1. DECISION SUPPORT SYSTEM FOR REAL-TIME URBAN FREIGHT MANAGEMENT Hanna Grzybowska hanna.grzybowska@upc.edu and Jaume Barceló jaume.barcelo@upc.edu Dept. of Statistics and Operations Research CENIT (Center for Innovation in Transport) www.cenit.es Universitat Politècnica de Catalunya

2. CONCEPTUAL APPROACHES TO REAL TIME FLEET MANAGEMENT AND THE ROLE IF ICT TECHNOLOGIES 2 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

3. Supplier Supplier’s Supplier Manufacturer Wholesaler/Distributor Retailer CITY AREA - client CITY LOGISTICS SCENARIO 3 Fleet management in urban areas has to explicitly account for the dynamics of traffic conditions leading to congestions and variability in travel times affecting the distribution of goods and the provision of services City Logistics activities are impacted by traffic congestion  must consider time-varying traffic congestion and operational constraints in routing and logistics optimization models Last-mile logistics Decisions must take into account all factors conditioning the problem  Decision Support System (DSS) The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

4. 4 GPS Satellites Vehicle’s Position Vehicle Vehicle’s Data Updated Route Cellular Antenna Vehicle’s Data Global Positioning System (GPS) GPS device pickups signal from satellites GPS device calculates position Establish communication with network Vehicle Data is sent to Fleet Management Center. Fleet Manager updates routes and returns them to vehicle. Fleet management Centre ICT TECHNOLOGIES AND REAL-TIME FLEET MANAGEMENT The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

5. BARCELONA’S TRAFFIC INFORMATION SYSTEM 5 Current and Short-Term Forecasted Travel Time The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

6. 6 Initial Demand and Fleet Specifications ROUTING AND SCHEDULING MODULE Initial Operational Plan DYNAMIC ROUTER AND SCHEDULER Dynamic Operational Plan CONCEPTUAL SCHEME FOR REAL-TIME FLEET MANAGEMENT SYSTEMS (Adapted from Regan, Jaillet, Mahmassani) Known/predicted demand for service Known/predicted driver/vehicle availability Load Acceptance Policies Pool of accepted demands REAL-TIME INFORMATION New demands  Unsatisfied demands  Traffic conditions  Fleet availability ASSUMING: A given Initial Operational Plan Fleets equipped with AVL tecnologies (i.e. GPS+GPRS) A Real-Time Information System OBJECTIVE: Design a DSS for Dynamic Routing and Scheduling The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

7. New Customer Request Cancelation of Service Arrival of Vehicle at Location Changes in Travel Times Delays in Delivery Start Times Breakdown of Vehicle Changes in Time Windows Bounds DYNAMIC ROUTER AND SCHEDULER Insertion Heuristics Local Search Operators TABU SEARCH Reactive Strategies One-by-one Pooling Preventive Strategies Vehicle Relocation Waiting Strategies Drive-First Wait-First Combined DYNAMIC EVENT AND VEHICLE TRACKING SIMULATOR NEW ROUTING PLAN EXTERNAL EVENTS INTERNAL EVENTS INTERNAL EVENTS DYNAMIC MONITORING SOLVING STRATEGIES PROPOSED DECISION SUPPORT SYSTEM FOR REAL-TIME FLEET MANAGEMENT The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

8. DYNAMIC ROUTER AND SCHEDULER Decide where to insert the new customer. New customer arrives at time t > 0. Fleet vehicles can be in one of the three status: –In service at some customer i (SER). –Moving to the next planned customer on the route or waiting at the customer location to start service within the time window. (MOV). –Idle at the Depot, without a previously assigned route (IDL). –Waiting at the client’s i (WAIT) This status determines when a vehicle should be diverted from its current route, be assigned to a new one if is idle or keep the planned trip. Whenever a new customer arrives, the status of a vehicle must be known to compute travel times for this new customer. If the vehicle has a MOV status, the travel time is computed from the current position of the vehicle to the location of the new customer. If the vehicle has IDL status, the travel time is just the travel time from the depot to the new customer. If the vehicle is has SER status, the amount of time needed to arrive to the customer is the remaining service time at the current customer plus the travel time between the current customer and the new customer. If the vehicle has WAIT status it can be send to other client. The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

