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  • 1.
    Amouzgar, Kaveh
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Uppsala University.
    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. Division of Industrial Engineering and Management, Uppsala University.
    Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm2021In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, no 12, p. 3572-3590Article in journal (Refereed)
    Abstract [en]

    Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices. 

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  • 2.
    Barrera Diaz, Carlos Alberto
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Del Riego Navarro, Andres
    University of Skövde, School of Engineering Science.
    Rico Perez, Alvaro
    University of Skövde, School of Engineering Science.
    Nourmohammadi, Amir
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Availability Analysis of Reconfigurable Manufacturing System Using Simulation-Based Multi-Objective Optimization2022In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 369-379Conference paper (Refereed)
    Abstract [en]

    Nowadays, manufacturing companies face an increasing number of challenges that can cause unpredictable market changes. These challenges are derived from a fiercely competitive market. These challenges create unforeseen variations and uncertainties, including new regional requirements or regulations, new technologies and materials, new market segments, increasing demand for new product features, etc. To cope with the challenges above, companies must reinvent themselves and design manufacturing systems that seek to produce quality products while responding to the changes faced. These capabilities are encompassed in Reconfigurable Manufacturing Systems (RMS), capable of dealing with uncertainties quickly and economically. The availability of RMS is a crucial factor in establishing the production capacity of a system that considers all events that could interrupt the planned production. The impact of the availability in RMS is influenced by the configuration of the systems, including the number of resources used. This paper presents a case study in which a simulation-based multi-objective optimization (SMO) method is used to find machines’ optimal task allocation and assignment to workstations under different scenarios of availability. It has been shown that considering the availability of the machines affects the optimal configuration, including the number of resources needed, such as machines and buffers. This study demonstrates the importance of the availability consideration during the design of RMS.

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  • 3.
    Barrera Diaz, Carlos Alberto
    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.
    Smedberg, Henrik
    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.
    An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems2023In: Mathematics, ISSN 2227-7390, Vol. 11, no 6, article id 1527Article in journal (Refereed)
    Abstract [en]

    In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

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  • 4.
    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.
    Ghobakhloo, Morteza
    Department of Industrial Engineering, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran.
    Yousefi, Milad
    Department of Industrial Engineering and Transportation, Universidade Federal do Rio Grande do Sul—UFRGS, Porto Alegre, RS, Brazil.
    Production Sustainability via Supermarket Location Optimization in Assembly Lines2020In: Sustainability, E-ISSN 2071-1050, Vol. 12, no 11, p. 1-16, article id 4728Article in journal (Refereed)
    Abstract [en]

    Manufacturers worldwide are nowadays in pursuit of sustainability. In the Industry 4.0 era, it is a common practice to implement decentralized logistics areas, known as supermarkets, to achieve production sustainability via Just-in-Time material delivery at assembly lines. In this environment, manufacturers are commonly struggling with the Supermarket Location Problem (SLP), striving to efficiently decide on the number and location of supermarkets to minimize the logistics cost. To address this prevalent issue, this paper proposed a Simulated Annealing (SA) algorithm for minimizing the supermarket cost, via optimally locating supermarkets in assembly lines. The efficiency of the SA algorithm was tested by solving a set of test problems. In doing so, a holistic performance index, namely the total cost of supermarkets, was developed that included both shipment cost and the installation cost across the assembly line. The effect of workload balancing on the supermarket cost was also investigated in this study. For this purpose, the SLP was solved both before and after balancing the workload. The results of the comparison revealed that workload balancing could significantly reduce the total supermarket cost and contribute to the overall production and economic sustainability. It was also observed that the optimization of material shipment cost across the assembly line is the most influencing factor in reducing the total supermarket cost.

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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
    Department of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Assembly Line Balancing Type-E with Technological Requirement: A Mathematical Model2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, Vol. 8, p. 183-188Conference paper (Refereed)
    Abstract [en]

    This study is motivated by a real-world assembly line in an automotive manufacturing company and it addresses the simple assembly line balancing problem type-E (SALBPE). The SALBPE aims to maximize the balance efficiency (BE) through determining the best combinations of cycle time and station number. To cope with the problem, a mixed integer nonlinear programming (MINLP) model is proposed. The MINLP model differs from the existing ALBPE models as it includes the technological requirements of assembly tasks and optimizes the variation of workload beside the BE. The validity of the proposed model is tested by solving the real-world case study and a set of benchmark problems.

