P. 2 • IFORS NEWS December 2019
OR Impact
Articles demonstrating direct benefits from implementing OR studies
Automated Airport Staff Scheduling at Swissport
International Ltd.
Section Editors: Sue Merchant <suemerchant@hotmail.com>; John Ranyard <jranyard@cix.co.uk>
Prof. Dr. Andreas Klinkert <andreas.
klinkert@zhaw.ch>, Head OR/OM Group,
Technical Project Lead, Zurich University
of Applied Sciences (ZHAW)
Dr. Peter Fusek <peter.fusek@zhaw.
ch>, Lead Mathematical Modelling,
ZHAW
Bruno Riesen, Vice President Business
Support, Business Project Lead, Swissport
Roman Berner, Optimization Specialist, Swissport
Other Swissport Project Team members
Rita Thalmann, Lead Application Project
Simona Segessenmann, Application Specialist
Project Overview
Swissport International Ltd. is the world’s leading
provider of airport ground services and air cargo
handling, with 66,000 employees and a consolidated
operating revenue of 2.99 billion euros in 2018.
Swissport provided ground services for some 282
million passengers in 2018, and handled roughly 4.8
million tons of air freight in 115 cargo warehouses
worldwide. At the end of June 2019, Swissport was
active at 310 airports in 49 countries, on behalf of some
850 aviation customers.
Airport ground handling involves a broad range of
tasks, including passenger services like check-in, gate
handling and transfer, and ramp services like baggage
management, aircraft handling, and aircraft servicing
and cleaning. Swissport employs at its main airports
up to 2500 people with hundreds of dierent work
skills and shift duties, and a multitude of contract
types. Monthly sta planning is highly complex and
expensive, and usually requires extensive manual work
by specially trained personnel.
Swissport decided more than 10 years ago to start to
try to optimize these sophisticated planning processes
with the help of an appropriate software solution.
Evaluation of the commercially available sta rostering
tools showed that no software was able to satisfactorily
solve the complex large-scale planning problems at
Swissport.
Extracted from:
IFORS News
Volume 13, Number 4,
December 2019, ISSN 2223-4374
P. 3 • IFORS NEWS December 2019
Articles demonstrating direct benefits from implementing OR studies
In 2007, Swissport launched a strategic R&D cooperation
with the Zurich University of Applied Sciences Institute
of Data Analysis and Process Design (IDP), with the aim
of developing innovative high-performance software for
automated sta rostering that is capable of solving Swissport’s
challenging planning problems. In addition, the tool needed
to be suciently general and exible to be usable in other
companies and industries with complex rostering problems.
This collaboration, called Auto-Roster, started as a
Swiss national research project and has continued as
a strategic long-term cooperation between ZHAW and
Swissport, with an aim to continually adapt, extend,
improve and deploy the developed optimization tool.
Employee Scheduling
This generally comprises three main phases: (1) task
generation and shift construction, (2) rostering, (3) real-
time dispatching. Phase 1 includes demand modeling,
task generation, shift design, and demand covering.
Phase 2 is the main rostering phase where daily shifts
are assigned to individual employees. It consists of
the days-o planning and shift assignment tasks, and
generates a detailed monthly sta schedule which is
communicated to the employees several days before
execution. Phase 3 deals with the real-time planning
and control tasks on the day of operational execution.
The Auto-Roster project mainly focuses on Phase 2 which
represents the most complex, expensive and sensitive
planning task at Swissport. The amount of work involved in
rostering at Swissport can be illustrated with reference to
the initial situation at Zurich Airport, the pilot site for the
project. At project start, the rostering department comprised
20 planners who worked almost exclusively on rostering the
over 2000 employees at Zurich Airport which corresponds to
around 400 working days per monthly plan.
Major innovations and challenges for the Auto-Roster software
came from a dynamic, demand-driven planning policy which
does not rely on repetitive shift patterns rolled out over a long-
term horizon, and from a rostering approach which attributes
high importance to individual employee preferences. Human
planners typically achieved a 95% wish fulllment rate, and it
was imperative that an automated solution also met this rate.
Methodology
In contrast to most commercial rostering tools, which are
generally based on stochastic search metaheuristics, Auto-
Roster relies signicantly on Mixed Integer Linear Programming
(MIP), combined with various other optimization techniques,
including decomposition and relaxation, pre- and post-
processing, and a variety of heuristic procedures.
Approaching large-scale real-world rostering problems by MIP
techniques is innovative and challenging, since computation
times are typically far beyond any acceptable limits. Developing
“good” MIP formulations to reduce solver computation times
was one of the most important and challenging parts of
the Auto-Roster project, and required deep knowledge in
combinatorial optimization, polyhedral combinatorics, and
graph theory. The project was several times close to failing
due to intractable MIP models and could only be continued
thanks to mathematical breakthroughs leading to powerful
new MIP formulations
Other major challenges came from feasibility issues, since
most real-world planning instances at Swissport are infeasible
at rst. Finding and explaining infeasibilities is intrinsically
complex, and a variety of algorithmic approaches had to be
developed to master these issues.
