56 research outputs found
Scheduling Aircraft to Reduce Controller Workload
We address a problem in air traffic management: scheduling flights
in order to minimize the maximum number of aircraft that
simultaneously lie within a single air traffic control sector at
any time . Since the problem is a generalization of the NP-hard
no-wait job-shop scheduling, we resort to heuristics. We report
experimental results for real-world flight data
A Systematic Review of Approximability Results for Traveling Salesman Problems leveraging the TSP-T3CO Definition Scheme
The traveling salesman (or salesperson) problem, short TSP, is a problem of
strong interest to many researchers from mathematics, economics, and computer
science. Manifold TSP variants occur in nearly every scientific field and
application domain: engineering, physics, biology, life sciences, and
manufacturing just to name a few. Several thousand papers are published on
theoretical research or application-oriented results each year. This paper
provides the first systematic survey on the best currently known
approximability and inapproximability results for well-known TSP variants such
as the "standard" TSP, Path TSP, Bottleneck TSP, Maximum Scatter TSP,
Generalized TSP, Clustered TSP, Traveling Purchaser Problem, Profitable Tour
Problem, Quota TSP, Prize-Collecting TSP, Orienteering Problem, Time-dependent
TSP, TSP with Time Windows, and the Orienteering Problem with Time Windows. The
foundation of our survey is the definition scheme T3CO, which we propose as a
uniform, easy-to-use and extensible means for the formal and precise definition
of TSP variants. Applying T3CO to formally define the variant studied by a
paper reveals subtle differences within the same named variant and also brings
out the differences between the variants more clearly. We achieve the first
comprehensive, concise, and compact representation of approximability results
by using T3CO definitions. This makes it easier to understand the
approximability landscape and the assumptions under which certain results hold.
Open gaps become more evident and results can be compared more easily
Algorithmic And Mathematical Programming Approaches To Scheduling Problems With Energy-Based Objectives
This dissertation studies scheduling as a means to address the increasing concerns related to energy consumption and electricity cost in manufacturing enterprises. Two classes of problems are considered in this dissertation: (i) minimizing the makespan in a permutation flow shop with peak power consumption constraints (the PFSPP problem for short) and (ii) minimizing the total electricity cost on a single machine under time-of-use tariffs (the SMSEC problem for short). We incorporate the technology of dynamic speed scaling and the variable pricing of electricity into these scheduling problems to improve energy efficiency in manufacturing.The challenge in the PFSPP problem is to keep track of which jobs are running concurrently at any time so that the peak power consumption can be properly taken into account. The challenge in the SMSEC problem is to keep track of the electricity prices at which the jobs are processed so that the total electricity cost can be properly computed.
For the PFSPP problem, we consider both mathematical programming and combinatorial approaches. For the case of discrete speeds and unlimited intermediate storage, we propose two mixed integer programs and test their computational performance on instances arising from the manufacturing of cast iron plates. We also examine the PFSPP problem with two machines and zero intermediate storage, and investigate the structural properties of optimal schedules in this setting.
For the SMSEC problem, we consider both uniform-speed and speed-scalable machine environments. For the uniform-speed case, we prove that this problem is strongly NP-hard, and in fact inapproximable within a constant factor, unless P = NP. In addition, we propose an exact polynomial-time algorithm for this problem when all the jobs have the same work volume and the electricity prices follow a so-called pyramidal structure. For the speed-scalable case, in which jobs can be processed at an arbitrary speed with a trade-off between speed and energy consumption, we show that this problem is strongly NP-hard and that there is no polynomial time approximation scheme for this problem. We also present different approximation algorithms for this case and test the computational performance of these approximation algorithms on randomly generated instances
Scheduling Coflows for Minimizing the Total Weighted Completion Time in Heterogeneous Parallel Networks
Coflow is a network abstraction used to represent communication patterns in
data centers. The coflow scheduling problem in large data centers is one of the
most important -hard problems. Many previous studies on coflow scheduling
mainly focus on the single-core model. However, with the growth of data
centers, this single-core model is no longer sufficient. This paper considers
the coflow scheduling problem in heterogeneous parallel networks. The
heterogeneous parallel network is an architecture based on multiple network
cores running in parallel. In this paper, two polynomial-time approximation
algorithms are developed for scheduling divisible and indivisible coflows in
heterogeneous parallel networks, respectively. Both algorithms achieve an
approximation ratio of with arbitrary release times.Comment: arXiv admin note: text overlap with arXiv:2204.0265
Efficient job scheduling for a cellular manufacturing environment
An important aspect of any manufacturing environment is efficient job scheduling. With an increase in manufacturing facilities focused on producing goods with a cellular manufacturing approach, the need arises to schedule jobs optimally into cells at a specific time. A mathematical model has been developed to represent a standard cellular manufacturing job scheduling problem. The model incorporates important parameters of the jobs and the cells along with other system constraints. With each job and each cell having its own distinguishing parameters, the task of scheduling jobs via integer linear programming quickly becomes very difficult and time-consuming. In fact, such a job scheduling problem is of the NP-Complete complexity class. In an attempt to solve the problem within an acceptable amount of time, several heuristics have been developed to be applied to the model and examined for problems of different sizes and difficulty levels, culminating in an ultimate heuristic that can be applied to most size problems. The ultimate heuristic uses a greedy multi-phase iterative process to first assign jobs to particular cells and then to schedule the jobs within the assigned cells. The heuristic relaxes several variables and constraints along the way, while taking into account the flexibility of the different jobs and the current load of the different cells. Testing and analysis shows that when the heuristic is applied to various size job scheduling problems, the solving time is significantly decreased, while still resulting in a near optimal solution. ii
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
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