Advanced SearchSearch Tips
Generation of Business Process Reference Model Considering Multiple Objectives
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Generation of Business Process Reference Model Considering Multiple Objectives
Yahya, Bernardo Nugroho; Wu, Jei-Zheng; Bae, Hye-Rim;
  PDF(new window)
The implementation of business process management (BPM) systems in large number of business organizations transforms BPM system into such a level of maturity and tends to collect large repositories of business process (BP) models. This issue encourages BP flexibility that leads to a large number of process variants derived from the same model, but differing in structure, to be stored in the large repositories of BP models. Therefore, the repositories may include thousands of activities and related business objects with variation of requirements and quality of service. It is a common practice to customize processes from reference processes or templates in order to reduce the time and effort required to design and deploy processes on all levels. In order to address redundancy and underutilization problems, a generic process model, called as reference BP, is absolutely necessary to cover the best of process variants. This study aims to develop multiple-objective business process genetic algorithm (MOBPGA) to find a set of non-dominated (Pareto) solutions of business reference model to enhance conventional approach which considered only a single objective on creating BP reference model by using proximity score measurement. A mixed-integer linear program is constructed to evaluate performance of the proposed MOBPGA on small-scale problems by using standard measures for multiple-objective techniques. The results will show the viability of applying MOBPGA in terms of simultaneously maximizing proximity score measurement, minimizing total duration, and total costs of the selected reference model.
Business Process Management;Business Process Reference Model;Multi-Objective;Genetic Algorithm;
 Cited by
Abboud, N., Inuiguchi, M., Sakawa, M., and Uemura, Y. (1998), Manpower allocation using genetic annealing, European Journal of Operational Research, 111(2), 405-420. crossref(new window)

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002), A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2), 182-197. crossref(new window)

Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.

Gen, M. and Cheng, R. (2000), Genetic Algorithms and Engineering Optimization, John Wiley and Sons, New York, NY.

Gen, M., Katai, O., McKay, B., Namatame, A., Sarker, R. A., and Zhang, B.-T. (2009), Intelligent and Evolutionary Systems, Springer-Verlag, Berlin, Germany.

Hajri-Gabouj, S. (2003), A fuzzy genetic multiobjective optimization algorithm for a multilevel generalized assignment problem, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 33(2), 214-224. crossref(new window)

Li, C., Reichert, M., and Wombacher, A. (2009), Discovering reference models by mining process variants using a heuristic approach, Business Process Management, Lecture Notes in Computer Science, 5701, 344-362

Lu, R. and Sadiq, S. (2006), Managing process variants as an information resource, Business Process Management, Lecture Notes in Computer Science, 4102, 426-431.

Minella, G., Ruiz, R., and Ciavotta, M. (2008), A review and evaluation of multiobjective algorithms for the flowshop scheduling problem, INFORMS Journal on Computing, 20(3), 451-471. crossref(new window)

Neubauer, T. and Heurix, J. (2008a), Defining secure business processes with respect to multiple objectives, Proceedings of the 3rd International Conference on Availability, Reliability and Security, Barcelona, Spain, 187-194.

Neubauer, T. and Heurix, J. (2008b), Multiobjective decision support for defining secure business processes: a case study, International Journal of Business Intelligence and Data Mining, 3(2), 177-195. crossref(new window)

Quan, L. and Tian, G.-S. (2009), A business processes' multi-objective optimization model based on simulation, Proceedings of International Conference on Information Management, Innovation Management and Industrial Engineering, Xi'an, China, 572-575.

Tiwari, A., Vergidis, K., and Turner, C. (2010), Evolutionary multi-objective optimization of business processes, Soft Computing in Industrial Application, Advances in Intelligent and Soft Computing, 75, 293-301.

Van der Aalst, W. M. P. (2000), Workflow verification: finding control-flow errors using Petri-net-based techniques, Business Process Management, Lecture Notes in Computer Science, 1806, 161-183.

Van der Aalst, W. M. P., Alves de Medeiros, A. K., and Weijters, A. J. M. M. (2005), Genetic process mining, Applications and Theory of Petri Nets, Lecture Notes in Computer Science, 3536, 48-69.

Vergidis, K., Tiwari, A., and Majeed, B. (2006), Business process improvement using multi-objective optimization, BT Technology Journal, 24(2), 229- 235. crossref(new window)

Verdigis, K., Tiwari, A., Majeed, B., and Roy, R. (2007), Optimisation of business process designs: an algorithmic approach with multiple objectives, International Journal of Production Economics, 109(1/2), 105-121. crossref(new window)

Wang, L.-C., Cheng, C.-Y., and Huang, L.-P. (2010), A genetic algorithm for directed graph-based supply network planning in memory module industry, Industrial Engineering and Management Systems, 9(3), 227-241. crossref(new window)

Wu, J.-Z., Chien, C.-F., and Gen, M. (2012), Coordinating strategic outsourcing decisions for semiconductor assembly using a bi-objective genetic algorithm, International Journal of Production Research, 50(1), 235-260. crossref(new window)

Yahya, B. N., Bae, H., Bae, J., Kim, D. (2010) Generating business process reference model using genetic algorithm, Proceedings of the 23th Annual Conference of Biomedical Fuzzy Association, Soft Computing Application, Kitakyushu, Japan, 245-248.

Zhou, G., Min, H., and Gen, M. (2003), A genetic algorithm approach to the bi-criteria allocation of customers to warehouses, International Journal of Production Economics, 86(1), 35-45. crossref(new window)

Zinflou, A., Gagne, C., Gravel, M., and Price, W. L. (2008), Pareto memetic algorithm for multiple objective optimization with an industrial application, Journal of Heuristics, 14(4), 313-333. crossref(new window)