimize cycle time PF heuristic performs well </li> <li> Slide 25 </li> <li> Operational Research &amp; ManagementOperations Scheduling25 Additional Complications The material handling system does not wait for a job to complete Paced assembly systems There may be multiple machines at each station and/or there may be bypass Flexible flow systems with bypass </li> <li> Slide 26 </li> <li> Operational Research &amp; ManagementOperations Scheduling Topic 3 Paced Assembly Systems </li> <li> Slide 27 </li> <li> Operational Research &amp; ManagementOperations Scheduling27 Paced Assembly Systems Conveyor moves jobs at fixed speeds Fixed distance between jobs Spacing proportional to processing time No bypass Unit cycle time time between two successive jobs linebalancing </li> <li> Slide 28 </li> <li> Operational Research &amp; ManagementOperations Scheduling28 Grouping and Spacing Attributes and characteristics of each job Color, options, destination of cars Changeover cost Group operations with high changeover Certain long operations Space evenly over the sequence Capacity constrained operations (criticality index) </li> <li> Slide 29 </li> <li> Operational Research &amp; ManagementOperations Scheduling29 Objectives Minimize total setup cost Meet due dates for make-to-order jobs Total weighted tardiness Spacing of capacity constrained operations P i (L) = penalty for working on two jobs L positions apart in i th workstation Regular rate of material consumption </li> <li> Slide 30 </li> <li> Operational Research &amp; ManagementOperations Scheduling30 Grouping and Spacing Heuristic Determine the total number of jobs to be scheduled Group jobs with high setup cost operations Order each subgroup accounting for shipping dates Space jobs within subgroups accounting for capacity constrained operations </li> <li> Slide 31 </li> <li> Operational Research &amp; ManagementOperations Scheduling31 Example Single machine with 10 jobs (with 2 attributes) Each job has a unit processing time Setup cost(color) If there is a penalty cost(sunroof) </li> <li> Slide 32 </li> <li> Operational Research &amp; ManagementOperations Scheduling32 Example Data </li> <li> Slide 33 </li> <li> Operational Research &amp; ManagementOperations Scheduling33 Grouping Group A: Jobs 1,2, and 3 Group B: Jobs 4,5, and 6 Group C: Jobs 7,8,9, and 10 Best order: A B C (according to setup cost) </li> <li> Slide 34 </li> <li> Operational Research &amp; ManagementOperations Scheduling34 Due date Grouped Jobs A B C Order A C B </li> <li> Slide 35 </li> <li> Operational Research &amp; ManagementOperations Scheduling35 Capacity Constrained Operations A C B </li> <li> Slide 36 </li> <li> Operational Research &amp; ManagementOperations Scheduling Topic 4 Flexible Flow Systems </li> <li> Slide 37 </li> <li> Operational Research &amp; ManagementOperations Scheduling37 Flexible Flow System with Bypass </li> <li> Slide 38 </li> <li> Operational Research &amp; ManagementOperations Scheduling38 Flexible Flow Line Loading Algorithm Objectives Maximize throughput Minimize work-in-process (WIP) Minimizes the makespan of a days mix Actually minimization of cycle time for MPS Reduces blocking probabilities </li> <li> Slide 39 </li> <li> Operational Research &amp; ManagementOperations Scheduling39 Flexible Flow Line Loading Algorithm Three phases: Machine allocation phase assigns each job to a specific machine at station Sequencing phase orders in which jobs are released dynamic balancing heuristic Time release phase minimize MPS cycle time on bottlenecks </li> <li> Slide 40 </li> <li> Operational Research &amp; ManagementOperations Scheduling40 1: Machine Allocation Bank of machines Which machine for which job? Basic idea: workload balancing Use LPT dispatching rule </li> <li> Slide 41 </li> <li> Operational Research &amp; ManagementOperations Scheduling41 2: Sequencing Basic idea: spread out jobs sent to the same machine Dynamic balancing heuristic For a given station, let p ij be processing time of job j on i th machine Letand (the total workload) </li> <li> Slide 42 </li> <li> Operational Research &amp; ManagementOperations Scheduling42 Dynamic Balancing Heuristic Let S j be the jobs released before and including job j Define Target </li> <li> Slide 43 </li> <li> Operational Research &amp; ManagementOperations Scheduling43 Minimizing Overload Define the overload of the ith machine The cumulative overload is Minimize </li> <li> Slide 44 </li> <li> Operational Research &amp; ManagementOperations Scheduling44 3: Release Timing MPS workload of each machine known Highest workload = bottleneck MPS cycle time Bottleneck cycle time Algorithm Step 1: Release all jobs as soon as possible, then modify according to Step 2: Delay all jobs upstream from bottleneck as much as possible Step 3: Move up all jobs downstream from the bottleneck as much as possible </li> <li> Slide 45 </li> <li> Operational Research &amp; ManagementOperations Scheduling45 Example </li> <li> Slide 46 </li> <li> Operational Research &amp; ManagementOperations Scheduling46 Data </li> <li> Slide 47 </li> <li> Operational Research &amp; ManagementOperations Scheduling47 Machine Allocation </li> <li> Slide 48 </li> <li> Operational Research &amp; ManagementOperations Scheduling48 Workload From this table we obtain each roweach column </li> <li> Slide 49 </li> <li> Operational Research &amp; ManagementOperations Scheduling49 Overload With job 1 as first job of sequence </li> <li> Slide 50 </li> <li> Operational Research &amp; ManagementOperations Scheduling50 Overload Matrix With job 1 to 5 as first job of sequence </li> <li> Slide 51 </li> <li> Operational Research &amp; ManagementOperations Scheduling51 Dynamic Balancing First Job </li> <li> Slide 52 </li> <li> Operational Research &amp; ManagementOperations Scheduling52 Selecting the Second Job Calculate the cumulative overload where </li> <li> Slide 53 </li> <li> Operational Research &amp; ManagementOperations Scheduling53 Cumulative Overload Selected next </li> <li> Slide 54 </li> <li> Operational Research &amp; ManagementOperations Scheduling54 Final Cycle Schedule jobs 4,5,1,3,2 Release timing phase Machine 4 is the bottleneck Delay jobs on Machine 1, 2, and 3 Expedite jobs on Machine 5 </li> <li> Slide 55 </li> <li> Operational Research &amp; ManagementOperations Scheduling55 Final Sheet Flexible Manufacturing Systems (FMS) Numerically Controlled machines Automated Material Handling system Produces a variety of product/part types Scheduling Routing of jobs Sequencing on machines Setup of tools Similar features but more complicated </li> </ul>

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