1. Thèse présentée par Anne-Laure Ladier et dirigée par Gülgün Alpan Soutenue publiquement le vendredi 21 novembre 2014 Devant un jury composé de: Mme Luce BROTCORNE Chargée de Recherche, HDR, INRIA Lille Nord EuropeRapporteur M. Pierre BAPTISTE Professeur, École Polytechnique de MontréalRapporteur M. Damien TRENTESAUX Professeur, Université de Valenciennes et du Hainaut-CambrésisExaminateur M. Allen G. GREENWOOD Professeur, Mississippi State UniversityExaminateur Mme Gülgün ALPAN Maître de Conférences, HDR, Grenoble INPDirectrice de thèse P LANIFICATION DES OPÉRATIONS DE C ROSS -D OCKING Prise en compte des incertitudes et de la capacité des ressources internes

2. S CHEDULING C ROSS -D OCKING O PERATIONS Integration of operational uncertainties and resource capacities Anne-Laure Ladier 21 st of November 2014

3. Anne-Laure Ladier | PhD defense | 21st of November 2014 3 The logistic challenge: deliver faster, cheaper

4. E XAMPLE OF S UPPLY -C HAIN Anne-Laure Ladier | PhD defense | 21st of November 2014 4 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

5. C ROSS - DOCKING Less than 24h of temporary storage Arrival and docking Unloading Control Transfer Loading 1 color = 1 client ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion 5 Anne-Laure Ladier | PhD defense | 21st of November 2014 Cross-docking operations: what are the real issues? Robust truck scheduling

6. 120 ARTICLES / 8 PLATFORM VISITS Anne-Laure Ladier | PhD defense | 21st of November 2014 6 Arrival time Departure time Operational level Literature 39% 61% 1% 14% 20% 65% Industry 62% 38% 0% 75% 25% 0% Inbound Outbound Both No constraint Literature 79% 7% 14% 10% 90% 62% 29% 9% 76% 24% Industry 13% 38% 63% 0%0% 100% 12% 0% 88% 0%0% 100% Exclusive Dest exclusive Mixed Yes No Zero Limited Infinite Limited Infinite Service mode Preemption Storage capacity Resource capacity Tactical level Dest exclusive Mixed Yes No Zero Limited Infinite Limited Infinite Exclusive Inbound Outbound Both No constraint 93% 7% 36% 49% 15% 100% 0% 0%0% 50% Truck filling Interchange ability Full LTL Pre-dest Dest exclusive Post-dest Full LTL Pre-dest Dest exclusive Post-dest Per truck Concentrated Per truck ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling Management of late arrivals Take human resources into account How to manage delayed trucks without disturbing other ongoing operations? How to schedule the workers in a way that fits the operations workload?

7. R ESEARCH Q UESTIONS Anne-Laure Ladier | PhD defense | 21st of November 2014 7 How to manage delayed trucks without disturbing other ongoing operations? How to schedule the workers in a way that fits the operations workload? How to schedule the trucks? ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

8. O VERVIEW Anne-Laure Ladier | PhD defense | 21st of November 2014 8 1. Truck scheduling 3. Employee timetabling and rostering 4. Scheduling employees and trucks together 2. Robust truck scheduling ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling Truck scheduling Robust truck scheduling Employee timetabling & rostering Trucks + employees together

9. P RESENTATION O UTLINE Context Truck scheduling How to schedule the trucks? Robust truck scheduling How to manage delayed trucks without disturbing other ongoing operations? Employee timetabling & rostering How to schedule the workers in a way that fits the operations workload? Trucks + employees together How to combine the two models? Conclusion & perspectives Anne-Laure Ladier | PhD defense | 21st of November 2014 9 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

10. C ROSSDOCK T RUCK S CHEDULING How to schedule the trucks? 10

11. S CHEDULING PROBLEM  Reservation system  Minimize  Quantity put in storage  Dissatisfaction of the transportation providers 10am-12am 6am-8am 9am-12am 6am-7am 6am-9am 11am-12am 7am-10am 11 Anne-Laure Ladier | PhD defense | 21st of November 2014 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