9. 1.Initial fleet scheduling 2.Start services while tracking vehicles 3.New client call is received at time t 4.Check vehicle positions and current travel times 5.Reject unfeasible routes (insertion / diversion) 6.Recalculate routes. 7.Execute new plan. In Transit In Service New Client DYNAMIC ROUTING AND SCHEDULING: Example of Dynamic Insertion Heuristic The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

10. DYNAMIC REROUTING WITH REAL-TIME INFORMATION (ADJUSTMENT SOF ROUTE 2 - GREEN AND 3 - BLUE ) 10 section removed from the original route new section of modified original route new client request registered at time t initially known client already visited by the assigned vehicle (before time instant t) initially known client still not visited by the assigned vehicle (before time instant t) on of the fleet vehicle serving route 2 at time t The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

11. AVERAGE LINK TRAVEL TIMES AND VARIANCES (Barcelona “Eixample”-CBD) 11 Working exclusively with average travel times may lead to significant deviations in city logistics problems where temporality is an important factor such as the VRPTW. A report on urban distribution in Barcelona (Robusté, 2005) found that: There were more than 62,000 commercial outlets  more than 60,000 daily unloading operations Service time between 13 and 16 minutes 50% of deliveries were made by 11:00 hrs The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

12. DEALING WITH TIME-DEPENDENT TRAVEL TIMES d i = departure time from client i s i = sevice time for client i T ij (d i ) = travel time from i to j when departing at time d i from client i T ij (d i )  T ij (d i ’) 12 Ad Hoc version of the algorithm of Ziliaskopoulos and Mahmassani Calculates the time-dependent shortest paths from all nodes of to one, specified as destination point. The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

13. REAL-TIME TRAFFIC INFORMATION SYSTEM REAL-TIME FLEET MONITORING SYSTEM The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

14. FRAMEWORK 14 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

15. TIME-DEPENDENT TRAVEL TIMES DATA FORECASTING MODULE MULTIPLE SIMULATION ITERATIONS “future” travel times actual travel times historical travel times data base SINGLE SIMULATION ITERATION t0t0 TRAFFIC SIMULATOR cjH(t0)cjH(t0) c j H (t 0 +1) cjH(t0+T)cjH(t0+T) … cjP(t0)cjP(t0)c j P (t 0 +1)cjP(t0+T)cjP(t0+T) … t0t0 Historical Data Base Actual Travel Times + Expected Travel Times ATIS 15 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

16. Time-Dependent Routing Plan: sequences of clients to visit Time-Dependent Shortest Paths: sequences of nodes between the clients for the vehicle to go through Present Travel Times Data Base Vehicle Fleet Performance Simulator Vehicles’ current: status position load List of clients: served omitted INPUT OUTPUT e.g.: instant of appearance of new event Depending on the current vehicle’s status, the value of time left to complete: service waiting trip on the current arc EXTERNAL TRIGGER INTERNAL EVENT e.g.: arrival to a client whose TW is closed end of providing a client with service VEHICLE FLEET PERFORMANCE SIMULATOR 16 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

17. MATHEMATICAL FORMULATION OF THE VEHICLE ROUTING PROBLEM WITH PICKUP AND DELIVERIES AND TIME WINDOWS Subject to : Each customer is served by the same vehicle Time windows feasibility Precedence requirement Route for vehicle k, from o(k) to d(k) Route and vehicle loads requirements 17 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

18. PICK UP & DELIVERY PROBLEMS WITH TIME WINDOWS 18 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

19. COMPOSITE HEURISTICS TO CONTRUCT THE INITIAL AND THE DYNAMIC ROUTING AND SCHEDULING 19 ALGORITHM PROVIDING THE INITIAL SOLUTION Based on the Simple Pairing Approach Based on the Sweep Algorithm Based on Customers’ Aggregation Areas PARALLEL TABU SEARCH PROCEDURE USING SIMULTANEOUSLY TWO LOCAL SEARCH HEURISTICS Pickup and Delivery Pair Shift Operator Pickup and Delivery Pair Exchange Operator POST-OPTIMIZATION Pickup and Delivery Pair Rearrange Operator 2-opt procedure The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

