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  • 1.
    Barrera Diaz, Carlos Alberto
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, Sweden.
    Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach2021In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 144195-144210Article in journal (Refereed)
    Abstract [en]

    In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.

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  • 2.
    Beheshtinia, Mohammad Ali
    et al.
    Industrial Engineering Department, Faculty of Engineering, Semnan University, Iran.
    Sedady, Fatima
    Industrial Engineering Department, Faculty of Engineering, Semnan University, Iran.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, Sweden.
    Ghobakhloo, Morteza
    Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, Sweden ; School of Economics and Business, Kaunas University of Technology, Lithuania.
    Iranmanesh, Mohammad
    La Trobe Business School, La Trobe University, Melbourne, Victoria, Australia.
    A fuzzy three-dimensional house of quality to integrate and coordinate departments’ activities in organizations2023In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed)
    Abstract [en]

    This study aims to introduce a method to integrate and coordinate departments’ activities to enhance the service quality of organizations using Quality Function Deployment (QFD). To this purpose, the classical two-dimensional House Of Quality (HOQ) matrix is changed to a three-dimensional form (3D-HOQ). The 3D-HOQ is applied to the marketing and Human Resources (HR) departments of a bank to determine customers’ and employees’ demands, respectively. The 3D-HOQ is also employed to provide a unique list of technical requirements to satisfy the identified demands. Obtaining a unique list of technical requirements with the cooperation of both departments reduces the inconsistency between departments, saves cost and time by preventing reworks and parallel works, and increases the organization’s efficiency. Moreover, 3D-HOQ is combined with the SERVQUAL technique and fuzzy theory to determine the weight of obtained technical requirements. The study is conducted in four main steps, (1) identifying the customers’ and employees’ demands, (2) identifying the technical requirements for simultaneous satisfaction of both customers’ and employees’ demands, (3) determining the relationships between the technical requirements and the identified demands, and (4) prioritizing technical requirements. Applying the 3D-HOQ resulted in identifying 30 customers’ demands, 30 employees’ demands, and 50 technical requirements. The study results show that "using new banking technologies" has the highest weight among the customers’ demands, and "job security" has been found to have the highest weight among employees’ demands. Moreover, "Intra-organizational processes automation" has been identified as the technical requirement with the highest weight.

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  • 3.
    Da Lio, Mauro
    et al.
    Department of Industrial Engineering, University of Trento, Italy.
    Donà, Riccardo
    Department of Industrial Engineering, University of Trento, Italy.
    Papini, Gastone Pietro Rosati
    Department of Industrial Engineering, University of Trento, Italy.
    Biral, Francesco
    Department of Industrial Engineering, University of Trento, Italy.
    Svensson, Henrik
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    A Mental Simulation Approach for Learning Neural-Network Predictive Control (in Self-Driving Cars)2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 192041-192064Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach to learning predictive motor control via mental simulations. The method, inspired by learning via mental imagery in natural Cognition, develops in two phases: first, the learning of predictive models based on data recorded in the interaction with the environment; then, at a deferred time, the synthesis of inverse models via offline episodic simulations. Parallelism with human-engineered control-theoretic workflow (mathematical modeling the direct dynamics followed by optimal control inversion) is established. Compared to the latter human-directed synthesis, the mental simulation approach increases autonomy: a robotic agent can learn predictive models and synthesize inverse ones with a large degree of independence. Human modeling is still needed but limited to providing efficient templates for the forward and inverse neural networks and a few other directives. One could consider these templates as the efficient brain network typologies that evolution produced to permit live beings quickly and efficiently learning. The structure of the neural networks both forward and inverse ones; is made of interpretable local models which follows the cerebellar organization (and are also similar to local model approaches known in the literature). We demonstrate the learning of a first-round model (contrasted to Model Predictive Control) for lateral vehicle dynamics. Then, we demonstrate a second learning iteration, where the forward/inverse neural models are significantly improved.

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  • 4.
    Danielsson, Oscar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Evaluation Framework for Augmented Reality Smart Glasses as Assembly Operator Support: Case Study of Tool Implementation2021In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 104904-104914Article in journal (Refereed)
    Abstract [en]

    Augmented reality smart glasses (ARSG) have been identified as relevant support tools for the Operator 4.0 paradigm. Although ARSG are starting to be used in industry, their use is not yet widespread. A previously developed online tool based on a framework for evaluating ARSG as assembly operator support is iteratively improved in this paper with expanded functionality. The added functionality consists of practical recommendations for implementing ARSG in production. These recommendations were produced with the help of five focus groups of industrial representatives working in production. The recommendations were evaluated using case studies at three different companies. The recommendations were found to be detailed and a good support for the process of considering ARSG integration into production. The companies overall found the tool and its recommendations to be relevant and correct for their cases.

