rss_2.0Sciendo RSS Feed for Foundations of Computing and Decision Scienceshttps://sciendo.com/journal/FCDShttps://www.sciendo.comhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/6471cc98215d2f6c89db1461/cover-image.jpghttps://sciendo.com/journal/FCDS140216https://sciendo.com/article/10.2478/fcds-2024-0009<abstract> <title style='display:none'>Abstract</title> <p>For long-term success, organizations and manufacturing companies must exploit the potential strengths of collective decision making in maintenance management. The maintenance strategy selection issue has been studied in a single decision-maker framework for a long time. This research is one of the first attempts at dealing with the enhancement of maintenance management through the participation of stakeholders in the decision making process. In this context, the author introduces a participatory multi criteria decision model that combines Borda count and PROMETHEE methodology to select the most appropriate maintenance strategy; in accordance with the decision makers’ preferences on a set of strategies evaluated according to conflicting criteria. Therefore, the PROMETHEE II method is used to manage the individual decisions of each stakeholder, while the Borda count is in charge of collectively selecting the best maintenance strategy, taking as a starting point stakeholder’s preferences being established thanks to PROMETHEE II. In the same context, the proposed model was applied to a real scenario: a textile company, and can be easily replicated in other industries.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00092024-05-26T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0010<abstract> <title style='display:none'>Abstract</title> <p>This bibliometric study aims to summarize the academic landscape of non-probabilistic data research, based on an examination of scientific output indexed in Web of Science and Scopus databases. It employs multiple methods to analyse and describe the collected corpus, including co-authorship and keyword co-occurrence networks to investigate patterns of collaboration and predominant research themes. Co-authorship analysis identified several robust research clusters, while keyword later spotlighted key thematic areas in the field. Countries, types of documents, categories, year of publication, citations and other metrics were also produced, and implications discussed. The findings present a structured overview of the non-probabilistic data research landscape, delineating the research trends, prominent authors, and emerging themes.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00102024-05-26T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0008<abstract> <title style='display:none'>Abstract</title> <p>To address the issue of insufficient accuracy in consumer recommendation systems, a new biased network inference algorithm is proposed based on traditional network inference algorithms. This new network inference algorithm can significantly improve the resource allocation ability of the original one, thereby improving recommendation performance. Then, the performance of this algorithm is verified through comparative experiments with network-based inference algorithms, network inference algorithms with initial resource optimization, and heterogeneous network inference algorithms. The results showed that the accuracy of the new network inference algorithm was 24.5%, which was superior to traditional one. In terms of system performance testing, the recommendation hit rate of the new network inference algorithm increased by 13.97%, which was superior to the other three comparative algorithms. The experimental results indicated that a novel network inference algorithm with bias can improve the performance of consumer recommendation systems, providing new ideas for improving the performance of consumer recommendation systems.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00082024-05-26T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0011<abstract> <title style='display:none'>Abstract</title> <p>Recently, numerous machine learning (ML) techniques have been applied to address database performance management problems, including cardinality estimation, cost modeling, optimal join order prediction, hint generation, etc. In this paper, we focus on query optimizer hints employed by users in their queries in order to mask some Query Optimizer deficiencies. We treat the query optimizer hints, bound to previous queries, as significant additional query metadata and learn to automatically predict which new queries will pose similar performance challenges and should therefore also be supported by query optimizer hints. To validate our approach, we have performed a number of experiments using real-life SQL workloads and we achieved promising results.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00112024-05-26T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0006<abstract> <title style='display:none'>Abstract</title> <p>In this paper, we study eight single-machine scheduling problems with rejection and position-dependent parameters. We consider two position-dependent parameters as follows: (1) position-dependent weights and (2) position-dependent processing times. In addition, we also introduce a weight-modifying activity or a rate-modifying activity into our problems. In the first six problems, the task is to minimize the sum of the total weighted completion time of accepted jobs and the total rejection cost of rejected jobs. We show that all six problems can be solved in polynomial time. In the last two problems, the task is to minimize the total weighted completion time of accepted jobs under the constraint that the total rejection cost of rejected jobs can not exceed a given upper bound. We show that these two problems are binary NP-hard and each problem admits an FPTAS.