Problem description

1. Simulation of a job shop scheduling strategyMarjan van den Akkerjoint work with:Koen de Bontridder, Rob van Geel, Han Hoogeveen

2. Problem description • Job shop scheduling • Minimize total weighted completion time or total weighted tardiness • In practice: disturbances in processing times Advanced scheduling algorithms or dispatching rules? • Evaluate by discrete-event simulation

3. Scheduling algorithms • Tabu search algorithm [DeBontridder 2001] • Dispatching rules: • Minimum slack time • First-come-first-served • Rescheduling: • After fixed number of operations • Based on deviation from expected starting time

4. Processing times • U[0.95 pj,1.05 pj] • U[0.8 pj,1.2 pj] • Exp(pj) • 4-Erlang(pj/4)

5. Discrete-event simulation • Events: • operation becomes available for processing • operation is finished • Scheduling algorithms use expected processing times pj • Test: • 20 jobs, 10 machines, 200 operations • 2 instances, per instance 3 runs of tabu search • for each scenario 50 simulation runs • compute tabu search value after each run: ‘real optimum’

6. Computational results wjCj

7. Preliminary results wjTj • Instances with optimal wjTj close to zero are very sensitive to disturbances (sim/tabu(after)) does not make sense. • Tests: • generate instances with optimal wjTj close to zero • multiply deadlines by factor a = 0.98,0.96… ,0.5 • Larger variance leads to larger value of tabu(after) • Behaviour similar to wjCj, but for large a MST may outperform Tabu for the Erlang distribution.

8. Computational results wjCj • Tabu is better for smaller variance (including Erlang) • Dispatching is better for larger variance • Rescheduling improves, although you can do too much