Distributed planning in a distribution centre

1. Distributed planning in a distribution centre Patrick De Causmaecker, Peter Demeester, Greet Vanden Berghe, Bart Verbeke

2. overview • problem: • scheduling of departments & personnel, with many strong and soft constraints • complexity of this problem • solution: • local scheduling with TS • distribution with agents and negotiation • results: • distribution centre

3. Distribution centre: DistriMedia • Books and cards come in from the printer • Orders come in from booksellers • Boxes with books and cards go out

5. qualification table of distribution centre

6. complexity of the problem scheduling of: 19 employees 12 tasks 5*8 hours (one week) number of possible solutions: 2^19*2^12*2^40 = 2^71 ~=1.000.000.000.000.000.000.000

7. The way to go • initial assignment of personnel to tasks • based on history • scheduling per task with tabu search (random search, with a memory) • distribution of personnel between tasks (agent technology/negotiations)

8. Initial Assignment of personnel Task 1 Tabu Search Task 3 Tabu Search Task 2 Tabu Search First Solution First Solution First Solution Negotiation Final solution Final solution Final solution

9. History record

10. work load for each task/day • receipt: 25 hours -> 3 employees (? 1 hour) • picking: 55 hours -> 7 employees (! 1 hour) • put on: 9 hours -> 1 employee (? 1hour) • reject:2 hours -> 0 employees (? 2 hours) • end station: 8 hours -> 1 employee (ok) • wrapping: 2 hours -> 0 employees (? 2 hours) • relocation: 8 hours -> 1 employee (ok) • transport: 14 hours -> 2 employees (! 2 hours) • returns: 6 hours -> 1 employee (! 2 hours) • administration: 9 hours -> 1 employee (? 1 hour) • shopping: 1 hour -> 0 employees (? 1 hour)

11. scheduling per task with tabu search • random initial solution (in our case, this is easy) • moves to solutions within a neighbourhood guided by a memory • (some moves become forbidden- tabu) • constraints are checked with numberings • good method for scheduling

12. Personnel Agentj Task agenti OmbudsAgent Start Time,Cost info Time,Task Time,Task Cost Cost Accept/refuse info Time,Cost distribution of personnel between tasks or departments TabuSearch Take most expensive Forward Evaluate Restart Add Cost coverage Take cheapest Search new problem

13. Results: assignment • receipt: EL,MA, MV • picking: CM, CV, EV, KC, LD, RV, SV • put on: RD • reject: nobody • end station: JD • wrapping: nobody • relocations: JC • transport: IM, TD • returns: KG • administration: VV • shopping: nobody

14. work load for each task/day • receipt: 25 hours -> 3 employees (? 1 hour) • picking: 55 hours -> 7 employees (! 1 hour) • put on: 9 hours -> 1 employee (? 1hour) • reject:2 hours -> 0 employees (? 2 hours) • end station: 8 hours -> 1 employee (ok) • wrapping: 2 hours -> 0 employees (? 2 hours) • relocation: 8 hours -> 1 employee (ok) • transport: 14 hours -> 2 employees (! 2 hours) • returns: 6 hours -> 1 employee (! 2 hours) • administration: 9 hours -> 1 employee (? 1 hour) • shopping: 1 hour -> 0 employees (? 1 hour)

15. Results: distribution

16. Future work • Reassignment of personnel • Other negotiation models.... • Other (larger) companies & problems

17. Questions...?