Research On Workflow Scheduling And Mining Method In Instance Aspect

Nowadays, workflow technology has become one of the core technologies of achieving enterprise business process automatization. Many process-aware information systems such as workflow management system, business process management system and enterprise resource planning system have employed workflow technology. At the same time, the relationship and constraints among multiple workflow instances of the same type commonly exist in practical production or administrative business process. Some of them have been noticed and utilized to improve efficiency or save execution cost. However, current workflow modeling methods for describing relationship and constraints among workflow instance still need to be further researched. Current workflow scheduling methods for dynamic workflow instance aspect handling are incomplete also. For example, it is still difficult to effectively and completely model the relationship and constraints among multiple workflow instances of the same type as what is happened in traditional worklow. Existing workflow scheduling mechanisms lack the capability of supporting multiple activities’dynamic instance aspect handling and optimization. Mining workflow model from event logs is an important workflow mining method, but existing workflow mining algorithms cann’t obtain information on workflow instance aspect. The above problems are researched deeply in this thesis. The main work and contributions of the thesis are as followes:(1) To describe and suit for the characteristic of dynamic instance aspect handling in workflows, a workflow instance aspect model is proposed.The new requirements of workflow model to support workflow instance aspect handling are analyzed. To meet these requirements, new elements such as instance aspect handling area, activity able of instance aspect handling and data operation supporting instance aspect handling are introduced to extend the process definition meta-model by the Workflow Management Coalition. Based on the extended meta-model, a workflow instance aspect model is proposed, which can describe these new elements and related data operations such as projection, selection, combination, distribution and division.(2) To solve the problem of supporting multiple activities’dynamic instance aspect handling under workflow management system environment, a mechanism that controls the process of workflow instance aspect handling is presented.The proposed control mechanism consists of several components such as process controller, BPA controller, activity instance manager, executor manager and related control algorithms. Among these components, the activity instance manager is mainly to maintain information related to activity instance groups, by which the control problems caused by workflow instance aspect handling can be solved. Besides, a scheduling engine supporting workflow instance aspect handling is designed according to such mechanism. The proposed scheduling engine can be viewed as an extention to workflow enactment service and it can work well with traditional workflow engine. An exhibited example shows that the proposed control mechanism can well control the automation process of workflow instance aspect handling and meet the needs of practical applications completely.(3) To solve the time optimization problem of grouping and scheduling multiple workflow activity instances with the objective of minimum acitity instances’total dwelling time and multiple constraints, denoted by UM.N|1|Tmin, its mathematic model is constructed and two scheduling algorithms of PSOSA-T and ACO-T are proposed.The PSOSA-T algorithm is based on particle swarm optimization and simulated annealing algorithm. It adopts a coding approach based on the sequence of activity instances. The feasible solutions are produced indirectly by decoding such sequence. Because traditional particle swarm optimization is easy to get trapped in local minima, simulated annealing algorithm is used to overcome such drawback. In the ACO-T algorithm, the candidate solutions are constructed directly by using the ACO’s characteristic in construction, which obviously improved the efficiency in optimization. Besides, the conception of wasted grouping time is defined according to the optimization objective of the Um,N|1|Tmin problem, which is used as a base of constructing heuristic information to guide the search process of ant colony. The results of simulation experiment show the effectiveness of these two algorithms.(4) To solve the time-cost trade-off optimization problem of grouping and scheduling multiple workflow activity instances with the objectives of minimum acitity instances’total dwelling time and minimum acitity instances’total cost and multiple constraints, denoted by UM,N|1|Tmin,Cmin, its mathematic model is constructed and two scheduling algorithms of MOPSO-TC and PACO-TC are proposed.The coding and decoding methods in the MOPSO-TC algorithm are similar to the PSOSA-T algorithm. However, MOPSO-TC adopts crowding distance measure of particle’s density and a time variant mutation operator based on random swapping to guide the search process of particles. A set of Pareto optimal solutions satisfying constraints can be obtained in the end. In the PACO-TC algorithm, the solutions are constructed just like the ACO-T algorithm. Besides, the conception of wasted grouping time and wasted grouping cost are introduced according to the two different optimization objectives of the UM,N|1|Tmin,Cmin problem, based on which the heuristic information and candidate list for the ants are designed. The experiments are also performed to evaluate these two algorithms in Pareto solution quality and time consuming. In comparison with MOPSO-TC, PACO-TC can get better solutions at the cost of increased time consuming.(5) To overcome the shortcomings of traditional workflow modeling methods, an algorithm to mine workflow instance aspect model from event logs is proposed.The proposed mining algorithm can effectively mine batch processing workflow models from event logs by considering the instance aspect relations among activity instances in multiple workflow cases. The notion of instance aspect handling feature and its corresponding mining algorithm are also presented for discovering the instance aspect handling area in the model by using the input and output data information of activity instances in events. The proposed two mining algorithms can make full use of current workflow mining algorithms and help to enhance their applicability in some sense. The experiment results show their effectiveness.