Shift设计与答案集规划

IF 0.4 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Fundamenta Informaticae Pub Date : 2015-09-27 DOI:10.3233/FI-2016-1396

Michael Abseher, M. Gebser, Nysret Musliu, Torsten Schaub, S. Woltran

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引用次数: 31

摘要

答案集编程(ASP)是一种强大的声明性编程范例，已成功地应用于许多不同的领域。最近，ASP在课程排课等难优化问题上也被证明是成功的。在本文中，我们接近另一个重要的任务，即轮班设计问题，旨在调整最小轮班数量，以满足所需的员工数量(通常在不同的时间段有所不同)，从而最大限度地减少人员配备过多和不足。我们提供了转换设计问题的ASP编码，据我们所知，ASP还没有解决这个问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Shift Design with Answer Set Programming

Answer Set Programming (ASP) is a powerful declarative programming paradigm that has been successfully applied to many different domains. Recently, ASP has also proved successful for hard optimization problems like course timetabling. In this paper, we approach another important task, namely, the shift design problem, aiming at an alignment of a minimum number of shifts in order to meet required numbers of employees (which typically vary for different time periods) in such a way that over- and understaffing is minimized. We provide an ASP encoding of the shift design problem, which, to the best of our knowledge, has not been addressed by ASP yet.

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来源期刊

Fundamenta Informaticae 工程技术-计算机：软件工程

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

9.8 months

期刊介绍： Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing: solutions by mathematical methods of problems emerging in computer science solutions of mathematical problems inspired by computer science. Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, algebraic and categorical methods.