By Cynthia Barnhart (auth.), Laurent Perron, Michael A. Trick (eds.)
The fifth overseas convention on Integration of AI and OR suggestions in Constraint Programming for Combinatorial Optimization difficulties (CPAIOR 2008) used to be held in Paris, France might 20–23, 2008. the aim of this convention sequence is to assemble researchers within the ?elds of constraint programming, arti?cial intelligence, and operations learn to discover methods of fixing large-scale, useful optimization difficulties via integration and hybridization of the ?elds’ di?erent suggestions. over the years, this examine group is gaining knowledge of that the ?elds have a lot in c- mon, and there was great richness within the ensuing cross-fertilization of ?elds. This yr, we allowed submissions of either lengthy (15 web page) and brief (5 web page) papers, with brief papers both being unique paintings, a discounted model of a protracted paper, or a longer summary of labor released in other places. We weren't s- prised through the sixty nine submissions within the lengthy paper classification: this can be an energetic ?eld with many researchers. We have been stunned through the sixty one brief paper submissions. This was once way over expected. With a hundred thirty top of the range submissions, compe- tion for reputation during this year’s software was once relatively ?erce. after all, we authorised 18 lengthy papers and 22 brief papers for presentation and booklet during this volume.
Read or Download Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems: 5th International Conference, CPAIOR 2008 Paris, France, May 20-23, 2008 Proceedings PDF
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Additional info for Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems: 5th International Conference, CPAIOR 2008 Paris, France, May 20-23, 2008 Proceedings
Rr + Wr ≤ 1 Rr = Wr if pe(h) = pe(k) if pe(h) = pe(k) (3) (4) Constraints on the capacity of local memory devices can now be defined in terms of M , W and R variables. When a task executes it always works on local data, therefore everything it needs (input and output buffers, internal data) is copied to the local device when the task starts. At the end of the execution all data allocated in DRAM are copied back, while all locally allocated requirements are left on the local device. Therefore, in order to state memory capacity constraint we first define: base usage(j) = comm(r)Rr + mem(i)Mi + pe(i)=j ar = (th , tk ) pe(k) = j comm(r)Wr ar = (th , tk ) pe(h) = j pe(h) = pe(k) Where mem(i) is the amount of memory required to store internal data of task i and comm(r) is the size of the communication buffer associated to arc r.
It can be computed by making the intersection between the task positioned at its earliest start and the task positioned at its latest start. Then the compulsory part profile is the aggregation of all compulsory parts of the different tasks of a cumulative constraint. When all tasks that have a non-empty compulsory part are completely fixed, the compulsory part profile is simply called the cumulative profile. The diﬀn constraint was introduced in  in order to handle multi-dimensional placement problems.
Finally, it can also be directly used within the two cumulative constraints, which are well-known necessary conditions for a non-overlapping constraint. , the difference between the resource capacity and the height of the peak is equal to a strictly positive integer ). Again, we can use the longest cumulative hole problem in order to check that we can fill enough the gap on top of the highest peak. This is illustrated by Part (C) of Figure 2. 3 Evaluating the Longest Cumulative Hole This section shows how to evaluate an upper bound of lmax σ (T ).