1. Axxom: What happened so far

2. Basic case study  lacquer production scheduling  3 recipes for lacquers,  specifying processing steps,  resources used (shared resources)  timing dependencies between processing steps  29 orders with  starting time, due date  recipe, amount first question: is there a feasible schedule? Solved with heuristics (non-laziness, non-overtaking...) with IF and UPPAAL

3. First extension  performance factors: describe break down of resources  availability factors: describe working hour constraints Axxom approach: extend processing times by performance And availability factors. second question: is there a feasible schedule for the extended processing times? Solved with heuristics (non-laziness, non-overtaking...) (with IF and ) UPPAAL

4. Costs  storage costs for products that are finished too early  delay costs for products that are finished too late  set up costs (colour change on resources) question: what is the cost-optimal schedule? Martijn and Gerd will report on modelling and the solution..

5. Working hours Martijn and Gerd will report on this..  working hours are Monday till Friday 8-20hrs.  there are no processes running outside working hours question: how to model? What are cost-optimal solutions?

6. Scaling up  a 73 job version  a 219 job version  with extended processing times  no costs, no working hours question: is there a feasible schedule? The 73-job version could be solved with more or less the 29-job approach. For the 219 job version we came into problems with clock numbers (one clock for each job) Idea: treat also clocks as shared resources, only active jobs use a clock. -> Gerd

7. Probabilistic evaluation Approach: > take schedules generated by UPPAAL, i.e. take job starting times from schedules generated. > Make a probabilistic model taking the machine break downs into account (MODEST) > Simulate the processes (Moebius) Result: the schedules derived with extended processing times had a higher probability for delay Reason: if we reserve time for possible break down, this time is wasted when there is no break-down. ->QEST

8. Generating schedules taking probabilism into account Basic scheduling questions: 1)Long term scheduling: how many orders can be treated? 2)Short term scheduling: what to do now? Questions: Should both questions be treated with the same models? What parameters go into which model? (colour changing costs, performance factors,....) What do performance factors mean in the context of short term scheduling? Holger will discuss this in more detail

9. Similarities between long-term scheduling and performance analysis -> ideas from Henrik