The current Auto-Roster optimization engine (without
front- and back-end) comprises some 25,000 lines of
MIP code and 30,000 lines of Java code. The largest MIP
instances contain around 1 million integer variables and
0.5 million constraints, and can typically be solved within
20 - 70 hours with relative MIP optimality gap << 0.5%. The
relative MIP optimality gap corresponds to the percentage
deviation of the best objective function value found so far
from the best objective bound resulting from the Branch-
and-Cut MIP solver process. For an illustration, see Fig.1,
which shows the convergence of the decreasing objective
function curve (measured in penalty points associated to
the current solution) and the increasing objective lower
bound, together with the associated MIP gap.
P. 4 • IFORS NEWS December 2019
Implementation
The Auto-Roster software
is fully implemented and in
operational use at all three
major airports in Switzerland,
namely Zurich, Geneva
and Basel. Other airports
in Europe are currently
preparing for deployment,
and the roll-out is being
continuously expanded.
Zurich Airport is one of
Swissport’s most complex
airports in terms of sta
rostering, and operational
implementation of the software in the various planning units
took several years.
Currently, a total of 57 internal customers are using Auto-Roster.
Since planning units often have dierent policies regarding
their rostering processes, software roll-out is generally
challenging and laborious. A detailed understanding of the
business needs and a close cooperation with the involved
planners and employees is indispensable.
The preparatory work for the introduction of the Auto-Roster
software generally includes a comprehensive analysis and
cleanup of the existing planning processes and data situation.
Along with the implementation, all associated business
processes must be thoroughly redesigned or adapted, and the
personnel involved must be carefully re-trained.
Years of experience with the Auto-Roster deployment have
shown that, after a demanding transition period of a few
months, customers generally express great satisfaction and
trust in the software, fully align their planning processes with
it, and report on substantial operational and nancial benets.
Business Impact
Auto-Roster has become one of Swissport’s most important
operational planning instruments. At the strategic level, top
management decided that the software should be rolled
out further to other stations within Europe. In addition,
the process optimizations developed in connection with
Auto-Roster should be transferred to other stations as best
practices.
A recent comprehensive study of the personnel planning
processes at all Swissport stations in Switzerland, carried
out by an external consulting company, concluded that
Auto-Roster is of central importance for Swissport and has
a significant business impact. The study also emphasized
that the tool is indispensable in the current planning
environment, and there are hardly any alternative tools
with similar functionality, performance, and flexibility.
The Auto-Roster software generates considerable nancial
savings for Swissport. Part of the nancial impact can be
quantied by comparing the rostering department’s post-
implementation workload with the original workload.>>
Planning eort can often be reduced by up to half, and
associated personnel costs are reduced accordingly. At the
three Swissport stations in Switzerland, the annual savings
from rostering eort reduction alone amount to around $1
million.
Besides reduced planning
expenses, Auto-Roster
has numerous other
impacts that also bring
substantial operational
and nancial benets,
including the following:
• Shift scheme optimization
(phase 1), based on Auto-
Roster’s sophisticated rough-
cut planning capabilities,
eliminates wasted shift times
and reduces operational
personnel costs.
• New employment contract models can be evaluated and
optimized by means of rough-cut algorithms.
• Proactive optimization of employee and contract mix
improves alignment of demand and supply.
• Improved roster quality, fairness, and robustness.
• A broad range of options for dening personal preferences,
wishes, and lifestyles allows employees to design their
individual work plans in great detail.
• A very high and stable wish fulllment rate of 95 - 100% leads
to high employee satisfaction.
• Shorter planning processes allow roster releases closer to
execution, reducing time and cost eort for intermediate
adaptations.
• Standardization and maintenance of informal planner know-
how reduces dependence from individual planners.
• Possibility to implement innovative rostering approaches
such as shift bidding.
Challenges
Various managerial, technical and operational challenges
had to be overcome during the 12-year cooperation. We
mention the following:
• As a non-standard, long-term, costly, complex, and
risky R&D project, this collaboration required strong
commitment, confidence, courage, patience and personal
trust from all stakeholders including Swissport’s top
management.
• A multitude of constraints from industrial regulations,
labor contracts and workplace agreements had to be
implemented, taking into account regional and group
specific differences and ever-changing operational
conditions and requirements specifications.
• Detailed modelling of employee preferences was difficult
and required the study of hundreds of informal employee
files and notes to understand and formalize individual
wishes.
• Computational performance is an ongoing challenge, since
current computation times are at the limit of the strictly
defined operational deadlines. Improvements typically
require deep mathematical and algorithmic research.
Further Business Opportunities
The Auto-Roster software can easily be transferred to other
customers and industries, since business rules, mathematical
models, and algorithms are designed on a generic abstraction
level. This opens up a variety of new business opportunities
for the protable application of the Auto-Roster tool.
