1 / 33

DECISION SUPPORT SYSTEM FOR REAL-TIME URBAN FREIGHT MANAGEMENT

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.

tia
Download Presentation

DECISION SUPPORT SYSTEM FOR REAL-TIME URBAN FREIGHT MANAGEMENT

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  3. Supplier’s Supplier Supplier Manufacturer Wholesaler/Distributor Retailer • CITY AREA • - client CITY LOGISTICS SCENARIO 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 Supply chain 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. ICT TECHNOLOGIES AND REAL-TIME FLEET MANAGEMENT GPS Satellites • 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. Vehicle’s Position Vehicle’s Data Vehicle’s Data UpdatedRoute UpdatedRoute Vehicle Cellular Antenna Fleet management Centre The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

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

  6. ROUTING AND SCHEDULING MODULE Known/predicteddemandforservice Known/predicted driver/vehicleavailability Pool of accepteddemands Load AcceptancePolicies CONCEPTUAL SCHEME FOR REAL-TIME FLEET MANAGEMENT SYSTEMS (Adapted from Regan, Jaillet, Mahmassani) Initial Demand and Fleet Specifications • ASSUMING: • A givenInitialOperational Plan • Fleetsequippedwith AVL tecnologies (i.e. GPS+GPRS) • A Real-Time InformationSystem • OBJECTIVE: • Design a DSS forDynamicRouting and Scheduling InitialOperational Plan • REAL-TIME INFORMATION • New demands • Unsatisfieddemands • Trafficconditions • Fleetavailability DYNAMIC ROUTER AND SCHEDULER DynamicOperational Plan The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

  7. NEW ROUTING PLAN DYNAMIC ROUTER AND SCHEDULER Insertion Heuristics Local Search Operators TABU SEARCH PROPOSED DECISION SUPPORT SYSTEM FOR REAL-TIME FLEET MANAGEMENT DYNAMIC EVENT AND VEHICLE TRACKING SIMULATOR New Customer Request Cancelation of Service SOLVING STRATEGIES EXTERNAL EVENTS • Reactive Strategies • One-by-one • Pooling Changes in Time Windows Bounds Breakdown of Vehicle • Preventive Strategies • Vehicle Relocation Arrival of Vehicle at Location • Waiting Strategies • Drive-First • Wait-First • Combined Delays in Delivery Start Times INTERNAL EVENTS INTERNAL EVENTS DYNAMIC MONITORING Changes in Travel Times 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. DYNAMIC ROUTING AND SCHEDULING: Example of Dynamic Insertion Heuristic In Transit Initialfleetscheduling Startserviceswhile tracking vehicles New clientcallisreceived at time t Checkvehicle positions and currenttravel times Rejectunfeasibleroutes (insertion / diversion) Recalculateroutes. Execute new plan. In Service New Client In Transit In Service 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 ) 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) • 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 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. The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

  12. DEALING WITH TIME-DEPENDENT TRAVEL TIMES 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. • di = departure time fromclienti • si = sevice time forclienti • Tij(di) = travel time fromitojwhendeparting at time difromclienti • Tij(di)Tij(di’) 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 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

  15. Actual Travel Times + Expected Travel Times ATIS Historical Data Base actual travel times “future”travel times … cjP(t0) cjP(t0+1) cjP(t0+T) cjH(t0+1) cjH(t0+T) cjH(t0) … TIME-DEPENDENT TRAVEL TIMES DATA FORECASTING MODULE MULTIPLE SIMULATION ITERATIONS SINGLE SIMULATION ITERATION historical travel times data base TRAFFIC SIMULATOR t0 t0 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 EXTERNAL TRIGGER INPUT e.g.: instant of appearance of new event Vehicle Fleet Performance Simulator OUTPUT • Depending on the current vehicle’s status, the value of time left to complete: • service • waiting • trip on the current arc • List of clients: • served • omitted • Vehicles’ current: • status • position • load • INTERNAL EVENT e.g.: • arrival to a client whose TW is closed • end of providing a client with service VEHICLE FLEET PERFORMANCE SIMULATOR 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 Subjectto: Eachcustomerisservedbythesamevehicle Routeforvehiclek, fromo(k)tod(k) Time windowsfeasibility Precedencerequirement Route and vehicleloadsrequirements The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

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

  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 COMPOSITE HEURISTICS TO CONTRUCT THE INITIAL AND THE DYNAMIC ROUTING AND SCHEDULING The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

  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 P2 D3 P3 NEIGHBOURHOOD 1 NEIGHBOURHOOD 2 P6 D6 D2 D1 D4 NEIGHBOURHOOD 3 P4 P1 DEPOT D5 P7 P5 D7 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). 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 The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

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

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

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

  26. MODELING ASSUMPTIONS • 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) The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

  27. TESTING SCENARIOS Table 1. Collection of the DSS performance testing scenarios Table 2. Results on fleet performance depending on the traffic information input The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

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

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

  30. SERVICE LEVELS IN SCENARIOS WITH DYNAMIC CLIENTS service level No stat. 100 80 60 50 40 20 cust. (%) The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

  31. TOTAL TRAVEL TIMES IN SCENARIOS WITH DYNAMIC CLIENTS total travel time [s] No 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. The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

  33. THANK YOU VERY MUCH FOR YOUR ATTENTION

More Related