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  • 6.
    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.

  • 7.
    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.
    Eskandari, Hamidreza
    Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
    An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem2020In: Engineering computations, ISSN 0264-4401, E-ISSN 1758-7077, Vol. 37, no 2, p. 501-521Article in journal (Refereed)
    Abstract [en]
    • Purpose – This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision makers aim to design an efficient assembly line while satisfying a set of constraints.
    • Design/methodology/approach – An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP in order to optimize the number of stations and the workload smoothness.
    • Findings – To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.
    • Originality/value – The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is employed in the IGA to enhance its local search capability.
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  • 8.
    Nourmohammadi, Amir
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran.
    Eskandari, Hamidreza
    Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran / School of Management, Swansea University, UK.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Integrated locating in-house logistics areas and transport vehicles selection problem in assembly lines2021In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, no 2, p. 598-616Article in journal (Refereed)
    Abstract [en]

    Decentralised in-house logistics areas, known as supermarkets, are widely used in the manufacturing industry for parts feeding to assembly lines. In contrary to the literature and inspired by observation in a real case, this study relaxes the assumption of using identical transport vehicles when deciding on the supermarkets’ location by considering the availability of different vehicles. In this regard, this study deals with the integrated supermarket location and transport vehicles selection problems (SLTVSP). A mixed-integer programming (MIP) model of the problem is developed. Due to the complexity of the problem, a hybrid genetic algorithm (GA) with variable neighborhood search (GA-VNS) is also proposed to address large-sized problems. The performance of GA-VNS is compared against the MIP, the basic GA, and simulated annealing (SA) algorithm. The computational results from the real case and a set of generated test problems show that GA-VNS provides a very good approximation of the MIP solutions at a much shorter computational time while outperforming the other compared algorithms. The analysis of the results reveals that it is beneficial to apply different transport vehicles rather than identical vehicles for SLTVSP.

  • 9.
    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. Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Balancing and scheduling assembly lines with human-robot collaboration tasks2022In: Computers & Operations Research, ISSN 0305-0548, E-ISSN 1873-765X, Vol. 140, p. 1-18, article id 105674Article in journal (Refereed)
    Abstract [en]

    In light of the Industry 5.0 trend towards human-centric and resilient industries, human-robot collaboration (HRC) assembly lines can be used to enhance productivity and workers’ well-being, provided that the optimal allocation of tasks and available resources can be determined. This study investigates the assembly line balancing problem (ALBP), considering HRC. This problem, abbreviated ALBP-HRC, arises in advanced manufacturing systems, where humans and collaborative robots share the same workplace and can simultaneously perform tasks in parallel or in collaboration. Driven by the need to solve the more complex assembly line-balancing problems found in the automotive industry, this study aims to address the ALBP-HRC with the cycle time and the number of operators (humans and robots) as the primary and secondary objective, respectively. In addition to the traditional ALBP constraints, the human and robot characteristics, in terms of task times, allowing multiple humans and robots at stations, and their joint/collaborative tasks are formulated into a new mixed-integer linear programming (MILP) model. A neighborhood-search simulated annealing (SA) is proposed with customized solution representation and neighborhood search operators designed to fit into the problem characteristics. Furthermore, the proposed SA features an adaptive neighborhood selection mechanism that enables the SA to utilize its exploration history to dynamically choose appropriate neighborhood operators as the search evolves. The proposed MILP and SA are implemented on real cases taken from the automotive industry where stations are designed for HRC. The computational results over different problems show that the adaptive SA produces promising solutions compared to the MILP and other swarm intelligence algorithms, namely genetic algorithm, particle swarm optimization, and artificial bee colony. The comparisons of human/robot versus HRC settings in the case study indicate significant improvement in the productivity of the assembly line when multiple humans and robots with collaborative tasks are permissible at stations.