12. I NPUT DATA  Number of inbound and outbound doors  Internal capacity  Number of clients  Number of inbound and outbound trucks  Clients of the outbound trucks  Number of pallets per client in each inbound truck  Outbound trucks capacity  Possible presence slots per truck, and associated penalties M ∞ 10am-12am 6am-8am 9am-12am 6am-7am 6am-9am 11am-12am 7am-10am 12 Anne-Laure Ladier | PhD defense | 21st of November 2014 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

13. D ECISIONS VARIABLES  Number of units moving at each time period:  from each inbound truck to each outbound truck  from each inbound truck to storage  from storage to each outbound truck  Time windows chosen for the trucks 13 Anne-Laure Ladier | PhD defense | 21st of November 2014 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

14. I NTEGER P ROGRAMMING MODEL (IP*) min (  × penalty on the inbound time window chosen +  × penalty on the outbound time window chosen +  × number of pallets put in storage) # trucks present ≤ # doors Pallets move from the present trucks onlyFlow conservation (for each destination) Outbound truck leave when fully loadedEach truck is assigned to exactly 1 time windowStock conservation rule 14 Anne-Laure Ladier | PhD defense | 21st of November 2014 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

15. C OMPUTATIONAL LIMITS Anne-Laure Ladier | PhD defense | 21st of November 2014 15 NP-hard (reduction with the 3-partition problem) ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling 6 doors 8 doors

16. Random selection of in/outbound schedules Maximum flow network problem H3 Tabu search: select a schedule in the neighborhood of the current one (change the time window of one truck) Objective: find the pallet moves that minimize total storage D ECOMPOSITION H EURISTICS Anne-Laure Ladier | PhD defense | 21st of November 2014 16 (IP1) to fix the inbound trucks schedule (IP*) on the restricted problem H1 (IP2) to fix the outbound trucks schedule (IP*) on the restricted problem H2 Objective: minimize difference between inbound pallet supply and outbound pallet demand (synchronize inbound and outbound) Same objective function than (IP*) Objective: minimize the outbound transport providers' dissatisfaction. Same objective function than (IP*) ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

17. H EURISTIC R ESULTS Anne-Laure Ladier | PhD defense | 21st of November 2014 17 R=0.4 / Average on 10 instances ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

18. T RUCK S CHEDULING : C ONCLUSION How to schedule the trucks? Anne-Laure Ladier | PhD defense | 21st of November 2014 18 IP*H1H2H3 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

19. R OBUST T RUCK S CHEDULING How to manage delayed trucks without disturbing other ongoing operations? 19

20. R OBUSTNESS ASSESSMENT METHODOLOGY  Discrete events simulation  Simulate complex stochastic processes  Add logic to react in unplanned situations  Gather data over multiple runs Software: FlexSim © www.flexsim.com Anne-Laure Ladier | PhD defense | 21st of November 2014 20 Exchange program funded by SimulationOptimization ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

21. FlexSim © simulation model S IMULATION MODEL Run the schedule + add random events Trucks arrival time Pallet transfer time Unloading time Ex: 20% of trucks are late  exponential distribution,  =10 min Ex: triangular distribution c v =0.1 min 21 IP*H1H2H3 Anne-Laure Ladier | PhD defense | 21st of November 2014 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

22. M EASURE R OBUSTNESS Measurement indicators Total number of pallets in stock Deviation in docking time inbound Deviation in docking time outbound Deviation in staying time inbound Deviation in staying time outbound Tolerance  1 pallet 5 min 20 min % off- limits (20 replications, 21 instances) Deterministic value  % off-limits 22 Anne-Laure Ladier | PhD defense | 21st of November 2014 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

23. R OBUSTNESS METRICS 23 Anne-Laure Ladier | PhD defense | 21st of November 2014 Trucks arrival time Pallet transfer time Unloading time Tolerance  (in min) to get 10% off-limits when 20% of the trucks are late, with a mean delay  =10 min Tolerance  (in min) to get 10% off-limits when the transfer time varies with Tolerance  (in min) to get 10% off-limits when the unloading time varies with ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

