Answer Sets: From Constraint Programming Towards Qualitative Optimization

Author(s):  
Gerhard Brewka
2010 ◽  
Vol 130 (2) ◽  
pp. 332-342 ◽  
Author(s):  
Shuichiro Sakikawa ◽  
Tatsuhiro Sato ◽  
Toyohisa Morita ◽  
Kenji Ohta

Author(s):  
Zeynep G. Saribatur ◽  
Thomas Eiter

The recently introduced notion of ASP abstraction is on reducing the vocabulary of a program while ensuring over-approximation of its answer sets, with a focus on having a syntactic operator that constructs an abstract program. It has been shown that such a notion has the potential for program analysis at the abstract level by getting rid of irrelevant details to problem solving while preserving the structure, that aids in the explanation of the solutions. We take here a further look on ASP abstraction, focusing on abstraction by omission with the aim to obtain a better understanding of the notion. We distinguish the key conditions for omission abstraction which sheds light on the differences to the well-studied notion of forgetting. We demonstrate how omission abstraction fits into the overall spectrum, by also investigating its behavior in the semantics of a program in the framework of HT logic.


Author(s):  
Yingchun Xia ◽  
Zhiqiang Xie ◽  
Yu Xin ◽  
Xiaowei Zhang

The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible.


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