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  • 10.
    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. Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration2024In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 187, article id 109775Article in journal (Refereed)
    Abstract [en]

    The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

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  • 11.
    Nourmohammadi, Amir
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Choosing efficient meta-heuristics to solve the assembly line balancing problem: A landscape analysis approach2019In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 81, p. 1248-1253Article in journal (Refereed)
    Abstract [en]

    It is widely known that the assembly line balancing problem (ALBP) is an NP-hard optimization problem. Although different meta-heuristics have been proposed for solving this problem so far, there is no convincing support that what type of algorithms can perform more efficiently than the others. Thus, using some statistical measures, the landscape of the simple ALBP is studied for the first time in the literature. The results indicate a flat landscape for the problem where the local optima are uniformly scattered over the search space. Accordingly, the efficiency of population-based algorithms in addressing the considered problem is statistically validated.

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  • 12.
    Nourmohammadi, Amir
    et al.
    University of Skövde, Virtual Engineering Research Environment. University of Skövde, School of Engineering Science.
    Fathi, Masood
    University of Skövde, Virtual Engineering Research Environment. University of Skövde, School of Engineering Science. Division of Industrial Engineering and Management, Uppsala University, Uppsala, Sverige.
    Ng, Amos H. C.
    University of Skövde, Virtual Engineering Research Environment. University of Skövde, School of Engineering Science. ision of Industrial Engineering and Management, Uppsala University, Uppsala, Sverige.
    Mahmoodi, Ehsan
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems2022In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 107, p. 1444-1448Article in journal (Refereed)
    Abstract [en]

    Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations.

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  • 13.
    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.
    Ruiz Zúñiga, Enrique
    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.
    A Genetic Algorithm for Bi-Objective Assembly Line Balancing Problem2019In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 519-524Conference paper (Refereed)
    Abstract [en]

    Assembly line designs in manufacturing commonly face the key problem of dividing the assembly tasks among the working stations so that the efficiency of the line is optimized. This problem is known as the assembly line balancing problem which is known to be NP-hard. This study, proposes a bi-objective genetic algorithm to cope with the assembly line balancing problem where the considered objectives are the utilization of the assembly line and the workload smoothness measured as the line efficiency and the variation of workload, respectively. The performance of the proposed genetic algorithm is tested through solving a set of standard problems existing in the literature. The computational results show that the genetic algorithm is promising in providing good solutions to the assembly line balancing problem.

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  • 14.
    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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  • 15.
    Nourmohammadi, Amir
    et al.
    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.
    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.
    Vollebregt, Janneke
    Scania Production Meppel B.V., Meppel, the Netherlands.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling2023In: CIRP - Journal of Manufacturing Science and Technology, ISSN 1755-5817, E-ISSN 1878-0016, Vol. 47, p. 71-85Article in journal (Refereed)
    Abstract [en]

    In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.

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  • 16.
    Slama, Ilhem
    et al.
    LIST3N, Université de Technologie de Troyes, France.
    Arbaoui, Taha
    LIST3N, Université de Technologie de Troyes, France.
    Nourmohammadi, Amir
    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. Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Assembly Line Balancing with Collaborative Robots Under Uncertainty of Human Processing Times2023In: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), IEEE, 2023, p. 2649-2653Conference paper (Refereed)
    Abstract [en]

    This paper studies the assembly line balancing problem with collaborative robots in light of recent efforts to implement collaborative robots in industrial production systems under random processing time. A stochastic version with uncertain human processing time is considered for the first time. The issue is defined by the potential for simultaneous human and robot task execution at the same workpiece, either in parallel or in collaboration. We provide stochastic mixed-integer programming based on Monte Carlo sampling approach for the balancing and scheduling of collaborative robot assembly lines for this novel issue type. In order to minimise the line cost including fixed workstation operating costs and resource costs caused by exceeding cycle time, the model determines both the placement of collaborative robots at stations and the distribution of work among humans and robots.

  • 17.
    Smedberg, Henrik
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Barrera Diaz, Carlos Alberto
    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.
    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. Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, Sweden.
    Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems2022In: Mathematical and Computational Applications, ISSN 1300-686X, E-ISSN 2297-8747, Vol. 27, no 6, article id 106Article in journal (Refereed)
    Abstract [en]

    Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today's manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

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