20. P2 D1 P6 D6 DEPOT D7 P7 P3 D3 P5 D5 D4 D2 P4 P1 NEIGHBOURHOOD 1NEIGHBOURHOOD 2 NEIGHBOURHOOD 3 20 Algorithm Initial Solution 1.Order known clients (Client’s Sorting Pre-Processing Algorithm using the Definitions of Customers’ Aggregation Areas 2.Create initial solution Select the farthest client in the listing Find its PD partner and delete them both from the listing IF it is the first iteration OR it is not possible to insert the pair into existing route THEN create route: depot-pickup customer-delivery customer-depot ELSE insert the pair in location causing minimal increment of the cost of the existing route The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

21. PARALLEL TABU SEARCH Works with two concurrent different search processes: The Pickup and Delivery Pair Shift Operator (NPDPSO) The Pickup and Delivery Pair Exchange Operator (NPDPEO). 21 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

22. POST-OPTIMIZATION PROCESS Post-optimization is realised by: Pickup and Delivery Pair Shift Operator 22 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

23. COMPUTATIONAL EXPERIMENTS AND RESULTS 23 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

24. THE SELECTED SCENARIO: Barcelona’s CBD 24 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

25. KTS seminar at Linkoping University, Norrkoping 16.03.2011 Clients Depot Downtown area of Barcelona. Commercial activities and tourism. It covers 747 hectares 1,570 links y 721 nodes. THE SIMULATION MODEL 25 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

26. 100 Customers: Constant demand and service time. Time windows 1 Depot; Opening hours 08:00 – 16:00 Fleet: 8 homogeneous vehicles with large capacity. Vehicles are equipped with GPS and real-time communication system with fleet manager. Real-time traffic information system. Simulation time: 10 hours (07:00 – 17:00) MODELING ASSUMPTIONS 26 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

27. Table 1. Collection of the DSS performance testing scenarios TESTING SCENARIOS ScenarioN o Initially Known ClientsTDSP Calculator TriggeredTraffic InformationUsed Module 1100once at the startaverage staticIRSM 2100at the start and after multiple timestime-dependantIRSM+DRSM with CRRM 380at the start and after multiple timestime-dependantIRSM+DRSM with CRRM Initial solutionFinal solution Scenario Ini N o r Total Travel Time [s] Total Waiting Time [s] Solution Cost [s] Fin N o r Total Travel Time [s] Total Waiting Time [s] Solution Cost [s] Service Level 1520602216695222717.0521320717189230396100% 2314914410372159516.0315582510449166274100% 3313602810393146420.0418824810254198501100% Table 2. Results on fleet performance depending on the traffic information input 27 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

28. TESTING SCENARIOS, USING SPI 28 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

29. TESTING SCENARIOS USING CSR 29 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

30. SERVICE LEVELS IN SCENARIOS WITH DYNAMIC CLIENTS 30 N o stat. 100 80 60 50 40 20 cust. (%) service level The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

31. TOTAL TRAVEL TIMES IN SCENARIOS WITH DYNAMIC CLIENTS 31 total travel time [s] N o stat. 100 80 60 50 40 20 cust. (%) The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

32. CONCLUSIONS The obtained results prove that the performance of the fleet strongly depends on the traffic information used to create and update the routing and scheduling plan. The usage of the time-dependent shortest paths, computed whenever a new even occurs, brings better results than when the average travel times’ estimates are employed, even in the case when not all the information on the clients to be served is initially available. The comparison of the initially planned and performed routing and scheduling plan indicates that the total travel time, total waiting time and the solution cost are always higher when executed. It is due to the fact that the plan does not take into consideration the most recent and forecasted changes in the traffic flow. No customer is left unserved. 32 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

33. 33 THANK YOU VERY MUCH FOR YOUR ATTENTION