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Delivery Schedule Reliability in Container Liner Shipping
Chung-Piaw TeoNUS Business School, National University of Singapore
Joint work withJasmine, Siu Lee Lam
School of Civil and Environmental Engineering, Nanyang Technological University
Abraham Zhang Waikato Management School, University of Waikato
Zhichao ZhengLee Kong Chuan School of Business, Singapore Management University
Vessel
Farm/Factory
Introduction – Liner Shipping
• Economic contribution– 90% of international trade takes place by sea, and the liner
shipping industry is responsible for 60% of them by value (IHS Global Insight 2009)
Introduction – Schedule Unreliability
• Schedule reliability– From December 2005 to June 2010, average schedule
delay (more than one day) ranged from 32% to 54% (Drewry 2010)
– 50% to 70% schedule reliability• Impacts on supply chain
– Difficulties in resourcecoordination
– Increase in safety stocks– Impossible to implement
just-in-time/lean strategies
Vessel
Farm/Factory
Schedule Reliability in Container Liner Shipping Using Copositive Cones 4
Introduction – New Initiatives
• Daily Maersk
14 July 2014 @ IFORS
[Figure from Maersk Line website on 12 Jul 2014]
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Introduction – New Initiatives
• Daily Maersk– 98% reliability in the first year– Six months after, its market share of Asia-Europe trade
increased from 21% to 25% (Leach 2012)– “Reliability is the new rate war; we need an end-to-end
view on reliability” – Eivind Kolding (former CEO of Maersk Line, 2011)
[Figure from Maersk Line website on 12 Jul 2014]
Schedule Reliability in Container Liner Shipping Using Copositive Cones 6
Introduction – New Initiatives
• Slow steaming since 2007– Fuel (bunker) cost– Now becomes the new standard
(fully adopted by 2010)– Confounding effects on schedule
reliability• More vulnerable to uncertainties• Ability to speed up to recover delays• Willingness to speed up?
– New generation of vessels: designed for slow steaming• Maersk Triple E class
[“Slow Steaming: The Full Story” by Maersk Line]
Schedule Reliability in Container Liner Shipping Using Copositive Cones 7
Introduction – Schedule Reliability
• Strategic value of schedule reliability is now widely recognized, but– Industry average in Q1 2014 is still 70.0% …
• Why?– Challenges from uncertainties
• Extreme weather conditions• Current and tides• Availability of empty containers• Port congestion (propagation effect)• Etc.
Schedule Reliability in Container Liner Shipping Using Copositive Cones 8
Introduction – Research Questions
1. Given a service route with fixed journey time and default sailing speeds, how to schedule port arrival and departure times to maximize schedule reliability?
2. How do the total journey time, default sailing speed, and sailing frequency affect schedule reliability?
3. What are the cost implications of improving scheduling reliability?
Schedule Reliability in Container Liner Shipping 9
How is the schedule derived?
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Literature
• Impact of schedule reliability on shippers– Notteboom (2006), Vernimmen et al. (2007), Lam et al.
(2011), Zhang & Lam (2013), etc.• Influential factors for schedule reliability
– Notteboom (2006), Vernimmen et al. (2007), Sözer and Dogan (2007), Chung & Chiang (2011),etc.
• Schedule design– Focus on cost minimizing (fuel cost, operating costs)– Stochastic programming– Christiansen et al. (2004), Qi & Song (2012), Wang & Meng
(2012a, 2012b), etc.
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Basic Model – Assumptions
• A vessel is always on time to start a round-trip voyage from its home port– Practice: sufficient buffer times between two consecutive round-trip
voyages• A vessel maintains a constant sailing speed at sea or be still
– Relaxed in model extensions• A port will not service a vessel until its scheduled arrival time
– Terminal handling capacity is a bottleneck in liner shipping• Each port of call is scheduled to be visited during the same
time window every week– Analysis on increasing sailing frequency
Schedule Reliability in Container Liner Shipping Using Copositive Cones 12
Basic Model – Notation
• : total number of port calls (in sequence)– Home port: port = port
• leg: voyage from the to port• : planned round-trip journey time
– No. of weeks = no. of vessels• : stochastic service time at the port
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Basic Model – Notation (Cont.)