24. R ESULTS / T RUCK ARRIVAL TIME  24 Anne-Laure Ladier | PhD defense | 21st of November 2014 Trucks arriving early or late Following an exponential distribution with parameter  Trucks arrival time ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

25. R ESULTS / T RUCK ARRIVAL TIME  Trucks arriving early or late Following an exponential distribution with parameter  25 Tolerance  (in minutes) to get 10% off-limits Anne-Laure Ladier | PhD defense | 21st of November 2014  Trucks arrival time ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling Tolerance  to get 10% off-limits when 20% of the trucks are late, with a mean delay  =10 min Robustness improvement

26. P ROPAGATION OF T RUCK D ELAYS Some schedules obtained with the IP models generate more delay propagation than others 26 Anne-Laure Ladier | PhD defense | 21st of November 2014 Non-critical trucksCritical trucks ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling SimulationOptimization

27. R OBUST VERSIONS OF THE M ODELS ? Tabu search Solving methods Minimax R1 Min objective in the worst case Robust optimization Generic approach Robust project scheduling Specific approach Critical tasks  Critical trucks Min R2 Min expected regret … 27 Anne-Laure Ladier | PhD defense | 21st of November 2014 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

28. Min buffer lengths standard deviations T3 R OBUST VERSIONS OF THE M ODELS ? Tabu search IP or H1/H2 Post-treatment of IP or H1/H2 result Solving methods Min average nb trucks at the doors D1 Min nb of doors used every hour D2 Min nb critical trucks D3 Insert buffers of equal length T1 Insert buffers of length prop. to nb successors T2 Max buffer lengths weighted sum T4 Robust project scheduling Specific approach Critical tasks  Critical trucks Resource redundancy Doors Time redundancy Buffer time Tabu search Solving methods 28 Anne-Laure Ladier | PhD defense | 21st of November 2014 1.Solve IP* 2.Run IP* again to obtain a solution with the same objective values but less critical trucks ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

29. R OBUST VERSIONS OF THE M ODELS ? Anne-Laure Ladier | PhD defense | 21st of November 2014 29 Minimax R1 Min objective in the worst case Robust optimization Generic approach Robust project scheduling Specific approach Critical tasks  Critical trucks Min R2 Min expected regret … Min average nb trucks at the doors D1 Min nb of doors used every hour D2 Min nb critical trucks D3 Insert buffers of equal length T1 Insert buffers of length prop. to nb successors T2 Min buffer lengths standard deviations T3 Max buffer lengths weighted sum T4 Resource redundancy Doors Time redundancy Buffer time Tabu search IP or H1/H2 Post-treatment of IP or H1/H2 result Solving methods ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

30. R ESULTS / T RUCK ARRIVAL TIME Anne-Laure Ladier | PhD defense | 21st of November 2014 30  Tolerance  (in minutes) to get 10% off-limits ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling Robustness improvement Improvement compared to IP* or H2

31. R OBUST T RUCK S CHEDULING : R ESULTS Anne-Laure Ladier | PhD defense | 21st of November 2014 31 Robust optimization Resource redundancy Time redundancy Insert buffers of equal length Insert buffers of length prop. to nb successors Min buffer lengths standard deviations Min average nb trucks at the doors Min nb critical trucks ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

32. R OBUST T RUCK S CHEDULING : C ONCLUSION How to manage delayed trucks without disturbing other ongoing operations? Anne-Laure Ladier | PhD defense | 21st of November 2014 32 IP*H1H2H3 Robust versions ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

33. E MPLOYEE TIMETABLING & ROSTERING How to schedule the workers in a way that fits the operations workload? 33

34. O VERVIEW Anne-Laure Ladier | PhD defense | 21st of November 2014 34 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling Literature Industry Resource capacity Limited 24%100% Every week Every day

