Högskolan i Skövde

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  • 1.
    Amouzgar, Kaveh
    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.
    Andersson, Tobias
    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.
    Metamodel based multi-objective optimization of a turning process by using finite element simulation2020In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 52, no 7, p. 1261-1278Article in journal (Refereed)
    Abstract [en]

    This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.

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  • 2.
    Deb, Kalyanmoy
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Indian Institute of Technology, Kanpur, Uttar Pradesh, India / Aalto University School of Economics , Finland.
    Datta, Rituparna
    ndian Institute of Technology, Kanpur, Uttar Pradesh, India.
    A bi-objective constrained optimization algorithm using a hybrid evolutionary and penalty function approach2013In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 45, no 5, p. 503-527Article in journal (Refereed)
    Abstract [en]

    Constrained optimization is a computationally difficult task, particularly if the constraint functions are nonlinear and non-convex. As a generic classical approach, the penalty function approach is a popular methodology which degrades the objective function value by adding a penalty proportional to the constraint violation. However, the penalty function approach has been criticized for its sensitivity to the associated penalty parameters. Since its inception, evolutionary algorithms have been modified in various ways to solve constrained optimization problems. Of them, the recent use of a bi-objective evolutionary algorithm in which the minimization of the constraint violation is included as an additional objective has received significant attention. In this article, a combination of a bi-objective evolutionary approach with the classical penalty function methodology is proposed, in a manner complementary to each other. The evolutionary approach provides an appropriate estimate of the penalty parameter, while the solution of an unconstrained penalized function by a classical method induces a convergence property to the overall hybrid algorithm. The working of the procedure on a number of standard numerical test problems and an engineering design problem is demonstrated. In most cases, the proposed hybrid methodology is observed to take one or more orders of magnitude fewer function evaluations to find the constrained minimum solution accurately than some of the best reported existing methodologies.

  • 3.
    Nourmohammadi, Amir
    et al.
    Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran.
    Eskandari, Hamidreza
    Iran Management & Technology Development Center, Tarbiat Modares University, Tehran, Iran.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Design of stochastic assembly lines considering line balancing and part feeding with supermarkets2019In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 51, no 1, p. 63-83Article in journal (Refereed)
    Abstract [en]

    This article aims to address the assembly line balancing problem (ALBP) and supermarket location problem (SLP) as two long-term interrelated decision problems considering the stochastic nature of the task times and demands. These problems arise in real-world assembly lines during the strategic decision-making phase of configuring new assembly lines from both line balancing and part-feeding (PF) aspects. A hierarchical mathematical programming model is developed, in which the first level resolves the stochastic ALBP by minimizing the workstation numbers and the second level deals with the stochastic SLP while optimizing the PF shipment, inventory and installation costs. The results of case data from an automotive parts manufacturer and a set of standard test problems verified that the proposed model can optimize the configuration of assembly lines considering both ALBP and SLP performance measures. This study also validates the effect of the stochastic ALBP on the resulting SLP solutions.

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