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Using Genetic Algorithms for investigating specific regions of the solution space
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
Swedish Defence Research Agency, Stockholm, Sweden.
2011 (English)In: Proceedings of the 2011 African Conference on Software Engineering and Applied Computing (ACSEAC 2011), 2011Conference paper, Published paper (Refereed)
Abstract [en]

In many practical cases decision makers are interested to understand the whole solution space, including possible outliers. By outlier we mean there is a solution that is theoretically possible, though with very low probability that it occurs. In many combinatorial problems, this is a very challenging task. During the past decade, the Data Farming community has done substantial work on developing methods and techniques for better understanding of the solution space. The data farming community has also looked at the design of experiments and used Latin hypercube (LH) techniques for this purpose. The LH is proven to be one of the important sampling methods for selecting a representative subset of the input space. In this paper, we consider a company X that wants to outsource m subprojects of a given project P. We assume that there are n potential subcontractors for each subproject. Thus, there will be n^m ways to assign the subprojects to the potential subcontractors. The project manager is interested to find those assignments that complete the project within a given time and a given cost frame. An exhaustive examination of all assignments is not feasible, if m and n are big numbers. We propose an objective-based genetic algorithm (GA) for finding the set of assignments that are mapped onto a given subset of the solution space. It means, as opposed to the design of experiment techniques, we start from the solution space and try to find the combinations of the input parameter values that can lead to a specific region of the solution space. By some numerical examples, we show how our GA identifies the set of such feasible assignments.

Place, publisher, year, edition, pages
2011.
Keyword [en]
Data Farming, Design of Experiments, Assignment Problem, Genetic Algorithms, Evolutionary Algorithms, Decision-support Systems, Project Management
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-53038OAI: oai:DiVA.org:kth-53038DiVA: diva2:468646
Conference
2011 African Conference on Software Engineering and Applied Computing
Note
QC 20111228Available from: 2011-12-21 Created: 2011-12-21 Last updated: 2014-03-28Bibliographically approved
In thesis
1. Using Genetic Algorithms for Large Scale Optimizationof Assignment, Planning and Rescheduling Problems
Open this publication in new window or tab >>Using Genetic Algorithms for Large Scale Optimizationof Assignment, Planning and Rescheduling Problems
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There has always been a need to solve real-life large-scale problems, suchas efficiently allocating limited resources, and other complex and conflicting situations related to combinatorial optimization genre. A class of combinato- rial optimization problems is NP-hard and, among many well-known, several of them are assignment, planning and rescheduling problems. Assignment problems can deal with optimal assignment of teams of collaborating agents; planning problems can be effects-based planning that search for promising plans to get desired end states with minimal cost; rescheduling problems can be multi-criteria optimization of rescheduling resources that modify existing original schedule. These large scale optimization problems are complex with intractable and highly complex search spaces. Currently, there are no known algorithms with polynomial time complexity, which can solve these problems. Genetic Algorithms have been successfully applied to solve many complex optimization problems but not to the specific problems mentioned above.

The aim of the research, presented in this thesis, is to use Genetic Algo- rithms for large scale optimization of assignment, planning and rescheduling problems. More specifically, the contributions of the thesis are to: (i) adapt existing and develop new efficient Genetic Algorithms to solve large scale as- signment problems, and (ii) adapt existing Genetic Algorithms to solve large scale effects-based planning, and multi-objective rescheduling optimization problems.In case of assignment, we solve a team assignment problem and investigate specific regions in a solution space for assignment problems with huge search spaces.

For the team assignment, an existing Genetic Algorithm is adapted and applied for optimal assignment of tasks to teams of collaborating agents. The algorithm is scalable, stable, robust and produces a near optimal solution. The results of the team assignment problem show that the existing Genetic Algorithms are not efficient for optimal assignment of tasks to teams of agents. Hence, to solve larger instances of the problem efficiently, new Genetic Algo- rithms are developed with emphasis on the construction of crossover opera- tors. Since teams assignment can be multi-criteria, a multi-objective model is constructed and two widely used multi-objective evolutionary algorithms are applied. Further, for the assignment problems with huge search spaces, an existing Genetic Algorithm is adapted to extract possible combinations of input parameters from a specified solution space region. To solve the large scale effects-based planning, a multi-objective optimization problem is formu- lated for the evaluation of operational plans and a multi-objective Genetic Algorithm is adapted and applied to the problem. The results show that the suggested algorithm is much more efficient than A*. For the rescheduling problem, a multi-objective optimization model for rescheduling of resources is proposed and a multi-objective Genetic Algorithm is adapted and applied to obtain the Pareto-optimal solutions.

The research presented in this thesis confirms that Genetic Algorithms can be used for large scale assignment, planning and rescheduling problems since they have shown to be suitable in solving these problems efficiently.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2014. 202 p.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 14:06
National Category
Computer Systems
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-143671 (URN)978-91-7595-047-1 (ISBN)
Public defence
2014-04-25, Sal D, Forum, Isafjordsgatan 39, Kista, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20140328

Available from: 2014-03-28 Created: 2014-03-27 Last updated: 2014-03-28Bibliographically approved

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