G. Misand, B.S. Johansen, G. Hasle, and J. Haukland. Integration of geographical information technology and constraint reasoning. In Geographical Information Systems, June 1995.  Home/Search Document Details and Download
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Spatio-Temporal Database Support for Legacy Applications - Böhlen, Jensen, Skjellaug (1998) (Correct)
.... SPATIO TEMPORAL DATA MANAGEMENT APPLICATION The case example presented here is based on an existing legacy planning and scheduling system (termed Ecoplan) used for forest management, specifically for long term forest harvest scheduling based on ecological, recreational, and economical constraints [16]. While the system has four modules, we focus on the data module, which at present manages data in a loosely coupled fashion. Spatial data is stored in files and is managed by the module using proprietary data structures. The associated textual and numeric property data is managed by a relational ....
Misund, G., Johansen, B., Hasle, G., and Haukland, J. 1995. Integration of Geographical Information Technology and Constraint Reasoning---a Promising Approach to Forest Management. Technical Report STF33A 95009, SINTEF Applied Mathematics, Oslo, Norway.
Integration of Constraint Reasoning, and Simulation models in.. - Adhikary (Correct)
....local search etc. have also been applied to forest management [17, 3, 21] Above techniques have difficulty in modelling wide variety of spatial, temporal, and visual constraints and or in performing (usually conflicting) multi criteria optimization that is inherent in the problem. Misund et al. [20] have suggested the integrated use of Geographic Information Technology (GIT) and AI techniques (constraint reasoning and local search) to model and solve such problems. They describe their approach on a simpler version of the problem where only clear cutting is allowed. The problem is called ....
....meters high. Soft Constraints ffl Constraint bounding times between harvest for economic and qualitative reasons. Optimization Criteria O 1 : Optimal Harvest Time The actual time between harvests should be as close to optimal time as possible. 1 This closely follows the definition given in [20] O 2 : Even Consumption Estimated harvested volume for any period p EHV p (S) where p 2 [0: M ] should be as close to average harvested volume, AHV (S) That is, the variance between the harvest volumes should be minimized. O 3 : Old Forest Given area of old forest should be maintained over ....
[Article contains additional citation context not shown here]
G. Misund, B. S. Johansen, G. Hasle, and J. Haukland. Integration of Geographical Information Technology and Constraint Reasoning - A Promising Approach to Forest Management. In Jan Terje Bjørke, editor, SCANGIS '95 - Proceedings of the 5th Research Conference on GIS, 12th-14th June 1995, Trondheim, Norway, May 1995.
Houria III: A Solver For Hierarchical Systems Of.. - Bouzoubaa, Neveu, Hasle Self-citation (Hasle) (Correct)
.... desires to have more 20 Chapter 1 0 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 10 ca=2 Figure 6 planning time with ca=2 0 2 4 6 8 10 12 14 16 18 1 2 3 4 5 6 7 8 9 10 ca=3 Figure 7 planning time with ca=3 than one solution such as geometric layout, physical simulations, user interface algorithm animation [15, 16, 17, 18] design and analysis of mechanical devices and electrical circuits. 7 ACKNOWLEDGMENTS Thanks for many useful discussions, and comments to early versions of this paper to Alain Borning and Michael Sannella. Thanks to the anonymous referees for useful comments. This project was supported in part by ....
G. Misand, B.S. Johansen, G. Hasle, and J. Haukland. Integration of geographical information technology and constraint reasoning. In Geographical Information Systems, June 1995.
Constraint Technology Applied to Forest Treatment Scheduling - Adhikary, Hasle, Misund (1997) (1 citation) Self-citation (Misund Hasle) (Correct)
....the wide variety of spatial, temporal, and visual constraints, and optimizing upon several, usually conflicting criteria that pertain to adequate formulations of the problem. In addition, the typical size of real life LTFTSPs is often beyond the practical capacity of these methods. Misund et al. [24] have suggested integrated use of Geographic Information Technology (GIT) and AI techniques (constraint reasoning and local search) to model and solve such problems. They describe their approach applied to a simpler version of the LTFTSP, called the Clear Cut Scheduling Problem (CCSP) where ....
....g, find a schedule S = fT 1 ; T p g, T i 2 T , such that for each period in the horizon a corresponding treatment assignment is made. Furthermore, the schedule has to satisfy a set of constraints C = fC 1 ; C c g. The Clear Cut Scheduling Problem (CCSP) as defined and treated in [24], is a special case of LTFTSP. Only one treatment type, clear cutting, is allowed. Each stand is assigned this treatment only once, i.e. the remaining periods are assigned the trivial treatment. In Figure 1, an example of a forest area divided into stands is shown. Two stands sharing a common ....
[Article contains additional citation context not shown here]
G. Misund, B. S. Johansen, G. Hasle, and J. Haukland. Integration of Geographical Information Technology and Constraint Reasoning - A Promising Approach to Forest Management. In Jan Terje Bjørke, editor, SCANGIS '95 - Proceedings of the 5th Research Conference on GIS, 12th-14th June 1995, Trondheim, Norway, May 1995.
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