Exploring simulation applications in education and research

Transcription

1 29-May-15 1 Exploring simulation applications in education and research Durk-Jouke van der Zee Faculty of Economics & Business / Operations Tecnomatix Digital Manufacturing Solutions Day

2 2 Dr. ir. D.J. van der Zee Research fields Manufacturing planning & control Simulation methodology Simulation-based serious gaming Health care logistics engineering Education Simulation Health care operations Asset management. Thesis projects Simulation: Methodology, Uses & Domains

3 29-May-15 3 Overview Typifying a simulation study Simulation education Beyond Simulation in support of education Simulation in support of research Simulation research on Concluding remarks

4 29-May Typifying a simulation study Engineering Project Problem domains STUDY Solutions (designs) Quantitative tools

5 29-May-15 5 Simulation study: main activities Real world (problem situation) Solutions/ Understanding SIMULATION STUDY Problem definition Computer model

6 29-May-15 6 Simulation education Understand the relevance of simulation for logistic analysis of operations in (service) industry Define and execute a simulation study in terms of its key steps, i.e. conceptual modelling, model coding, and experimenting. Provide decision support to clients and stakeholders

7 29-May-15 7 Simulation courses (BSc, MSc) Students: Operations research (30), industrial engineering (50-60), operations management (50-60) Mandatory courses Half semester Tutorials, and computer practicals Focus: Use of simulation for logistic modelling & analysis (costs vs time-based performance)

8 29-May-15 8 How? Basics Coding (Plant Simulation) Experimenting (Plant Simulation, MS Excel, SPSS) Study (case-based) Conceptual modeling Input distributions Model validation Decision support

9 29-May-15 9 Coding: Plant Simulation, why? State of art, e.g. object oriented Applicable for many purposes Model coding (SimTalk) Much used in education and industry Customer support

10 29-May Experimenting Simulation tool: model building <-> statistics Experimental design: Experiment manager Output: Data collection OK, data analysis? Presenting results

11 29-May Simulation & Practice: Doing internships Many (increasing) 3 months / 6 months Industry, Logistics, Health Abroad Software infrastructure? Licenses?

12 29-May Internships Planning, control, design of: Assembly lines Warehouses Logistic networks Parking lots Operating rooms Emergency department Intensive care Manufacturing shops Leisure etc..

13 29-May Findings Mastering code (Plant Simulation) is usually not the issue - Conceptual modelling (what & why) - Input distributions - (Experimental design) - Jump to the model instead of problem analysis. - What-if..

14 29-May In support of education Build students modelling skills Replace real-life experiences / company visits, projects Learning environment: simulation-based serious gaming.

15 29-May Asset Management - Combining tools Engineering Project Problem domains STUDY Solutions (designs) #machines, layout, machine type, tooling etc.? Quantitative tools

16 Flexible Manufacturing System 29-May-15 16

17 29-May Design Flexible Manufacturing System Use deterministic approaches to estimate number of stations, pallets etc. Use solutions found as a starting point for a series of simulation experiments. Balance costs and lead times in solution finding

18 29-May System design methodology students Focus: Testing of alternative logistic designs (time, costs, risks) Case: stroke chain

19 29-May Case: Acute stroke Ischemic stroke: blockage blood vessel Treatment: intravenous thrombolysis (actilyse; dissolve clot) Effectiveness of treatment is strongly time related (window of opportunity < 4.5 hours; every minute counts)

20 [Lahr et al. 2013] 29-May-15 20

21 29-May Health care operations students Prior knowledge: diverse; many do have a weak background in operations management Focus: introduce health care operations management issues starting from a realistic setting Effects of variability on logistic performance Health care logistic decision making Company visits not possible (group size, patient privacy )

22 29-May Emergency Department

23 29-May Set-up a mini-course for ED-staff Slides series What is logistic decision making about? Effects of variability on system performance? Alternative designs: Fast track Observatory Alternative performance measures Learn about health care (ED) operations

24 29-May In support of research Test alternative systems designs Chains, networks (production, stroke, LNG, ) Develop new rules for operations planning & control Work load control, dispatching, staff assignment. Important Model speed (accuracy of model outputs) Internal language (program logic yourself) Facilities for experimenting Output analysis

25 Testing new dispatching rules 29-May-15 25

26 29-May Research on Simulation methodology Simulation application new uses

27 29-May Simulation methodology Analysis methodology <-> experimenting Modelling methodology <-> model building Conceptual modelling Alternative uses of simulation -> serious gaming Focus on using simulation for logistic analysis purposes Simulation: art and/or science

28 29-May Conceptual Modelling Frameworks Step-wise approach Good practices Methods Diagramming techniques