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00062024-02-16T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0005<abstract> <title style='display:none'>Abstract</title> <p>The online 3D bin packing problem(3D-BPP) is widely used in the logistics industry and is of great practical significance for promoting the intelligent transformation of the industry. The heuristic algorithm relies too much on manual experience to formulate more perfect packing rules. In recent years, many scholars solve 3D-BPP via deep reinforcement learning(DRL) algorithms. However, they ignore many skills used in manual packing, one of the most important skill is workers put the item aside if the item is packed improperly. Inspired by this skill, we propose a DRL algorithm with a buffer zone. Firstly, we define the wasted space and the buffer zone. And then, we integrate them into the DRL algorithm framework. Importantly, we compare the bin utilization with di erent thresholds of wasted space and di erent buffer zone sizes. Experimental results show that our algorithm outperforms existing heuristic algorithms and DRL algorithms.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00052024-02-16T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0007<abstract> <title style='display:none'>Abstract</title> <p>Automatic crack detection in construction facilities is a challenging yet crucial task. However, existing deep learning (DL)-based semantic segmentation methods for this field are based on fully supervised learning models and pixel-level manual annotation, which are time-consuming and labor-intensive. To solve this problem, this paper proposes a novel crack semantic segmentation network using weakly supervised approach and mixed-label training strategy. Firstly, an image patch-level classifier of crack is trained to generate a coarse localization map for automatic pseudo-labeling of cracks combined with a thresholding-based method. Then, we integrated the pseudo-annotated with manual-annotated samples with a ratio of 4:1 to train the crack segmentation network with a mixed-label training strategy, in which the manual labels were assigned with a higher weight value. The experimental data on two public datasets demonstrate that our proposed method achieves a comparable accuracy with the fully supervised methods, reducing over 65% of the manual annotation workload.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00072024-02-16T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0003<abstract> <title style='display:none'>Abstract</title> <p>It is well known that airport security is an important component of homeland security, since airports are highly vulnerable to terrorist attacks. In order to improve the overall security of the domestic airport network, some work studied the budget allocation of domestic airport network protection. They established a minimax optimization model and designed an exact cutting plane algorithm to solve the problem. However, the exact algorithm can not solve large-scale problems in an acceptable time. Hence, this paper designs a heuristic cutting plane algorithm for solving budget allocation of large-scale domestic airport network protection. Finally, numerical experiments are carried out to demonstrate the feasibility and effectiveness of the new algorithm.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00032024-02-16T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0001<abstract> <title style='display:none'>Abstract</title> <p>This paper is devoted to the study of a multi-criteria scheduling problem on unrelated processors with machines’ learning effect, with the goal of minimizing makespan, machine cost and maximal flow-time simultaneously, which is an NP-hard problem. An improved particle swarm optimization algorithm equipped with the overloaded operators, as well as a procedure of Levy flight, is proposed to generate the Pareto-optimal solutions. The experimental results show that the Levy flight strategy can effectively improve the performance of the algorithm, which can generate more non-dominated solutions, and slightly reduce the execution time of the process.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00012024-02-16T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0004<abstract> <title style='display:none'>Abstract</title> <p>Matrix completion is widely used in information science fields such as machine learning and image processing. The alternating direction method of multipliers (ADMM), due to its ability to utilize the separable structure of the objective function, has become an extremely popular approach for solving this problem. But its subproblems can be computationally demanding. In order to improve computational e ciency, for large scale matrix completion problems, this paper proposes an improved ADMM by using convex combination technique. Under certain assumptions, the global convergence of the new algorithm is proved. Finally, we demonstrate the performance of the proposed algorithms via numerical experiments.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00042024-02-16T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2024-0002<abstract> <title style='display:none'>Abstract</title> <p>Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool systems and further threaten their security. In this paper, we propose to use latent space protect image classification. Specifically, we train a feature searching network to make up the difference between adversarial examples and clean examples with label guided loss function. We name it DefenseFea(input transformation based defense with label guided loss function), experimental result shows that DefenseFea can improve the rate of adversarial examples that achieved a success rate of about 99% on a specific set of 5000 images from ILSVRC 2012. This study plays a positive role in the further investigation of the relationship between adversarial examples and clean examples.