Evolutionary Multi-Objective Optimization of business process designs with pre-processing

Georgoulakos, Kostas; Vergidis, Kostas; Tsakalidis, George; Samaras, Nikolaos

doi:10.1109/cec.2017.7969404

Cited by 10 publications

(17 citation statements)

References 12 publications

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“…Current trends in BPM accentuate BPR and BPO as the initiatives for rethinking and re-organizing business processes with the specific purpose of making them perform better. In the light of the aforementioned issues in BPO, the authors attempted to produce business process designs that conform to the problem specification, adding an initial form of preprocessing to investigate whether the task alternatives can provide feasible solutions [6]. The results demonstrate that the framework-with the aid of a preprocessing stage-increased its capability of generating diverse designs and selecting those with optimal objective values for business processes in a reduced amount of time, suggesting a more elaborate investigation.…”

Section: Related Workmentioning

confidence: 99%

“…This paper presents the p3 extension of the bpo F business process optimization framework [21] that includes a more thorough preprocessing stage, as discussed in Section 4. The p3 preprocessing stage builds upon the work in [6] and aims to further enhance the performance of the evolutionary multi-objective optimization algorithms (EMOAs) in relation to the BPO problem.…”

Section: Related Workmentioning

confidence: 99%

“…Business process optimization is among the key research areas that provide a formal perspective towards the concept of business processes. The approach presented in this paper builds upon a multi-objective evolutionary framework for business process models [5] and an initial preprocessing approach [6] that demonstrates promising results. This paper Computation 2021, 9, 16 2 of 15 introduces an elaborate and improved preprocessing phase (p3) with the aim of further increasing the efficiency of the business process composition and optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…A strategy to reduce the computational complexity of the problem is to reduce the number of candidate tasks that can participate in a solution by identifying and eliminating those that are redundant given the problem formulation, as demonstrated in the authors' initial work [6]. The next section introduces the p3 preprocessing phase in an attempt to thoroughly elaborate the above-mentioned strategy and improve the performance and quality of results of the optimization framework.…”

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confidence: 99%

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An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models

Tsakalidis¹,

Georgoulakos²,

Paganias³

et al. 2021

Computation

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Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact that many approaches have produced promising results regarding the enhancement of attribute performance, little has been done to reduce the computational complexity due to the size of the problem. The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs) to generate a series of diverse optimized business process designs based on specific process requirements. The preprocessing phase follows a systematic rule-based algorithmic procedure for reducing the library size of candidate tasks. The experimental results on synthetic data demonstrate a considerable reduction of the library size and a positive influence on the performance of EMOAs, which is expressed with the generation of an increasing number of nondominated solutions. An important feature of the proposed phase is that the preprocessing effects are explicitly measured before the EMOAs application; thus, the effects on the library reduction size are directly correlated with the improved performance of the EMOAs in terms of average time of execution and nondominated solution generation. The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working on the problem specification at an earlier stage.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models

Tsakalidis¹,

Georgoulakos²,

Paganias³

et al. 2021

Computation

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“…Reference [37] describes the use of several MOEAs such as the Non-Dominated Sorting GA-2 (NSGA2), and the Strength Pareto EA-2 (SPEA2) to solve the Business Process Optimization (BPO) problem. The authors provide a set of BPO alternative solutions for several experimental and reallife scenarios and produce satisfactory results, since this approach generates diverse designs, and selects those with optimal objective values for business processes in less time.…”

Section: Introductionmentioning

confidence: 99%

Optimization for Risk Decision-Making Through Simulated Annealing

Eraña-Díaz¹,

Cruz-Chávez²,

Rivera-López

et al. 2020

IEEE Access

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In this paper, a computational methodology combining the simulated annealing algorithm with two machine learning techniques to select a near-optimal safeguard set for business risk response is presented. First, a mathematical model with four types of risk factor responses (avoid, mitigate, transfer, and accept) is constructed. Then, the simulated annealing algorithm is applied to find a set of near-optimal solutions to the model. Next, these solutions are processed by the k-means clustering algorithm for identifying three categories, and with a decision tree classifier, the most relevant elements of each one are obtained. Finally, the categorized solutions are shown to the decision-makers through a user interface. These stages are designed with the aim of the users can take an appropriate safeguard set and develop one specific and optimal program to respond to business risk factors. The results generated by the proposed approach are reached in a reasonable time using less computational resources than those used by other procedures. Furthermore, the best results obtained by the simulated annealing algorithm use a lower business budget, and they have a relative-error less than 0.0013% of the optimal solution given by a deterministic method.

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