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]

Schedule Reliability in Container Liner Shipping Using Copositive Cones 5

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]

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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]

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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.

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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?

Schedule Reliability in Container Liner Shipping Using Copositive Cones 10

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

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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 }

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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]

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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

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Comparison of Schedules

< 20s CPU time

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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

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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!