Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.creator | Ng, Chun-fai | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/3755 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | An evolutionary approach to adaptive workflow management | en_US |
| dcterms.abstract | Workflow technologies are associated with the control and execution of operation processes of an organization. An effectively managed workflow can mean the right information being transferred to the right person at the right time. By considering different participants of a workflow process as working together on different tasks in a business process, we propose a technique to facilitate the optimal arrangement of inter-dependent tasks. Most workflow models are developed to handle static, predefined business processes. However, business operations must be adaptive to the changing business environment, and therefore, for most real-world applications, how tasks interact with one another will also have to be captured and managed. A "dynamic" workflow model that is adaptive in nature will thus be needed to cope with the constantly changing activities. In particular, since different departments within or between organizations may operate differently and may have conflicting goals, an adaptive workflow model is expected to allow a business process to be executed subject to different, possibly contradictory constraints. Given these requirements, we propose an evolutionary approach to an adaptive workflow model. The model can facilitate the handling of business activities in organizations that have to cope with rapid changes in their business environment. It uses a Genetic Algorithm to adaptively modify connections of the entry and exit points of each task so that maximum throughput, in terms of effectiveness and efficiency can be achieved. A process can be regarded as a set of connected tasks. The tasks must be connected under some precedence constraints, that is, certain tasks cannot go before some other tasks because they require information or depend on the results from those tasks. Our work optimizes the workflow process by changing the connection of the tasks while preserving the validity of the process to the precedence constraints. We denote the connections by a 2-dimensional matrix. In our evolutionary algorithm, we implement crossover by exchanging the connection configuration of randomly selected tasks from parents, hopefully combining their advantages. In addition, we implement mutation by randomly regenerating the connection configuration of randomly selected tasks to avoid the search being stuck in some local maximums. We propose a recovery mechanism to enable the children from such genetic operations in our evolutionary algorithm to be valid to the precedence constraints. Minimizing the feed backward distance of tasks is the objective of our evolutionary algorithm. The objective function may be modified to allow for new optimization objectives. The proposed algorithm has been integrated into a workflow-process-modeling tool. The technique is tested with simulated and real cases. The test results have shown that the proposed technique can be very useful in different real applications including those of logistics and supply chain management. | en_US |
| dcterms.extent | xi, 113 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2003 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Phil. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Workflow -- Management | en_US |
| dcterms.accessRights | open access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b16824301.pdf | For All Users | 3.85 MB | Adobe PDF | View/Open |
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