Estimating performance of a business process model

Kamrani,; Ayani,; Moradi,

doi:10.1109/wsc.2009.5429226

Cited by 11 publications

(11 citation statements)

References 15 publications

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“…In [1], we presented a model for estimating the performance of human agents in a business process. In this model the value added by an agent to a task (per time unit) is described as a function of the capabilities of the agent and the importance or weights of these capabilities for the task.…”

Section: Contributionsmentioning

confidence: 99%

“…creativity may have a huge impact on a problem-solving task, while it is less important for work on a conveyor belt. Hence, the sum of agents' capabilities weighted by their importance for the task constitutes an indicator of the value added by agents to the task [1].…”

Section: Contributionsmentioning

confidence: 99%

“…In an earlier work [1], we presented a model for estimating the performance of agents in a business process model. This model was used in [2] for optimizing the performance of a business process model by assigning tasks to agents.…”

Section: Introductionmentioning

confidence: 99%

“…For a team of m agents G A , we define the time schedule of the team as a set of real numbers T = {α i,0 , α i,i , α S∪{i} : i ∈ A, S ⊆ A \ {i}, |S| ≥ 1}, where all α i,0 , α i,i and α S∪{i} fulfill the constraint expressed by (1). Given a team G A and its time schedule T , the overall structure of the team is settled, i.e.…”

Section: Introductionmentioning

confidence: 99%

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A model for estimating the performance of a team of agents

Kamrani

Ayani

Moradi

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-In this paper, we present a model for estimating the performance of a team of agents, based on the capabilities of the agents and importance of these capabilities for the task. Performance of a team is assumed to be the sum of contributions of individual agents and contributions of subgroups built in the team. We introduce a set of notations, which is required for discussing the suggested models. We also propose a model to estimate the benefit of an agent from interaction with other agents in a subgroup. Based on this benefit model and different (common) strategies, the agents devise plans in which they formulate to what extent they are willing to cooperate with other agents. A negotiation algorithm that resolves the conflicts between the desires of the agents is presented. The effect of this algorithm and different strategies are tested on a set of generated data. The test results show that the performance of a team when the agents choose a cooperation strategy that follows the principle of least effort (Zipf's law) is higher than teams with other cooperation strategies.

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Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A model for estimating the performance of a team of agents

Kamrani

Ayani

Moradi

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

Self Cite

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show abstract

“…In a previous paper [9] we presented a model for estimating performance of human agents in a business process. In this model the performance or value added by an agent to a task is defined as a function of the capabilities of the agent and the importance of these capabilities for the task.…”

Section: Agentsmentioning

confidence: 99%

Optimizing a Business Process Model by Using Simulation

Kamrani¹,

Ayani²,

Karimson³

2010

2010 IEEE Workshop on Principles of Advanced and Distributed Simulation

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Abstract-In this paper we present the problem of optimizing a business process model with the objective of finding the most beneficial assignment of tasks to agents, without modifying the structure of the process itself. The task assignment problem for four types of processes are distinguished and algorithms for finding optimal solutions to them are presented: 1) a business process with a predetermined workflow, for which the optimal solution is conveniently found using the well-known Hungarian algorithm. 2) a Markovian process, for which we present an analytical method that reduces it to the first type. 3) a nonMarkovian process, for which we employ a simulation method to obtain the optimal solution. 4) the most general case, i.e. a nonMarkovian process containing critical tasks. In such processes, depending on the agents that perform critical tasks the workflow of the process may change. We introduce two algorithms for this type of processes. One that finds the optimal solution, but is feasible only when the number of critical tasks is few. The second algorithm is even applicable to large number of critical tasks but provides a near-optimal solution. In the second algorithm a hill-climbing heuristic method is combined with Hungarian algorithm and simulation to find an overall near-optimal solution for assignments of tasks to agents. The results of a series of tests that demonstrate the feasibility of the algorithms are included.

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Work process improvement through simulation optimization of task assignment and mental workload

Kandemir

Handley

2018

Comput Math Organ Theory

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Estimating performance of a business process model

Cited by 11 publications

References 15 publications

A model for estimating the performance of a team of agents

A model for estimating the performance of a team of agents

Optimizing a Business Process Model by Using Simulation

Work process improvement through simulation optimization of task assignment and mental workload

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