35. T IMETABLING & R OSTERING PROBLEM Anne-Laure Ladier | PhD defense | 21st of November 2014 35 How many hours should the employees work? Should we hire temporary workers? How long should each employee work on each task? ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

36. T IMETABLING IN THE L ITERATURE Aeronautic industry: crew rostering/scheduling Geographic dimension Health care: nurse rostering Simpler qualification profiles Permanence centered ≠ fluctuating demand Services: call centers Homogeneous workforce, no differences in qualifications Logistics Daily rostering problem for a real logistic platform Heuristics, local search Günther and Nissen (2010, 2014) 36 Anne-Laure Ladier | PhD defense | 21st of November 2014 Castillo-Salazar et al. (2012) Burke et al. (2004) Wan (2005), Bard et al. (2003) How to solve the weekly and daily problems including all logistic-specific constraints? ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

37. S EQUENTIAL S OLVING Anne-Laure Ladier | PhD defense | 21st of November 2014 37 Detailed task allocation Starting/ending time per employees 1 or 2 weeks ¼ hour Weekly timetabling Daily rostering Nb temporary workers Total nb hours worked Exact times Day Hour and shift Ben works 8 hours on Friday Ben works from 9h to 17h on Friday Ben unloads from 9h to 11h15, controls from 11h15 to 12h … MILP1 MILP2 MILP3 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

38. E XAMPLE OF MODEL : MILP1 min (  × temporary workers penalty +  × qualifications penalty +  × equity penalty +  × ergonomic penalty +  × unplanned absence penalty ) Match the workloadLegal constraints: min 4h/day, max 10h/day, 44h/weekTemporary workers work exactly 7h/dayHours of the fixed-contract employees Anne-Laure Ladier | PhD defense | 21st of November 2014 38 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

39. E MPLOYEE T IMETABLING : R ESULTS Anne-Laure Ladier | PhD defense | 21st of November 2014 39 Timetabling interface for teaching purposes Another interface was developped and implemented in industry ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

40. E MPLOYEE T IMETABLING : C ONCLUSION How to schedule the workers in a way that fits the operations workload? Anne-Laure Ladier | PhD defense | 21st of November 2014 40 MILP1 MILP2 MILP3 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

41. S CHEDULING T RUCKS & E MPLOYEES TOGETHER How to combine the two models? 41

42. O VERVIEW Anne-Laure Ladier | PhD defense | 21st of November 2014 42 IP*H1H2H3 MILP1 MILP2 MILP3 Robust versions ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

43. Linking constraints I TERATIVE A PPROACHES : I DEAS Anne-Laure Ladier | PhD defense | 21st of November 2014 43 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling Starting point?  Employees-first  Trucks-first Workload Capacity constraints

44. E MPLOYEES -F IRST I TERATIVE A PPROACH Anne-Laure Ladier | PhD defense | 21st of November 2014 44 Input data IP* H2 or MILP1 MILP2 MILP3 Workload Capacity constraints Announced timetable ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

45. T RUCKS -F IRST I TERATIVE A PPROACH Anne-Laure Ladier | PhD defense | 21st of November 2014 45 Input data IP*H2 or MILP1 MILP2 MILP3 IP*H2 or Workload Capacity constraints Announced timetable Workload ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

46. T RUCKS +E MPLOYEES S CHEDULING : R ESULTS Anne-Laure Ladier | PhD defense | 21st of November 2014 46 ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

47. T RUCKS +E MPLOYEES S CHEDULING : C ONCLUSION Anne-Laure Ladier | PhD defense | 21st of November 2014 47 IP*H1H2H3 MILP1 MILP2 MILP3 Robust versions How to combine the two models? ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

48. C ONCLUSION AND P ERSPECTIVES 48

49. C ONTRIBUTIONS Anne-Laure Ladier | PhD defense | 21st of November 2014 49 Identify the optimization issues relevant for today’s cross-docking industry Business-specific model Solving Validation Deal in an integrated manner with the key operational decisions Integrate uncertainty to validate the model suitability to business Literature review Platform visits Gaps analysis Decomposition strategies to solve NP-hard problems Robustness evaluation using simulation + robust models ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

