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Hosseini, S. Ahmad
Publications (2 of 2) Show all publications
Hosseini, A., Lindroos, O. & Wadbro, E. (2019). A holistic optimization framework for forest machine trail network design accounting for multiple objectives and machines. Canadian Journal of Forest Research, 49(2), 111-120
Open this publication in new window or tab >>A holistic optimization framework for forest machine trail network design accounting for multiple objectives and machines
2019 (English)In: Canadian Journal of Forest Research, ISSN 0045-5067, E-ISSN 1208-6037, Vol. 49, no 2, p. 111-120Article in journal (Refereed) Published
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.

National Category
Other Mathematics Forest Science
Identifiers
urn:nbn:se:umu:diva-154287 (URN)10.1139/cjfr-2018-0258 (DOI)000458033400001 ()
Available from: 2018-12-14 Created: 2018-12-14 Last updated: 2019-02-20Bibliographically approved
Hosseini, S. A., Sahin, G. & Unluyurt, T. (2017). A penalty-based scaling algorithm for the multi-period multi-product distribution planning problem. Engineering optimization (Print), 49(4), 583-596
Open this publication in new window or tab >>A penalty-based scaling algorithm for the multi-period multi-product distribution planning problem
2017 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 49, no 4, p. 583-596Article in journal (Refereed) Published
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.

Keywords
Network flows, distribution planning, nonlinear programming, scaling algorithm, epsilon-optimality
National Category
Computational Mathematics
Identifiers
urn:nbn:se:umu:diva-133725 (URN)10.1080/0305215X.2016.1206474 (DOI)000395050200003 ()
Available from: 2017-05-05 Created: 2017-05-05 Last updated: 2018-06-09Bibliographically approved
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