• : sailing speed on the leg• : sailing distance of the leg• : random noise in the sailing time on the leg
– Extreme weather conditions• : actual sailing time on the leg
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Basic Model – Notation (Cont.)
• : scheduled arrival time at the port– : scheduled arrival time interval between the and port
• : actual arrival time at the port
14 July 2014 @ IFORS
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Basic Model – Notation (Cont.)
• : difference between the actual and scheduled interval time between the and port
• : arrival delay time at the port
–
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Basic Model – Objective
• : weight or the unit delay time cost at the port• Objective: minimize
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Cost Computation – Network Flow
14 July 2014 @ IFORS
~𝑑2=max {0 ,~𝑐1 }~
𝑑3=max {0 ,~𝑐2 ,~𝑐2+~𝑐1 }
Schedule Reliability in Container Liner Shipping Using Copositive Cones 18
Worst-Case Expected Cost
• •
• Theory based on Natarajan et al. (2011) and Kong et al. (2013)
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Model Extensions
• Variable speed – bunker cost– Dynamic
• Speed up if there was a delay at previous port• Different speeds for different scenarios
– Static• Optimize speeds at different legs
• Extreme weather conditions– Separate the scenarios with extreme weather
– Conditional moments and multiple cones
Schedule Reliability in Container Liner Shipping Using Copositive Cones 20
Numerical Studies – Case Analysis
• Maersk Line AE2 service (Daily Maersk)– ports, weeks– Head-haul (westbound) speed = 22.0 knots
Westbound
[Figure from Maersk Line website on 12 Jul 2014]
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Numerical Studies – Case Analysis
• Maersk Line AE2 service (Daily Maersk)– ports, weeks– Head-haul (westbound) speed = 22.0 knots– Back-haul (eastbound) speed = 19.0 knots– Vessel capacity = 6600 TEU
Eastbound
[Figure from Maersk Line website on 12 Jul 2014]
Schedule Reliability in Container Liner Shipping Using Copositive Cones 22
Numerical Studies – Data
• Data source: Maersk Line (2013), SeaRates.com (2013), portworld.com (2013)
• Port time includes pilotage in/out, berthing, cargo handling, etc.
• Extreme weathers may cause up to 24 hours delay in the South China Sea, the Indian Ocean and the English Channel
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Numerical Studies – Data
• 1251 historical data points
Schedule Reliability in Container Liner Shipping Using Copositive Cones 24
Comparison of Schedules
< 20s CPU time
Schedule Reliability in Container Liner Shipping Using Copositive Cones 25
Performance of COP Schedule
• > 97% on-time probability under four common distributions• Practice: 98% (Daily Maersk ports)
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Performance Comparison
Port/Sea Time Distribution Pattern Uniform Normal Gamma Two-point
COP Schedule
Vessel Reliability (Avg) 98.0% 98.0% 97.8% 95.9%
- Rotterdam 98.0% 97.6% 97.2% 93.8%
- Bremerhaven 100.0% 100.0% 100.0% 100.0%
Bunker (tons) 8,327 8,327 8,327 8,327
Maersk AE2 Schedule
Vessel Reliability (Avg) 97.4% 97.4% 97.2% 92.2%
- Rotterdam 98.1% 97.6% 97.3% 93.7%
- Bremerhaven 100.0% 100.0% 100.0% 100.0%
Bunker (tons) 8,327 8,327 8,327 8,327
Schedule Reliability in Container Liner Shipping Using Copositive Cones 27
Impacts of Sailing Speeds
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Impacts of Total Journey Time
Total journey time (weeks)
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Concluding Remarks
• Copositive cone optimization model for schedule reliability problem
• Robust performance comparable to existing Daily Maersk schedules
• Cost-reliability trade-off analysis• Future work (on-going)
– Variable speeds– Changing sailing frequency– Cargo reliability (overbooking and transhipment)
Thank you!