An automated optimisation framework for the development of re-configurable business processes: a web services approach

Vergidis, Kostas; Turner, Christopher; Alechnovic, Alex; Tiwari, Ashutosh

doi:10.1080/0951192x.2013.814159

Cited by 18 publications

(23 citation statements)

References 26 publications

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“…The design size ranges from 04 tasks to 06 tasks according to [11]. Table III shows -clearly-that BPMOO Framework provides the finest results with MA-NSGAII, in comparison with [11]. Fig.…”

Section: Experimentation and Resultsmentioning

confidence: 98%

“…The authors chose the minimum Fitness value of each solution as a parameter to evaluate each solution. The obtained results by [11] are resumed with the tournament selection. As explained in Subsection II-B, Mass selection is the only technique that gives importance to solution's appearance during the evaluation process.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

“…The results are obtained with an initial population of 500 then 100 individuals (see Table II), a number of iterations up to 20 iterations. The design size ranges from 04 tasks to 06 tasks according to [11]. Table III shows -clearly-that BPMOO Framework provides the finest results with MA-NSGAII, in comparison with [11].…”

Section: Experimentation and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

Mahammed¹,

Benslimane²,

Hamdani

2018

Computational Intelligence and Its Applications

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Optimization is known as the process of finding the best possible solution to a problem given a set of constraints. The problem becomes challenging when dealing with conflicting objectives, which leads to a multiplicity of solutions. Evolutionary algorithms, which use a population approach in their search procedures, are advised to suitably solve the problem. In this article, we present an approach for an evolutionary combinatorial multi-objective optimization of business process designs using a variation of NSGAII, baptized MA-NSGAII. The variants of NSGAII are numerous. In fact, the vast majority deals either with the crossover operator or with the crowding distance. We discuss an optimization Framework that uses (i) a proposal of effective Fitness function, (ii) 02 contradictory criteria to optimize and (iii) an original selection technique. We test the proposed Framework with a real life case of multi-objective optimization of business process designs. The obtained results clearly indicate that an effectual Fitness function combined with the appropriate selection operator affects undeniably quality and quantity of solutions.

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“…The design size ranges from 04 tasks to 06 tasks according to [11]. Table III shows -clearly-that BPMOO Framework provides the finest results with MA-NSGAII, in comparison with [11]. Fig.…”

Section: Experimentation and Resultsmentioning

confidence: 98%

Section: Experimentation and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

Mahammed¹,

Benslimane²,

Hamdani

2018

Computational Intelligence and Its Applications

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show abstract

“…Many enterprise systems in organisations, such as ERP (Enterprise Resource Planning), possess event logging capabilities. Such event logs may be mined in order to reconstruct a chain of activities that have taken place within the organisation and administrated by the system (Tiwari et al 2008, IEEE Task Force on Process Mining 2011, Turner et al 2012 and then further analysed by automated techniques to provide optimised processes (Tiwari et al 2010, Vergidis et al 2015. Similar event logging based audit trails have been utilised in the field of cyber threat detection within networked software systems.…”

Section: The Audit Trail For Maintenancementioning

confidence: 99%

Intelligent decision support for maintenance: an overview and future trends

Turner

Emmanouilidis

Tomiyama

et al. 2019

International Journal of Computer Integrated Manufacturing

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The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision making systems. While e-maintenance practice provides a framework for internet connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of a comprehensive framework for its processing, analysis and use should be a valuable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable 'Human in the loop' interactions.

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“…However, today processes and process systems, business processes in particular, whatever area of activity they belong to (manufacture, service, maintenance, ensuring, procurement, logistics) are modelled not quantitatively but on the contrary, qualitatively and descriptively in the form of verbal, textual, table, graphic and other descriptions, or notations, of the flows of works, resources, informational data, etc. (Vergidis et al, 2015;Brasseur et al, 2017). Existing literature dedicated to processes understands the modelling of processes as so-called regulation, documentation and accompanying document circulation and optimization as taking some measures to harmonize and partially improve the processes (Jeston and Nelis, 2013).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Manufacturing Processes and of Systems Under the Conditions of Uncertainty

Madera¹

2019

J. Eng. Appl. Sci.

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This study is dedicated to optimization of manufacturing processes and manufacturing systems wherein interrelated activities are conducted in order to achieve the united common goal of converting the input flows of material, informational and financial resources into output ones valuable for the consumer. The process outcomes are a priori unknown and are determined by uncertain factors such as the future demand, risks and opportunities, the future states of the economy and finances, the market conjuncture, the prices of energy carriers and many others. In order to design the processes and process systems adequately, their mathematical optimization models must take into account the uncertainty of all the factors that exert influence on the course and outcomes of the processes in future. For this purpose, the study has devised a mathematical probabilistic optimization model of a process and process system with taking into account the uncertainty of the future values of the factors and the states of the (competitive, political, economic, financial, conjunctural, etc.) environment. A model in the form of an intervally stochastic random value with the arbitrary unimodal distribution of its values within their change interval has been accepted as the mathematical model of the factor uncertainty. The intervally stochastic model of the uncertainty is adequate for the real uncertainty and conforms to the agent's psychology when making the decision and quantitatively estimating the uncertainties. The mathematical model devised by this study describes the processes conducted both in the separate manufacturing, ensuring, servicing links and in the process system in whole. To ensure the possibility of numerically determining the optimal parameter values for a process and process system, the probabilistic mathematical optimization model is reduced to a determinate mathematical optimization model, the solution of which can be effortlessly obtained by means of existing software intended for the solution of mathematical programming problems. The application of the devised mathematical models has been exemplified by optimization of a particular production process that includes ensuring and logistic processes, too. This study considers influence of the uncertain factors, such as investments into the process, future prices of the production factors and energy carriers, consumer's demand, price of the end product, as well as future actualization of the possible opportunities and risks. It has been shown that the results of modelling and optimizing the processes under the uncertainty conditions are intervally stochastic and must be represented in the form of the intervals of their values depending on the probabilities of actualization of the risks and opportunities.

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An automated optimisation framework for the development of re-configurable business processes: a web services approach

Cited by 18 publications

References 26 publications

Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

Intelligent decision support for maintenance: an overview and future trends

Optimization of Manufacturing Processes and of Systems Under the Conditions of Uncertainty

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