50. P ERSPECTIVES Anne-Laure Ladier | PhD defense | 21st of November 2014 50 IP*H1H2H3 MILP1 MILP2 MILP3 Robust versions ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

51. P ERSPECTIVES Anne-Laure Ladier | PhD defense | 21st of November 2014 51 IP*H1H2H3 Robust versions Rolling horizon heuristic Robustness to changes in the truck content Combine approaches: Decomposition+simulation H2 +rolling horizon… Vary ratio R to study the effects of congestion ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

52. P ERSPECTIVES Anne-Laure Ladier | PhD defense | 21st of November 2014 52 MILP1 MILP2 MILP3 Machine learning methods to tune the weights Weighted multi-objective: Generate many solutions of the Pareto front (simulated annealing) and let the manager choose ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

53. P ERSPECTIVES Anne-Laure Ladier | PhD defense | 21st of November 2014 53 IP*H1H2H3 MILP1 MILP2 MILP3 Robust versions Exact decomposition methods Guyon et al. (2012) ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

54. P ERSPECTIVES & A PPLICATIONS Anne-Laure Ladier | PhD defense | 21st of November 2014 54 ©SAP-Vuzix http://youtu.be/9Wv9k_ssLcIhttp://youtu.be/9Wv9k_ssLcI Crossdock online scheduling for real-time operations management ©SAP-Vuzix Urban distribution: create pooling platforms for the last-kilometer ©DHL.com π-crossdock: very fast transshipment for a new distribution system Physical Internet manifesto, B. Montreuil ContextTruck schedulingEmployee timetablingEmployees+trucks Conclusion Robust truck scheduling

55. T HANK YOU ! M ERCI !

56. Thèse présentée par Anne-Laure Ladier et dirigée par Gülgün Alpan Soutenue publiquement le vendredi 21 novembre 2014 Devant un jury composé de: Mme Luce BROTCORNE Chargée de Recherche, HDR, INRIA Lille Nord EuropeRapporteur M. Pierre BAPTISTE Professeur, École Polytechnique de MontréalRapporteur M. Damien TRENTESAUX Professeur, Université de Valenciennes et du Hainaut-CambrésisExaminateur M. Allen G. GREENWOOD Professeur, Mississippi State UniversityExaminateur Mme Gülgün ALPAN Maître de Conférences, HDR, Grenoble INPDirectrice de thèse P LANIFICATION DES OPÉRATIONS DE C ROSS -D OCKING Prise en compte des incertitudes et de la capacité des ressources internes

57. R EFERENCES  Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2012). A survey on workforce scheduling and routing problems. In International Conference on the Practice and Theory of Automated Timetabling (pp. 283–302). Son, Norway.  Burke, E. K., De Causmaecker, P., Vanden Berghe, G., & Van Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7(6), 441–499.  Wan, L. (2005). Staff planning and scheduling in the service industry: an application to US Postal Service mail processing and distribution centers. University of Texas.  Bard, J. F., Binici, C., & DeSilva, A. H. (2003). Staff scheduling at the United States Postal Service. Computers & Operations Research, 30(5), 745–771. doi:10.1016/S0305-0548(02)00048-5  Günther, M., & Nissen, V. (2014). A comparison of three heuristics on a practical case of sub-daily staff scheduling. Annals of Operations Research, 218(1), 201–219.  Günther, M., & Nissen, V. (2010). Sub-daily staff scheduling for a logistics service provider. Künstliche Intelligenz, 24(2), 105–113. doi:10.1007/s13218-010-0023-5  Guyon, O., Lemaire, P., Pinson, É., & Rivreau, D. (2010). Cut generation for an integrated employee timetabling and production scheduling problem. European Journal of Operational Research, 201(2), 557–567. doi:10.1016/j.ejor.2009.03.013 Anne-Laure Ladier | PhD defense | 21st of November 2014 57