Passenger Rail Vehicle/Staff Deployment Optimization: Best Practices and Next Steps

Transcription

1 Passenger Rail Vehicle/Staff Deployment Optimization: Best Practices and Next Steps Robert Mulder President, IVU Traffic Technologies Inc San Francisco, CA June 22, 2016

2 IVU Traffic Technologies Company History 1976 IVU Founded 5 founders Berlin Roots in Operations Research 2000 Initial public offering 200 employees Germany Transportation planning software tools 2016 Customers ~ employees Worldwide Software and hardware Standard products Optimization 2

3 IVU Traffic Technologies AG Represented Worldwide Aachen Berlin Birmingham UK Bogotá Budapest Frankfurt Hanoi Ho Chi Minh City Montreal Paris 3 Rome Santiago de Chile San Francisco Tel Aviv Veenendaal

4 The IVU.rail for the full range of operational tasks A common database for all modules Continuous flow of information between the modules Process support via integrated optimization components (OPTI) 4

5 What is Optimization? Optimization means: Improving a current situation Getting to the optimum Optimization in Rail: Setting rules for a problem solution (e.g. duty length, maintenance rules) Defining an optimization goal (e.g. saving vehicles, increase robustness) Obtaining a solution which obeys the rules is the optimum can be taken in production 5

6 Optimization is defined by you Permissibility (legal regulations, fare rules) Social acceptability (duty lengths, break times, happiness of employees) Optimal results Costs (effective usage, resource conservation) Operational Stability (distribution, uniformity) 6

7 For a range of scenarios, optimization tools will result in savings for a typical rail road Scenario optimize annual crew schedule for on train staff optimize annual equipment schedule incl. consideration of maintenance capacities optimize assignment of duties and off days sequences to employees simulation of what if scenarios: new bids, negotiation of labor rules, depot changes Benefit Save 5%-15% of paid time for complex scenarios reduction of downtimes leads to overall savings on rolling stock save overtime by balanced accounts + increase fairness solid decision support during negotiations and for strategic issues 7

8 Example: Optimization tools can generates optimized, rule-compliant equipment schedules vehicle types and costs rule-compliant vehicle cycles vehicle cycle rules (turnaround times, service times) required passenger capacity maintenance rules and capacities layover capacities OPTI compliant train formations services (maintenance, cleaning, refuel) uniformity non-revenue trips 8

9 Crew Schedule Optimization Goal: Crew scheduling serving all equipment schedules Complex rules: Duty type rules Duty type mix Restrict line changes Feature: Qualifications Stress routes 9

10 Adjustment Optimization Goal: Small changes in the train schedule small changes in the crew schedule Train schedule changes due to constructions Extra trips for sport events or schools Friday timetable with small differences from Monday-Thursday timetable Rules: Keep crew schedule content similar, only add extra trips to existing duties Keep duty frames similar, only adjust start and end time within a limit Feature: Set a bonus for every duty that the optimization keeps equal or similar 10

11 Job Optimization Goal: Job creation for a specific time period Rules: Fatigue and hours of service rules Soft and hard preference rules Feature: Profiles for different objectives, e.g. stability vs. lowest costs Check and verify intermediate solution in every moment Currently being implemented at VIA Rail in Montreal 11

12 Optimization Experience: Trenitalia Italy s largest Railroad regional commuter/freight/long-distance 8,000 trains per day with 14,000 employees Old System: Manual = each region planned vehicle and staff dispatch locally Redundant systems in place connected with proprietary interfaces Inefficient and unproductive New System: Standard software for all three rail types and specific rules for each Single database with use of standard interfaces Company work rules and legal requirements part of planning process Optimization brings great improvements: More reliable duties/jobs optimized by system A new level of transparency and flexibility in duty planning scheduling and optimization Trenitalia Project Director 12

13 Optimization Experience: Lessons Learned Optimization is incorruptible: Very often incorrect equipment schedules or crew schedules are discovered Optimization detects unwritten rules Optimization is complex: Setup of parameters, especially in bigger scenarios, is a complex task Profound knowledge of manual equipment and crew schedules is needed Defining the optimization goal often needs a compromise between diverging company interests Optimization is a great scheduling automatic: Calculated equipment and crew schedules are 100% correct according to rules Scheduling process is more efficient (= faster, more often) Quick calculation of different scenarios possible (e.g. for RFP s, labor negotiations) 13

14 Thank You!