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  • 5.
    Fathi, Masood
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Nourmohammadi, Amir
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    An optimization model for balancing assembly lines with stochastic task times and zoning constraints2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 32537-32550, article id 8663269Article in journal (Refereed)
    Abstract [en]

    This study aims to bridge the gap between theory and practice by addressing a real-world assembly line balancing problem (ALBP) where task times are stochastic and there are zoning constraints in addition to the commonly known ALBP constraints. A mixed integer programming (MIP) model is proposed for each of the straight and U-shaped assembly line configurations. The primary objective in both cases is to minimize the number of stations; minimizing the maximum of stations’ mean time and the stations’ time variance are considered secondary objectives. Four different scenarios are discussed for each model, with differences in the objective function. The models are validated by solving a real case taken from an automobile manufacturing company and some standard test problems available in the literature. The results indicate that both models are able to provide optimum solutions for problems of different sizes. The technique for order preference by similarity to ideal solution (TOPSIS) is used to create reliable comparisons of the different scenarios and valid analysis of the results. Finally, some insights regarding the selection of straight and U-shaped layouts are provided.

  • 6.
    Karlsson, Alexander
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Bekar, Ebru Turanoglu
    Deparment of Industrial and Materials Science, Chalmers University of Technology, Gothenburg, Sweden.
    Skoogh, Anders
    Deparment of Industrial and Materials Science, Chalmers University of Technology, Gothenburg, Sweden.
    Multi-Machine Gaussian Topic Modeling for Predictive Maintenance2021In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 100063-100080Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behaviors. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems.

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  • 7.
    Karlsson, Ingemar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Bandaru, Sunith
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Department of Civil and Industrial Engineering, Uppsala University, Swede.
    Online Knowledge Extraction and Preference Guided Multi-Objective Optimization in Manufacturing2021In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 145382-145396Article in journal (Refereed)
    Abstract [en]

    The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker's preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.

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  • 8.
    Nourmohammadi, Amir
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Zandieh, Mostafa
    Department of Industrial Management, Management and Accounting Faculty, SBU, G.C., Tehran, Iran.
    Ghobakhloo, Morteza
    Department of Industrial Engineering, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran / Modern Technology Development and Implementation Research Center, University of Hormozgan, Bandar Abbas, Iran.
    A water-flow like algorithm for solving U-shaped assembly line balancing problems2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 129824-129833Article in journal (Refereed)
    Abstract [en]

    The problem of assigning assembly tasks to the stations arranged along a material handling device is known as assembly line balancing. This paper aims to address the U-shaped assembly line balancing problem (UALBP) which arises when a U-shaped assembly line has to be configured. It is widely known that this problem is NP-hard. Accordingly, different meta-heuristics based on a single solution (such as Simulated Annealing) or a population of solutions (such as Genetic Algorithms) have been proposed in the literature. Meanwhile, it has been argued that either of these meta-heuristics with a fixed number of solutions cannot maintain efficient search progress and thus can lead to premature convergence. Thus, this study aims at adopting a novel meta-heuristic algorithm with dynamic population sizes, namely Water Flow-like Algorithm (WFA), inspired by the behaviour of water flows in nature, to address the UALBP. The line efficiency and variation of workload are considered as the primary and the secondary objective, to be optimized, respectively. To verify the efficiency and robustness of the proposed WFA, a real case study taken from an automobile manufacturer as well as a set of standard problems are solved and the results compared with the existing approaches in the literature. The computational results demonstrate the superiority of the WFA, particularly in addressing medium to large-sized problems.

