1. Accuracy is not enough: optimizing for a fault detection delayMatej Šprogar, Domen Verber, 2023, original scientific article Abstract: This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so. Keywords: artificial neural networks, deep learning, fault detection, accuracy, multi-objective optimization Published in DKUM: 30.11.2023; Views: 41; Downloads: 2
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2. An efficient metaheuristic algorithm for job shop scheduling in a dynamic environmentHankun Zhang, Borut Buchmeister, Xueyan Li, Robert Ojsteršek, 2023, original scientific article Keywords: metaheuristic algorithm, improved multi-phase particle swarm optimization, cellular neighbor network, dynamic job shop scheduling, simulation modelling Published in DKUM: 19.05.2023; Views: 237; Downloads: 17
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3. Multi-objective optimization algorithms with the island metaheuristic for effective project management problem solvingChristina Brester, Ivan Ryzhikov, Eugene Semenkin, 2017, original scientific article Abstract: Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks.
Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques.
Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa.
Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management. Keywords: 0-1 multi-objective constrained knapsack problem, project management portfolio problem, multi-objective evolution-based optimization algorithms, collaborative and cooperative meta-heuristics Published in DKUM: 04.05.2018; Views: 1312; Downloads: 289
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4. Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysisFranko Puh, Zoran Jurković, Mladen Perinic, Miran Brezočnik, Stipo Buljan, 2016, original scientific article Abstract: Optimization of machining processes is essential for achieving of higher productivity and high quality products in order to remain competitive. This study investigates multi-objective optimization of turning process for an optimal parametric combination to provide the minimum surface roughness (Ra) with the maximum material-removal rate (MRR) using the Grey–Based Taguchi method. Turning parameters considered are cutting speed, feed rate and depth of cut. Nine experimental runs based on Taguchi’s L9 (34) orthogonal array were performed followed by the Grey relational analysis to solve the multi- response optimization problem. Based on the Grey relational grade value, optimum levels of parameters have been identified. The significance of parameters on overall quality characteristics of the cutting process has been evaluated by the analysis of variance (ANOVA). The optimal parameter values obtained during the study have been validated by confirmation experiment. Keywords: ANOVA, Grey relational analysis, multi-objective optimization, Taguchi method, turning Published in DKUM: 12.07.2017; Views: 1090; Downloads: 388
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5. Optimal robust motion controller design using multi-objective genetic algorithmAndrej Sarjaš, Rajko Svečko, Amor Chowdhury, 2014, original scientific article Abstract: This paper describes the use of a multi-objective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with non-negativity conditions. Regional pole placement method is presented with the aims of controllers% structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multi-objective function is composed of different unrelated criteria such as, robust stability, controllers' stability and time performance indexes of closed loops. The design of controllers and multi-objective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm - Differential evolution. Keywords: disturbance observer, DOB, uncertainty systems, optimal robust control, multi-objective optimization, differential evolution Published in DKUM: 15.06.2017; Views: 1259; Downloads: 348
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6. Multi-objective optimization of automated storage and retrieval systemsTone Lerher, Matjaž Šraml, Matej Borovinšek, Iztok Potrč, 2013, original scientific article Abstract: The multi-objective optimization of automated warehouse is discussed and evaluated in present paper. Since most of researchers in material handling community had performed optimization of decision variables with single objective function only (usually named with minimum travel time, maximum throughput capacity, minimum cost, etc.), the multi-objective optimization (time-cost-quality) will be presented in present research. For the optimization of decision variables in objective functions, the method with genetic algorithms is used. The main objective of our contribution is to determine the performance of the system according to the multi-objective optimization technique. Keywords: automated warehouses, performance, multi-objective function, optimization Published in DKUM: 10.07.2015; Views: 960; Downloads: 67
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8. A multi-objective optimization approach for designing automated warehousesTone Lerher, Matej Borovinšek, Iztok Potrč, Matjaž Šraml, 2012, published scientific conference contribution Abstract: A multi objective optimization of automated warehouses is discussed and evaluated in present paper. Since most of researchers in material handling community had performed optimization of decision variables with single objective function only (usually named with minimum travel time, maximum throughput capacity, minimum cost, etc.), the multi objective optimization (travel time - cost - quality) will be presented. For the optimization of decision variables in objective functions, the method with genetic algorithms is used. To find the Pareto optimal solutions, the NSGA II genetic algorithm was used. The main objective of our contribution is to determine the performance of the system according to the multi objective optimization technique. The results of the proposed model could be useful tool for the warehouse designer in the early stage of warehouse design. Keywords: automated warehouses, material handling, multi objective optimization Published in DKUM: 10.07.2015; Views: 1118; Downloads: 32
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