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2024-00022024-02-16T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0018<abstract> <title style='display:none'>Abstract</title> <p>The definition of architecture is a crucial task in software development, where the architect is responsible for making the right decisions to meet specific functional and quality requirements. These architectural design decisions form the foundation that shapes the arrangement of elements within a system. Unfortunately, these decisions are often poorly documented, implicit in various artifacts, or inadequately updated, leading to negative consequences on the maintainability of a system and resulting in rework and cost overruns. The objective of this systematic mapping study is to comprehend the current state regarding approaches for traceability of architectural design decisions and how these decisions are linked with the different artifacts used in software development. To achieve this, an information extraction protocol is followed, utilizing databases with search strings, inclusion, and exclusion criteria. The findings demonstrate that this knowledge is highly relevant; however, it is rarely explicitly documented. As a result, most works propose diverse approaches to extract this knowledge from existing technical documentation, commonly used tools, and other sources of product and process information. In contrast, it is evident that there is no standard for documenting design decisions, leading each author to present a subjective version of what is important and where to trace these decisions. This suggests that there is still a significant amount of research to be conducted regarding the traceability of these architectural design decisions and their connection with software artifacts. Such research could lead to intriguing new proposals for investigation.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00182023-12-21T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0021<abstract> <title style='display:none'>Abstract</title> <p>DNA computing is a highly interdisciplinary field which combines molecular operations with theoretical algorithm design. A number of algorithms have been demonstrated in DNA computing, but to date network flow problems have not been studied. We aim to provide an approach to calculate the value of the maximum flow in networks by encoding the mathematical problem in DNA molecules and by using molecular biology techniques to manipulate the DNA. We present results which demonstrate that the algorithm works for an example network problem.</p> <p>This paper presents the first application of DNA computing to network-flow problems. The presented algorithm has a linear time complexity where the calculation itself is done in a constant number of steps.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00212023-12-21T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0019<abstract> <title style='display:none'>Abstract</title> <p>Depending on the legal system, judges may have varying degrees of discretion in determining the type and extent of sentence that can be imposed for a particular offence. Nevertheless, it appears that even in systems traditionally considered discretionary, accepted patterns play a significant role in determining penalties, and judges utilize merely a limited spectrum of potential penalties in repetitive cases. Confirmation of the predictability of sentencing in certain categories of cases facilitates the possibility of automation. Utilising a computer program to assist judges in sentencing proposals based on input is feasible. This program can reflect the standard practice of sentencing penalties and punitive measures in a particular category of cases or rectify it, depending on the adopted sentencing policy. The objective of the article is to present findings from research that investigated whether a specific relation shapes the dimension of penalties and penal measures for cases concerning driving under the influence of alcohol in Poland, in the context of possible automation of the sentencing process. Another aim of this study is to provide an example of a straightforward mathematical recommendation model that tries to reflect both the discovered correlations in the data and the presumed intentions of legislators.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00192023-12-21T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0020<abstract> <title style='display:none'>Abstract</title> <p>A method of parallelizing the process of solving the traveling salesman problem is suggested, where the solver is a heuristic algorithm. The traveling salesman problem parallelization is fulfilled by clustering the nodes into a given number of groups. Every group (cluster) is an open-loop subproblem that can be solved independently of other subproblems. Then the solutions of the respective subproblems are aggregated into a closed loop route being an approximate solution to the initial traveling salesman problem. The clusters should be enumerated such that then the connection of two “neighboring” subproblems (with successive numbers) be as short as possible. For this, the destination nodes of the open-loop subproblems are selected farthest from the depot and closest to the starting node for the subsequent subproblem. The initial set of nodes can be clustered manually by covering them with a finite regular-polygon mesh having the required number of cells. The efficiency of the parallelization is increased by solving all the subproblems in parallel, but the problem should be at least of 1000 nodes or so. Then, having no more than a few hundred nodes in a cluster, the genetic algorithm is especially efficient by executing all the routine calculations during every iteration whose duration becomes shorter.