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  • 1.
    Hosseini, Ahmad
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Lindroos, Ola
    Sveriges lantbruksuniversitet, Swedish University of Agricultural Sciences.
    Wadbro, Eddie
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A holistic optimization framework for forest machine trail network design accounting for multiple objectives and machines2019In: Canadian Journal of Forest Research, ISSN 0045-5067, E-ISSN 1208-6037, Vol. 49, no 2, p. 111-120Article in journal (Refereed)
    Abstract [en]

    Ground-based mechanized forestry requires the traversal of terrain by heavy machines. The routes they take are often called machine trails, and are created by removing trees from the trail and placing the logs outside it. Designing an optimal machine trail network is a complex locational problem that requires understanding how forestry machines can operate on the terrain as well as the trade-offs between various economic and ecological aspects. Machine trail designs are currently created manually based on intuitive decisions about the importance, correlations, and effects of many potentially conflicting aspects. Badly designed machine trail networks could result in costly operations and adverse environmental impacts. Therefore, this study was conducted to develop a holistic optimization framework for machine trail network design. Key economic and ecological objectives involved in designing machine trail networks for mechanized cut-to-length operations are presented, along with strategies for simultaneously addressing multiple objectives while accounting for the physical capabilities of forestry machines, the impact of slope, and operating costs. Ways of quantitatively formulating and combining these different aspects are demonstrated, together with examples showing how the optimal network design changes in response to various inputs.

  • 2.
    Hosseini, S. Ahmad
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.
    Sahin, Guvenc
    Unluyurt, Tonguc
    A penalty-based scaling algorithm for the multi-period multi-product distribution planning problem2017In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 49, no 4, p. 583-596Article in journal (Refereed)
    Abstract [en]

    Multi-period multi-product distribution planning problems are depicted as multi-commodity network flow problems where parameters may change over time. The corresponding mathematical formulation is presented for a discrete time setting, and it can also be used as an approximation for a continuous time setting. A penalty-based method which employs a cost-scaling approach is developed to solve some auxiliary penalty problems aiming to obtain an optimal solution for the original problem. The experiments on both random instances and case study problems show that the algorithm finds good-quality solutions with reasonable computational effort.

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