Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Optimization of Task Assignment to Collaborating Agents
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).ORCID iD: 0000-0002-6283-7004
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
2011 (English)In: IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CISched 2011, Paris, France: IEEE Computational Intelligence Society , 2011, 17-24 p.Conference paper, Published paper (Refereed)
Abstract [en]

The classic task assignment problem (AP) assigns m agents to n tasks, where each task is assigned to exactly one agent. This problem and many of its variations, including the case where a task is assigned to a group of agents working independently, have been discussed extensively in the literature. We consider a specific class of task assignment problems where each task is assigned to a group of collaborating agents that work as a team. Thus, changing one of the group members may have a vital impact on the output of the group. We assume that each agent has a set of capabilities and each task has certain requirements. The objective is to assign agents to teams such that the gain is maximized.

We suggest a Genetic Algorithm (GA) for finding a near optimal solution to this class of task assignment problems. To the best of our knowledge, this class of APs has not been considered in the literature, probably due to the difficulty of evaluating the performance of a team of agents. Recently, we have developed a formal method for measuring performance of a team which is used in this paper to formulate the objective function of our GA. We analyze the quality of the obtained solution by comparing the result of our GA with (a) the exact solution of some smaller problems, and (b) with the results of the exact solution of specific cases that can be obtained by the Hungarian algorithm. We provide experimental results on efficiency, stability, robustness and scalability of the solution obtained by our GA.

Place, publisher, year, edition, pages
Paris, France: IEEE Computational Intelligence Society , 2011. 17-24 p.
Keyword [en]
Task Assignment Problem, Genetic Algorithms, Evolutionary Algorithms, Optimization
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-45938DOI: 10.1109/SCIS.2011.5976547Scopus ID: 2-s2.0-80052082446ISBN: 978-161284196-0 (print)OAI: oai:DiVA.org:kth-45938DiVA: diva2:453151
Conference
IEEE Symposium on Computational Intelligence in Scheduling. Paris, 11 April 2011 - 15 April 2011
Note

QC 20111102

Available from: 2011-11-01 Created: 2011-11-01 Last updated: 2014-03-28Bibliographically approved
In thesis
1. Simulation-based Optimization and Decision Making with Imperfect Information
Open this publication in new window or tab >>Simulation-based Optimization and Decision Making with Imperfect Information
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The purpose of this work is to provide simulation-based support for making optimal (or near-optimal) decisions in situations where decision makers are faced with imperfect information. We develop several novel techniques and algorithms for simulation-based optimization and decision support and apply them to two categories of problems: (i) Unmanned Aerial Vehicle (UAV) path planning in search operations, and; (ii) optimization of business process models. Common features of these two problems for which analytical approaches are not available, are the presence of imperfect information and their inherent complexity.

In the UAV path planning problem, the objective is to define the path of a UAV searching for a target on a known road network. It is assumed that the target is moving toward a goal and we have some uncertain information about the start point of the target, its velocity, and the final goal of the target. The target does not take evasive action to avoid being detected. The UAV is equipped with a sensor, which may detect the target once it is in the sensor’s scope. Nevertheless, the detection process is uncertain and the sensor is subject to both false-positive and false-negative errors. We propose three different solutions, two of which are simulation-based. The most promising solution is an on-line simulation-based method that estimates the location of the target by using a Sequential Monte Carlo (SMC) method. During the entire mission, different UAV paths are simulated and the one is chosen that most reduces the uncertainty about the location of the target.

In the optimization of the business process models, several different but related problems are addressed: (i) we define a measure of performance for a business process model based on the value added by agents (employees) to the process; (ii) we use this model for optimization of the business process models. Different types of processes are distinguished and methods for finding the optimal or near-optimal solutions are provided; (iii) we propose a model for estimating the performance of collaborative agents. This model is used to solve a class of Assignment Problems (AP), where tasks are assigned to collaborative agents; (iv) we propose a model for team activity and the performance of a team of agents. We introduce different collaboration strategies between agents and a negotiation algorithm for resolving conflicts between agents. We compare the effect of different strategies on the output of the team.

Most of the studied cases are complex problems for which no analytical solution is available. Simulation methods are successfully applied to these problems. They are shown to be more general than analytical models for handling uncertainty since they usually have fewer assumptions and impose no restrictions on the probability distributions involved. Our investigation confirms that simulation is a powerful tool for providing decision-making support. Moreover, our proposed algorithms and methods in the accompanying articles contribute to providing support for making optimal and in some cases near-optimal decisions: (i) our tests of the UAV simulation-based search methods on a simulator show that the on-line simulation method has generally a high performance and detects the target in a reasonable time. The performance of this method was compared with the detection time when the UAV had the exact information about the initial location of the target, its velocity, and its path (minimum detection time). This comparison indicated that the online simulation method in many cases achieved a near-optimal performance in the studied scenario; (ii) our business process optimization framework combines simulation with the Hungarian method and finds the optimal solution for all cases where the assignment of tasks does not change the workflow of the process. For the most general cases, where the assignment of tasks may change the workflow, we propose an algorithm that finds near-optimal solutions. In this algorithm, simulation, which deals with the uncertainty in the process, is combined with the Hungarian method and hill-climbing heuristics. In the study of assigning tasks to collaborative agents we suggest a Genetic Algorithm (GA) that finds near-optimal solutions with a high degree of accuracy, stability, scalability and robustness. While investigating the effect of different agent strategies on the output of a team, we find that the output of a team is near-optimal, when agents choose a collaboration strategy that follows the principle of least effort (Zipf’s law) and use our suggested algorithm for negotiation and resolving conflicts. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2011. xiii, 36 p.
Series
Trita-ICT-ECS AVH, ISSN 1653-6363 ; 11:09
Keyword
Simulation-based Optimization, UAV Path Planning, Business Process Optimization
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-50171 (URN)978-91-7501-151-6 (ISBN)
Public defence
2011-12-09, Sal D, Forum, Isafjordsgatan 39, Kista, 14:00 (English)
Opponent
Supervisors
Note
QC 20111202Available from: 2011-12-02 Created: 2011-12-02 Last updated: 2011-12-07Bibliographically approved
2. 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

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Schulte, Christian

Search in DiVA

By author/editor
Younas, IrfanKamrani, FarzadSchulte, ChristianAyani, Rassul
By organisation
Software and Computer Systems, SCS (Closed 20120101)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 136 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf