ISA-95 Friendly RTN Models for Industrial Production Scheduling. Pedro M. Castro Ignacio E. Grossmann Iiro Harjunkoski

Transcription

1 ISA-95 Friendly TN odels for Industrial roduction Scheduling edro. Castro Ignacio E. Grossmann Iiro Harjunkoski

2 otivation EWO aims to simultaneously account for KI across multiple business units Integration of supply chain management, production control, planning & scheduling Need to efficiently transfer data and information between different systems Focus on production management system and scheduling solution ISA-95 standard can act as data-exchange platform Goal: Adopt standard in a way that fulfills requirements of most common scheduling problems arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 2

3 Generic scheduling approach esource-task Network rocess representation roduction recipe & network topology athematical formulations Discrete- & continuous-time Key success drivers odeling paradigm easily understood by business stakeholders Flexible approach, easily modified when new information becomes available Challenge Develop TN models complying to the ISA-95 standard kg 200 kg ix 1 A_ix_1 60 min 1000 kw A_ix_2 70 min ix kw ower 50 kw 10 kw S1A A_eact A_ack 120 min 30 min 320 kg A 20 kg eactor Waste ack Line arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 3

4 ISA-95 data implementation Variety of complex XL files rocess segment information Equipment aterial ersonnel Operations capability Operations definition Operations schedule ixing eaction ackaging arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 4

5 apping data with TN process model Sets definition Set Description Elements B2L file (XL implementation ISA-95) T aterials ower, 1, 2, Waste, A, B, C aterialinformation EQ Equipment ixer1, ixer2, eactor1, acking1, acking2, acking3 EquipmentInformation ID Class ID ixer, eactor, ack EquipmentInformation O Operations roda, rodb, rodc OperationsDefinitionInformation Set SG Description Segments ixing, eaction, Elementsackaging B2L rocesssegmentinformation file (XL implementation ISA-95) 1(SG,SG) Immediate precedence ixing.eaction, eaction.ackaging rocesssegmentinformation 2(SG,ID) Segment belongs to class ixing.ixer, eaction.eactor, ackaging.ack rocesssegmentinformation 3(ID,EQ) ID belonging to equipment ixer.ixer1, ixer.ixer2, eactor.eactor1, ack.acking1, ack.acking2 arameters from production recipe ateq1(o,sg,t) 1 2 Waste A B C roda.ixing roda.eaction 20 roda.ackaging 320 rodb.ixing -500 rodb.eaction 50 rodb.ackaging 400 rodc.ixing rodc.eaction 15 rodc.ackaging 380 ateq2 (O,EQ,T) ixer1. ower ixer2. ower eactor1.ower EquipmentInformation acking1.ower OperationsDefinitionInformation.xml acking2.ower roda acking3.ower rodb rodc Time (O,EQ) ixer1 ixer2 eactor1 acking1 acking2 acking3 roda rodb rodc arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 5

6 TN discrete-time model (GAS) Few lines of code generate: esources Time availability parameters Subsets for domain constraints Tasks Structural parameters rocessing times Timing of resource consumption/production Actual model quite simple arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 6

7 Computational studies How far can we go in terms of problem size? Vary # batches per product (1:1:1 A,B,C) Short-term scheduling models, minimizing makespan Discrete-time (DT), 5 min slots ore batches multiple executions of the same task Continuous-time with multiple time grids (CT) ore batches more variables and constraints (batch index) # batches akespan (min) DT CUs CT CUs >1600 (gap=71.3%) Discrete-time much better approach But we want 100+ batches, sometimes 500! arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 7

8 How about relying on periodic scheduling? Step 1: Find optimal cyclic pattern Given batch proportion between different recipes (ex. 1:1:1) Cycle time= 510 min Step 2: Solve short-term scheduling for a given number of repetitions Find optimal start-up and shutdown phases Almost all binary variables are fixed Timing of events still free to vary Start-up epeating pattern Shut-down arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 8

9 esults for periodic-based scheduling Advantages Exact same solution as short-term discrete-time model Continuous-time is more accurate Better computational performance Disadvantages eriodic scheduling is for stable production of a few products Underlying model works just for a specific type of plant topology Why not periodic-based scheduling with discrete-time? Need narrow time window for task execution Difficult to generate because start-up phase determined by optimization # batches akespan (min) Total CUs # batches Binary variables Total variables Equations arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 9

10 Conclusions Guidelines for generating TN-based scheduling model from ISA-95 data Existing generic models simply not good enough to allow tackling problems of industrial relevance Need for optimization based decomposition strategy that is competitive with simple heuristics Concept tested by repeating pattern from periodic scheduling Continuous-time approach allows for more freedom Using just a few binary variables Future work Develop a generic and efficient matheuristic Integration of (meta)heuristics and mathematical programming Test in a set of real-life problems arch 9, 2016 Enterprise-Wide Optimization eeting: ABB roject Overview 10