29 Activity 1. Understanding the problem situation 2. Determine objectives - modeling objectives - general project objectives Details 29-May Identify clients and subject matter experts Understand the problem situation, preferably by interviewing clients and subject matter experts Define modeling objectives starting from the aims of the organization. Objectives can be expressed in terms of three components: Achievement, i.e. what the clients hope to achieve, measures of performance, and the constraints within which the clients (modeler) must work (e.g. budget, design options, available space). Consider: Time scale for doing the study. Relate to choice of model detail Flexibility ease of changing the model Run speed Visual display Ease-of-use/interaction Model/component reuse 3. Identify the model outputs Check modeling objectives for relevant performance measures Establish model outputs helping to identify potential bottlenecks in systems operations Determine format for representing responses 4. Identify the model inputs Select quantitative and qualitative model data that can be changed, in order to represent candidate solutions. Such data may be partly identified by modeling objectives Determine range over which model inputs may be varied Consider factors not being directly controlled within the system 5. Determine model contents: scope and level of detail Determine model scope: - Identify the system boundary - Identify all components in the real system that lie within the model boundary - Assess whether to include components Determine model detail (attributes) for all components included Identify assumptions and simplifications concerning model scope and detail, and assess their impact on model outputs Document model scope and detail, including a justification for their inclusion to the model

30 Example New planning system Activity 1. Understanding the problem situation 1.Determining modeling objectives 2. Determining general project objectives Main results Clients: Two groups of clients with an alternative focus on the problem: - Planning and logistics department, aiming at: reducing labor-intensity, increasing transparency of planning activities, reducing nervousness, and lower stock levels, based on better insights in the production system and improved supportive systems. - Lean team, aiming at: lowering order variety on the production floor, reducing buffer usage, a better exploitation of product/process characteristics in planning. Further investigation revealed: - Many shortcomings in the current planning system, see Promising directions for developing planning logic. For the first production stage (up to packaging) the concept of cyclical planning has been studied, and embraced as an avenue for further engineering. According to the concept blends are produced according to a fixed cyclical pattern. Further engineering concerns cycle contents, cycle length, blend sequencing, the scheduling of spare capacity to deal with demand fluctuations etc. For the second stage, mainly packaging, a customer responsive planning system is foreseen. - Simulation use should be focused on developing the concept of cyclical planning. Overall aims: The company strives to become lean. This includes a lean planning system. General modeling objectives: The model should allow for co-creation of a new planning system. Specific modeling objectives: reduce (1) stock by at least 20%, without harming service level, (2) variability of waiting times in buffers, (3) reduce nervousness, (4) reduce product waste. Expectations (process): - The simulation study facilitates a joint structural approach in planning system development. - Adequate solutions build on active participation of stakeholders in planning system development. Expectations (outcomes): - A planning concept, which is tested off-line in a dynamic setting for its logic (completeness, feasibility). - Analysis of specific scenario s related to the setup of the planning concept, and estimated customer demand. Project duration: 6 months for developing an initial planning concept; 3 months for further refinement. Flexibility: Model should allow for easy adaptations being built on a robust and jointly understood skeleton model, which clearly identifies generic elements of the planning system. Run speed: Less important for testing logic of the modeling concept. For logistic analysis it is important. Visual display: Very important. Insightful display of models should support further, joint refinement of the planning system, and solution acceptance. Model reuse: Model reuse for alternative product groups is considered. 29-May [Van der Zee et al. 2008] 2. Identifying the model outputs Performance: (1) Stock reduction: average and spread of stock levels per blend, (2) Service level: average number of stock outs per blend per week, (3) Product quality: average and spread of waiting times per blend for each buffer, (4) Nervousness of the system: Use of reserve capacity (next to fixed planning cycle), (5) Waste: Change over losses. Cause and effect: several measures. 4. Identifying the model inputs Planning system: alternative configurations, for example, choice of cycle length, cycle contents, settings for reserve capacity, local rules for operational control of production processes etc. Scenario analysis: Alternative demand levels per blend.

31 29-May Production planning - Determining scheme for production cycle - Determining inventory policy & parameters - Aggregated customer demand - Performance (stock levels, stock out...) Legend Flows Agents Goods Control Data Production scheduling - Planning reserve capacity - Produce schedule of production orders - Production schedule - Production cycle - Inventory policy - Production schedule - Available inventory and WIP - Production schedule - Inventory level for roasted beans ERP system - Update inventory and WIP of goods - Generate transport data - Inventory level for packaged liquids - Customer orders - Transport data Roasting Extraction Concentrating (1&2) Packaging Storage & Distribution Customer - Execute production schedule - Execute production schedule - Execute local rules - Execute local rules - Execute local rules - Consumption Internal agents External agents Diagrams

32 29-May Simulation-based serious gaming Serious gaming: game use for learning purposes Learning Education foster insights, creativity Training tailored towards specific settings, tool use. Simulation-based Computer support Shift of purpose: decision support (analysis) -> player learning Compare example ED

33 Alternative uses: acute health networks 29-May Pre-hospital - Primary care (GP) - EMS - Self referral Intra hospital - Emergency department - Diagnostics - Treatment Followup Efficiency Balance Timeliness Influences Effectiveness (treatment outcomes)

34 [Lahr et al. 2013] 29-May-15 34

35 29-May Concluding remarks Guided tour Simulation study: more than a coded model, although it is indispensable Simulation: art and science Simulation methodology: highly needed Applications Education Being the subject of Course support Learning environment Research Improve modelling methodology Alternative uses: Simulation-based serious gaming New fields: health

36 Thank you for your attention 29-May-15 36