Titlebook: Integration of AI and OR Techniques in Constraint Programming; 12th International C Laurent Michel Conference proceedings 2015 Springer Int

清楚

Uncertain Data Dependency Constraints in Matrix Models,n per item, the production rates, or the wage costs, in applications such as production planning, economics, inventory management. Data imprecision applies to the cells of the matrix and the output vector. Our approach contributes the following results: 1) the identification of the context of matrix

散布

Enhancing MIP Branching Decisions by Using the Sample Variance of Pseudo Costs,nce by measuring the relative error of a pseudo-cost confidence interval. The second method performs a specialized version of a two-sample Student’s .-test for filtering branching candidates with a high probability to be better than the best history candidate..Both methods were implemented in the MI

引起痛苦

Large Neighborhood Search for Energy Aware Meeting Scheduling in Smart Buildings, MIP is used to repair the schedule so as to minimize energy consumption. This approach is far more effective than solving the complete problem as a MIP problem. Our results show that solutions from the LNS-based approach are up to 36% better than the MIP-based approach when both given 15 minutes.

Vulvodynia

Interregnum

Rectangle Placement for VLSI Testing,acros must be placed without overlap in a given area, but no wiring between the macros exists. Typically, industrial problems of this type require placing hundreds of macros of different sizes and shapes and include additional constraints such as fixing or grouping some of the macros. Many tools and

死貓他燒焦

大吃大喝

A Benders Approach to the Minimum Chordal Completion Problem,ple to pose, presents considerable computational difficulties and has been tackled mostly by heuristics. In this paper, an integer programming approach based on Benders decomposition is presented. Computational results show that the improvement in solution times over a simple branch-and-bound algori

豐滿中國

Embedding Decision Trees and Random Forests in Constraint Programming,rvious to classical modeling approaches. The core idea in EML consists in embedding a Machine Learning model in a traditional combinatorial model. So far, the method has been demonstrated by using Neural Networks and Constraint Programming (CP). In this paper we add one more technique to the EML ars

卷發(fā)

Scheduling with Fixed Maintenance, Shared Resources and Nonlinear Feedrate Constraints: A Mine Plans, how crushers, loaders and trucks should be used to realise the short term plan. It is important to achieve both grade targets (by blending) and maximise the utilisation (i.e., throughput) of the mine. The resulting problem is a non-linear scheduling problem with maintenance constraints, blending

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