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00202023-12-21T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0017<abstract> <title style='display:none'>Abstract</title> <p>Adequate and desirable connections between suppliers and customers necessitate an appropriate flow of information. Therefore, a promising and proper data collaboration in the supply chain is of tremendous significance. Thus, the study’s main objective is to provide multiple objective programming models under uncertain conditions to assess the performance of suppliers. To meet that aim, a case study for the reliability assessment of the presented model is carried out. That section is associated with supply chain visibility (SCV). Likewise, the likelihood of unpredicted and undesirable incidents involving supply chain risk (SCR) is taken into consideration. The intimate relation between visibility and risk of the supply chain is deemed efficient for the performance of the supply chain. Incoherence in maximization and minimization of SCR and SCV and other factors, including costs, capacity, or demand, necessitates multiple objective programming models to assess suppliers’ performance to accomplish the before-mentioned aims. The study’s results indicate the high reliability of the proposed model. Besides, the numeral results reveal that decision-makers in selecting suppliers mainly decrease SCR and then attempt to enhance SCV. In conclusion, the provided model in the study can be a desirable model for analyzing and estimating supplier performance with SCR and SCV simultaneously.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00172023-10-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0012<abstract> <title style='display:none'>Abstract</title> <p>In this paper, we have done a rapid and very simple algorithm that resolves the multiple objective combinatorial optimization problem. This, by determining a basic optimal solution, which is a strong spanning tree constructed, according to a well-chosen criterion. Consequently, our algorithm uses notions of Bellman’s algorithm to determine the best path of the network, and Ford Fulkerson’s algorithm to maximise the flow value. The Simplex Network Method that permits to reach the optimality conditions manipulates the two algorithms. In short, the interest of our work is the optimization of many criteria taking into account the strong spanning tree, which represents the central angular stone of the network. To illustrate that, we propose to optimize a bi-objective distribution problem.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00122023-10-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0011<abstract> <title style='display:none'>Abstract</title> <p>This paper proposes a mathematical model of Sustainable Closed-Loop Supply Chain Networks (SCLSCNs). When an outbreak occurs, environmental, economic, and social aspects can be traded off. A novelty aspect of this paper is its emphasis on hygiene costs. As well as healthcare education, prevention, and control of COVID-19, this model offers job opportunities related to COVID-19 pandemic. COVID-19 damages lead to lost days each year, which is one of the negative social aspects of this model. COVID-19 was associated with two environmental novelties in this study. positive and negative effects of COVID-19 can be observed in the environmental context. As a result, there has been an increase in medical waste disposal and plastic waste disposal. Multi-objective mathematical modeling whit Weighted Tchebycheff method scalarization. In this process, the software Lingo is used. The COVID-19 pandemic still has a lot of research gaps because it’s a new disease. An SC model that is sustainable and hygienic will be developed to fill this gap in the COVID-19 condition disaster. Our new indicator of sustainability is demonstrated using a mixed-integer programming model with COVID-19-related issues in a Closed-Loop Supply Chain (CLSC) overview.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00112023-10-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0016<abstract> <title style='display:none'>Abstract</title> <p>This study examines the issue of distribution network design in the supply chain system. There are many production factories and distribution warehouses in this issue. The most efficient strategy for distributing the product from the factory to the warehouse and from the warehouse to the customer is determined by solving this model. This model combines location problems with and without capacity limits to study a particular location problem. In this system, the cost of production and maintenance of the product in the factory and warehouse is a function of its output. This increases capacity without additional costs, and ultimately does not lose customers. This algorithm is a population-based, innovative method that systematically combines answers to obtain the most accurate answer considering quality and diversity. A two-phase recursive algorithm based on a scattered object has been developed to solve this model. Numerical results show the efficiency and effectiveness of this two-phase algorithm for problems of different sizes.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00162023-10-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/fcds-2023-0014<abstract> <title style='display:none'>Abstract</title> <p>Locating facilities such as factories or warehouses is an important and strategic decision for any organization. Transportation costs, which often form a significant part of the price of goods offered, are a function of the location of the plans. To determine the optimal location of these designs, various methods have been proposed so far, which are generally definite (non-random). The main aim of the study, while introducing these specific algorithms, is to suggest a stochastic model of the location problem based on the existing models, in which random programming, as well as programming with random constraints are utilized. To do so, utilizing programming with random constraints, the stochastic model is transformed into a specific model that can be solved by using the latest algorithms or standard programming methods. Based on the results acquired, this proposed model permits us to attain more realistic solutions considering the random nature of demand. Furthermore, it helps attain this aim by considering other characteristics of the environment and the feedback between them.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/fcds-2023-00142023-10-05T00:00:00.000+00:00en-us-1