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Efficient Genetic Algorithms for Optimal Assignment of Tasks to Teamsof Agents
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
Swedish Defence Research Agency, Stockholm, Sweden.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-6283-7004
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The problem of optimally assigning agents (resources) to a given set oftasks is known as the Assignment Problem (AP). The classical AP and manyof its variations have been discussed extensively in the literature. In thispaper, we present a specific class of Assignment Problems (APs) in which eachtask is assigned to a group of collaborating agents. In this AP, collaborationof all agents is required to perform the task and an agent cannot individuallydo it.

We present a mathematical model for this type of AP and use GeneticAlgorithm (GA) to solve the model, since there are no known polynomial timealgorithms for this class of APs. We show that for larger instances of the problem,the GA with one-point crossover operator cannot efficiently find nearoptimalsolutions. In general, the efficiency of the GA depends on the choiceof genetic operators (selection, crossover, mutation) and associated parameters.In order to design an efficient GA for finding near optimal assignment oftasks to collaborative teams, we focus on construction of crossover operators.We compare and analyze the efficiency of several well-known crossover operatorssuch as one-point, two-point, three-point, position-based and order-basedcrossover operators. We suggest modifications to these operators by addinga shuffled repair list to them and show that their efficiency is enhanced forsolving the presented AP. Furthermore, we introduce two new crossover operators,team-based and team-based shuffled list crossover operators, whichsolve large-scale models of our AP efficiently.

Keyword [en]
Genetic algorithms, Combinatorial optimization, Evolutionary computations, Heuristics, Large scale optimization, Team assignment problem
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-143762OAI: oai:DiVA.org:kth-143762DiVA: diva2:708375
Note

QS 2014

Available from: 2014-03-27 Created: 2014-03-27 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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