Incorporating aspects of habitat fragmentation into long-term forest planning using mixed integer programming

2008 ◽  
Vol 255 (3-4) ◽  
pp. 440-446 ◽  
Author(s):  
Karin Öhman ◽  
Peder Wikström
1979 ◽  
Vol 10 (2) ◽  
pp. 65-73
Author(s):  
F. H.D. Conradie ◽  
P. H. Van Den Berg

In Part I of this series of two articles, the reader was introduced to linear mixed integer programming techniques in the evaluation of capital investment. In this article, the interpretation, use, and limitations of the dual variables are discussed. Numerical examples are given to illustrate the application of the technique under consideration, as well as the results obtained. The model used was designed for a one-division organization, and takes into account nine types of restrictions. Provision is also made for the use of long-term financing, whereas other models reported in the literature to date, could only cope with short-term financing of proposed projects. Copies of Part I, setting out the model in full, as well as an English translation, are available from the authors.In Deel I van hierdie reeks van twee artikels is die gebruik van lineere en gemengde heeltal-programmeringstegnieke in die evaluering van voorgestelde kapitaalinvestering uiteengesit. In hierdie artikel word die interpretasie, gebruik en beperkings van die duaalveranderlikes bespreek. Numeriese voorbeelde word ook gegee om die toepassing van die tegniek onder bespreking, asook die resultate verkry, te illustreer. Die model wat gebruik word, is ontwerp vir 'n onderneming met net een afdeling, en neem nege soorte beperkings in aanmerking. Voorsiening word ook gemaak vir die gebruik van langtermyn finansiering, alhoewel ander modelle wat tot dusver in die literatuur beskryf is, slegs korttermyn finansiering van voorgestelde projekte kon behartig. Afskrifte van Deel I, wat die model volledig uiteensit, asook 'n Engelse vertaling, kan van die skrywers verkry word.


2019 ◽  
Vol 49 (12) ◽  
pp. 1493-1503
Author(s):  
Chourouk Gharbi ◽  
Mikael Rönnqvist ◽  
Daniel Beaudoin ◽  
Marc-André Carle

The unit restriction model and the area restriction model are the two main approaches to dealing with adjacency in forest harvest planning. In this paper, we present a new mixed-integer programming (MIP) formulation that can be classified as both a unit restriction approach and an area restriction approach. We need to generate a feasible cluster to formulate the model. However, unlike other approaches, there is no need to generate specific model constraints representing computationally burdensome clusters for large cases. We describe and analyze our approach by comparing it with the most efficient approaches presented in the literature. Comparisons are made from modeling and computational points of view. Results showed that the proposed model was competitive with regard to modeling complexity and size of formulation. Furthermore, it is easy to implement in standard modeling software.


Author(s):  
Fred Glover ◽  
Saïd Hanafi

Recent metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide this search. These guidance approaches are useful in intensification and diversification strategies related to fixing subsets of variables at particular values. The authors’ preceding Part I study demonstrated how to improve such approaches by new inequalities that dominate those previously proposed. In Part II, the authors review the fundamental concepts underlying weighted pseudo cuts for generating guiding inequalities, including the use of target objective strategies. Building on these foundations, this paper develops a more advanced approach for generating the target objective based on exploiting the mutually reinforcing notions of reaction and resistance. The authors demonstrate how to produce new inequalities by “mining” reference sets of elite solutions to extract characteristics these solutions exhibit in common. Additionally, a model embedded memory is integrated to provide a range of recency and frequency memory structures for achieving goals associated with short term and long term solution strategies. Finally, supplementary linear programming models that exploit the new inequalities for intensification and diversification are proposed.


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