Evolutionary Business Process Optimization using a Multiple-Criteria Decision Analysis method

Mahammed, Nadir; Benslimane, Sidi Mohamed; Ouldkradda, Ali; Fahsi, Mahmoud

doi:10.1109/cits.2018.8440166

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…According to [26] [27] one of the most widely used MOEA's that has been effective in finding the Pareto optimal solutions is the elitist NSGA-II algorithm. Both the diversity and the convergence abilities of the NSGA-II algorithm have been demonstrated by [28]. They have also shown the suitability of NSGA-II in producing an acceptable number of optimized design alternatives regarding the problem complexity and in a reasonable timeframe.…”

Section: Nsga-ii (Non-dominated Sorting Genetic Algorithm Ii)mentioning

confidence: 85%

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Ruane

Walsh

Cosgrove

2022

Acta Tech. Jaurinensis

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Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.

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Section: Nsga-ii (Non-dominated Sorting Genetic Algorithm Ii)mentioning

confidence: 85%

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Ruane

Walsh

Cosgrove

2022

Acta Tech. Jaurinensis

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“…Several approaches have been used to model multiobjective optimization in business processes. An evolutionary algorithm-based optimization framework is proposed in [6], employing the well-known Non-Dominated Sorting Genetic Algorithm II (NS-GAII) [7], to generate optimized business processes by considering search space, fitness function, and optimization constraints. Evolutionary algorithms are also used in [8] to generate optimized business processes, based on predefined requirements, a task library, and input/output resources.…”

Section: Related Workmentioning

confidence: 99%

Pareto-Optimal Trace Generation from Declarative Process Models

Diaz,

López,

Quesada

et al. 2024

Lecture Notes in Business Information Processing

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Declarative process models (DPMs) enable the description of business process models with a high level of flexibility by being able to describe the constraints that compliant traces must abide by. In this way, a well-formed declarative specification generates a family of compliant traces. However, little is known about the difference between different compliant traces, as the only criterion used for comparison is satisfiability. In particular, we believe that not all compliant traces are alike: some might be sub-optimal in their resource usage. In this work, we would like to support users of DPMs in the selection of compliant and optimal traces. In particular, we use Dynamic Condition Response (DCR) graphs as our language to represent DPMs, extending it with a parametric definition of costs linked to events. Multiple types of cost imply that different traces might be optimal, each according to a different cost dimension. We encode costeffective finite trace generation as a Constraint Optimisation Problem (COP) and showcase the feasibility of the implementation via an implementation in MiniZinc. Our initial benchmarks suggest that the implementation is capable of providing answers efficiently for processes of varying size, number of constraints, and trace length.

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“…Most of the algorithms that are considered evolutionary algorithms have limits within their execution related to the number of evaluations or generations that will take part in their execution cycle or with the population size that they manage. An interesting approach focusing on the importance of the population initialization on the solutions resulting from an evolutionary multiobjective optimization (EMOO) is presented in [32]. Mahammed et al propose the use of a multicriteria decision analysis method and an EMOO framework to sort or classify the initial population.…”

Section: Related Workmentioning

confidence: 99%

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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Evolutionary Business Process Optimization using a Multiple-Criteria Decision Analysis method

Cited by 7 publications

References 14 publications

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Pareto-Optimal Trace Generation from Declarative Process Models

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

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