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  • 9.
    Ruiz Zúñiga, Enrique
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Urenda Moris, Matías
    Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Rubio-Romero, Juan Carlos
    School of Industrial Engineering, University of Malaga, Campus of Teatinos, 29071 Malaga, Spain.
    A Simulation-Based Optimization Methodology for Facility Layout Design in Manufacturing2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 163818-163828Article in journal (Refereed)
    Abstract [en]

    Optimizing production systems is urgent and indispensable if companies are to cope with global competition and a move from mass production to mass customization. The urgency of this need is more obvious in old production plants with a history of modifications, expansions, and adaptations in their production facilities. It is common to find complex, intricate and inefficient systems of material and product flows as a result of poor production facility layout. Several approaches can be used to support the design of optimal facility layouts. However, there is a lack of a suitable generic methodology for designing such layouts. Additionally, there has been little focus on the data and resources required, or on how simulation and optimization can support the design of optimal facilities. To overcome these deficiencies, this paper studies the integration of simulation and optimization for the design and improvement of facility layouts taking into account production and logistics constraints. The paper includes a generic perspective and a detailed implementation. The proposed methodology is evaluated in two case studies and by drawing on the principles and tools of the functional resonance analysis method. This method analyzes the implementation order and variability of a group of processes that can lead to unwanted outcomes. The results can provide managers and other stakeholders with a methodology that adequately considers production and logistics constraints when seeking an optimized facility layout design.

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  • 10.
    Shah, Syed Muhammad Ali
    et al.
    Swedish Institute of Computer Science.
    Sundmark, Daniel
    Swedish Institute of Computer Science / Mälardalen University, Sw eden.
    Lindström, Birgitta
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Andler, Sten F.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Robustness Testing of Embedded Software Systems: An Industrial Interview Study2016In: IEEE Access, E-ISSN 2169-3536, Vol. 4, p. 1859-1871, article id 7438745Article in journal (Refereed)
    Abstract [en]

    Embedded software is at the core of current and future telecommunication, automotive, multimedia, and industrial automation systems. The success of practically any industrial application depends on the embedded software system’s dependability, and one method to verify the dependability of a system is testing its robustness. The motivation behind this study is to provide a knowledge base of the state of the practice in robustness testing of embedded software systems and to compare this to the state of the art. We have gathered information on the state of the practice in robustness testing from seven different industrial domains (telecommunication, automotive, multimedia, critical infrastructure, aerospace, consumer products, and banking) by conducting thirteen semi-structured interviews. We investigate different aspects of robustness testing, such as the general view of robustness, relation to requirements engineering and design, test execution, failures, and tools. We highlight knowledge from the state of the practice of robustness testing of embedded software systems. We found different robustness testing practices that have not been previously described. Our study shows that the state of the practice, when it comes to robustness testing, differs between organizations and is quite different from the state of the art described in the scientific literature. For example, methods commonly described in the literature (e.g., the fuzzy approach) are not used in the organizations we studied. Instead, the interviewees described several ad-hoc approaches that take specific scenarios into account (e.g., power failure or overload). Other differences we found concern classification of robustness failures, the hypothesized root causes of robustness failures, and the types of tools used for robustness testing. The study is a first step in capturing the state of the practice of robustness testing of embedded software systems. The results can be used by both researchers and- practitioners. Researchers can use our findings to understand the gap between the state of the art and the state of the practice and develop their studies to fill this gap. Practitioners can also learn from this knowledge base regarding how they can improve their practice and acquire other practices.

  • 11.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Danielsson, Oscar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gustavsson, Patrik
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Augmented Reality Smart Glasses in the Smart Factory: Product Evaluation Guidelines and Review of Available Products2017In: IEEE Access, E-ISSN 2169-3536, Vol. 5, p. 9118-9130, article id 7927376Article in journal (Refereed)
    Abstract [en]

    Augmented reality smart glasses (ARSG) are increasingly popular and have been identified as a vital technology supporting shop-floor operators in the smart factories of the future. By improving our knowledge of how to efficiently evaluate and select ARSG for the shop-floor context, this paper aims to facilitate and accelerate the adoption of ARSG by the manufacturing industry. The market for ARSG has exploded in recent years, and the large variety of products to select from makes it not only difficult but also time consuming to identify the best alternative. To address this problem, this paper presents an efficient step-by-step process for evaluating ARSG, including concrete guidelines as to what parameters to consider and their recommended minimum values. Using the suggested evaluation process, manufacturing companies can quickly make optimal decisions about what products to implement on their shop floors. The paper demonstrates the evaluation process in practice, presenting a comprehensive review of currently available products along with a recommended best buy. The paper also identifies and discusses topics meriting research attention to ensure that ARSG are successfully implemented on the industrial shop floor.

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