Title: Combinatorial optimization techniques for manpower scheduling problems
Authors: Zhang, Zizhen (張子臻)
Abstract: This thesis studies a class of manpower scheduling problems in the field of
operations research. In daily operations of a company, we may need to dispatch
a number of employees to carry out a set of requests or tasks. Making a good
schedule for each employee is one of the most crucial decisions for the manager
and decision maker. Generally speaking, a schedule is a subset of tasks assigned
to employees over a specific planning horizon. The high-quality schedule can
help improve the efficiency of manpower and reduce the operational cost, thereby
strengthening the competitiveness of the company.
The manpower scheduling problems we investigate in this thesis aim at planning
a schedule that can satisfy the following main requirements. First, a schedule
must be practical. It needs to take various kinds of rules or regulations into account.
Second, it should provide sufficient
exibility for the employees to perform
their assigned tasks. Third, the company can reduce their operational cost without
the provided service level being negatively affected.
Apart from the manpower scheduling problems, there are many other scheduling
problems in literature. For example, machine scheduling problems and jobshop
scheduling problems are two typical types of scheduling problems that have attracted much academic attention in the last several decades. Both of them
share some similarities with the manpower scheduling problems. However, the
manpower scheduling problems put more emphasis on the nature and behavior
of human beings. In particular, we summarize some characteristics of the manpower
scheduling problems as follows: (1) working periods and working hours;
(2) skills and qualifications; (3) travel and service time; (4) collaborations and
synchronization; (5) knowledge and learnability; and (6) other characteristics.
In this thesis, we first introduce the background of our study and then present
some related literature of the manpower scheduling problems in the area of
discrete combinatorial optimization. Subsequently, we elaborate three practical
manpower scheduling problems, namely, the Manpower Scheduling Problem
with Crew Collaboration Constraints (MSPCC), the Manpower Scheduling Problem with Regular Working Hour Constraints (MSPRWH), and the Manpower
Scheduling Problem with Crew Holding Cost (MSPCHC). All these problems are
stemmed from real-world applications, which re
ect some or all above-mentioned
characteristics. Finally, we conclude the thesis with closing remarks.
The MSPCC is a practical scheduling and routing problem that tries to synchronize
worker schedules to complete all tasks. We first provide an integer
programming model for the problem and discuss its properties. Next, we show
that the tree data structure can be used to represent the MSPCC solutions, and
its optimal solution can be obtained from one of trees by solving a minimum
cost
ow model for each worker type. Based on the above findings, we develop
for the problem a novel tabu search algorithm employing tree-based search operators.
Finally, we evaluate the effectiveness of the tabu search algorithm by
computational experiments on two sets of instances.
The MSPRWH examines an inspector scheduling problem with time windows
whereby labour regulations affect planning horizons, and therefore, profitability. The goal is to determine a schedule, that is, a set of routes for inspectors that
maximizes profitability from visited locations, based on the conditions that inspectors
can only travel during stipulated working hours within each period in a
given planning horizon and that the inspectors are only required to return to the
depot at the end of the last period. We propose an effective tabu search algorithm
with an ejection pool representation to solve the MSPRWH. We evaluate the
effectiveness of our tabu search algorithm with extensive experiments based on
the team orienteering problem with time windows instances and a set of modified
solomon's benchmark instances. The results indicate that our approach generates
high-quality solutions.
The MSPCHC is a simplified version of the real-world film shooting problem,
which aims to determine a shooting sequence so as to minimize the total cost of the
actors involved. We first formulate the problem as an integer linear programming
model and then devise a branch-and-bound algorithm to solve it. Subsequently,
we enhance the branch-and-bound algorithm by several accelerating techniques,
including preprocessing, dominance rules and caching search states. Extensive
experiments over two sets of benchmark instances suggest that our branch-and-bound
algorithm is superior to the currently best exact algorithm for the problem.
Finally, the impacts of different parameter settings are also disclosed by some
additional experiments.
Notes: CityU Call Number: HF5549.5.M3 Z45 2014; xii, 137 p. : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2014.; Includes bibliographical references (p. 122-137)
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