Interactive Scheduling Systems (II)

1. Interactive Scheduling Systems (II) 1998년 4월 28일 서울대학교 산업공학과 공장자동화연구실 최 병 대

2. Solving problems in production scheduling Robin Lane and Stephen Evans (The CIM Institute, Cranfield Univ., UK) Computer Integrated Manufacturing Systems, 1995, 8(2), pp.117-124 공장자동화 연구실 최 병 대

3. Contents • Introduction • General problem solving • Problem solving in production scheduling • Solving minor problems in scheduling • Solving major problems in scheduling • Benefits

4. Introduction • Enhanced Support for Production Scheduling project • design and development of DSS for master scheduling and detailed scheduling with MRP II Systems • Motivation • a great deal of dissatisfaction with the master scheduling process • This paper • describes a model of problem solving processes in master scheduling as a basis for improving the effectiveness of people and computers within production scheduling • examples from two prototype software applications

5. Stage Task Comment What can be done One or more How to go about it Put solution into action 1 2 3 4 5 6 7 8 Identify problem Gather data Analyse data Generate solutions Select solutions Plan Test/Rehearse Action General problem solving • General problem solving process (by Newman)

6. Problem solving in production scheduling • Scheduling tasks • ill-defined tasks • difficulty in determining when they have been successfully completed • schedulers view their tasks as involving many smaller well-defined problems not one large ill-defined problem • not just problems that can be solve, but also difficulties • Two types of problem solving process • minor problems: associated with individuals & components • major problems: associated with resources & performance criteria • informating: minor problems solving results are used within support for the major problem solving

7. Stage Task Comment Using specified parameters Scheduler defines ‘very small’ To all remaining problems Only where necessary 1 2 3 4 5 6 Gather data Process data Identify problems Discard very small problems Apply predetermined procedure Modify actions individually Solving minor problems (1/2) • Minor problem solving process MRP II module MPS exceptions processing Exception Messages 1 2 3 4 5 6

8. Solving minor problems (2/2) • MPS exceptions processing module • discarding very small problems • applications of a predetermined procedure • the importance of the item • classification: Pareto analysis (A, B1, B2, B3, C) • prioritization: on/off labelling • the production process for the item: • item grouping: by processing routing and production lead time • definition of planning horizon (firm, planning 1, 2, 3, 4, free) • the timing of the recommended change • driving factor(item classification) vs. resistance factor(planning horizon) • the size of the recommended change • the type of change recommended(e.g., reschedule, or cancel) • Modify actions individually

9. 1 2 4 5 6 7 8 3 Solving major problems (1/2) • Major problem solving process Stage Task Comment In terms of significance According to significance The preferred set Change the old schedule 1 2 3 4 5 6 7 8 Gather data Analyse data Identify problems Assess problems Prioritise problems Generate sets of solutions Select one set of solutions Implement selected set MRP II module RCRP applications Exception Messages

10. Solving major problems (2/2) • RCCP applications • Generates sets of solutions (two stages) • Outline solver: formulate approximated plan for detailed solutions • use of alternative resource • reschedule in by one week • work overtime up to 20% of base capacity • hire temporary labour up to 10% of base capacity • Detailed solver: selects actions that fulfill req’ts of outline solutions • finding suitable jobs for rescheduling • three tactics: Limit, Mix, Spread • batching rule and usage of other resources check • finding the most appropriate resource to use as an alternative • selection: resources with lightest workload or best match

11. Benefits • Learning: build up trust in the capabilities of the software rather than relying solely upon their own abilities • Concentrating on the more difficult issues of matching schedules to overall performance criteria • Improving the integration of different scheduling tasks within an organization • Co-operative problem solving: well suited to cell-based planning or the use of electronic control stations operated with a high degree of autonomy

12. Cooperative system design in scheduling P. Lopez*, P. Esquirol*, L. Haudot* and M. Sicard** *Laboratoire d’Analyse et d’Architecture des Systemes du CNRS, France **Dassault Avitation DGT/DTN/EL, France IJPR, 1998, 36(1), PP. 211-230

13. Contents • Introduction • Decision problem in scheduling • Experimental example • Knowledge acquisition • Constraint modeling • Constraint-based analysis • Proposed architecture for cooperative release • Conclusion

14. Introduction • Approach based on decision support • A part of ‘constraint based analysis’ and propagation paradigms • its aim is not to generate a solution relative to a given criteria but to characterize a set of solutions compatible with the constraints • only apply the constraint propagation mechanism and not the labelling functions • Issues • to understand how the cooperation between two interacting poles can take benefits from an efficient implementation of constraint-based reasoning in scheduling

15. Decision problem in scheduling • Cooperation: interaction between two poles human and computer to achieve a common goal • Methodology for cooperative scheduling • ‘constraint based analysis’: characterize the set of feasible schedules • decision phase based on specific criteria that will allow the selection of a particular feasible solution

16. Experimental example • A flanged element mfg. Workshop turning out aircraft structural parts • job shop • 3000 part types, 40 classes, 12 routeings • part: reference #, routing class, delivery date • cutting, trimming. Annealing, hardening, forming... • limited series production • low level of automation • Current system • provide set of parts which are automatically created • not considering real workshop status, absence of flexibility, absence of indicators which facilitate decision-making • strong need for a cooperative decision support system

17. Knowledge acquisition • To study cognitive processes for problem resolution and cooperation • Knowledge Acquisition methodologies • Knowledge collection: brainwriting • Knowledge structuring • reference graph building • path identification on the reference graph • elementary idea extraction • knowledge classification • Concepts analysis: crossing ideas in matrices • In this case • three important fields noted : needs of flexibility, support and interactivity

18. Constraint modelling (1/2) • Class and area constraints • The geometric problem: non-overlapping

19. Constraint modelling (2/2) • Time constraints • C1: individual time windows constraints for elementary cutting op. • C2: pooling time constraints • C3: disjunctive constraints on the cutting machine

20. Constraint-based analysis (1/4) • Arc consistency: disjunctive constraints, removing inconsistent start time • Edge-finding

21. Constraint-based analysis (2/4) Rule 1 Rule 2

22. Constraint-based analysis (3/4) Rule 3

23. Constraint-based analysis (4/4) • Adjustment of limit times (updating mechanism) For R2, directly updated For R3 For R1 For R4

24. Proposed architecture for cooperative release

25. Conclusion • The paper presents an interdisciplinary methodology based on • KA:interaction recommendations for the design • constraint-based reasoning: consistency checking for the problem at hand • This work is of a generic nature • can be extended for the design of dedicated tools in other domains