blocks|key|212589|text|http://www.perfecttableplan.com/|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|212590|我相信这是一个家伙，通常张贴在Joel上的软件。|212591|不过从来没试过。希望能帮上忙。|212592|entityMap|0|LINK|mutability|MUTABLE|url^0|0|W|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@$A|P|B|Q|1|R]]|C|$]]|$1|D|3|E|5|6|7|S|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|T|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|U|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|4]]]]

<a href="http://www.perfecttableplan.com/" rel="nofollow noreferrer">http://www.perfecttableplan.com/</a>

I believe this is from a guy that usually posts at Joel On Software.

Never tried it though.
Hope it helps.

blocks|key|873132|text|我是PerfectTablePlan的开发人员。我在这里发文章，还有Joel的软件业务。;0)|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|873133|组合问题，如座位分配，在算法上是相当棘手的。事实上是NP-硬。在60个座位上容纳60名客人的方式是60！(60阶乘)，这比已知宇宙中的原子数目还要多。|873134|PerfectTablePlan允许您指定A必须坐在B的旁边，但绝不接近C。它使用遗传算法自动分配席位。这在实践中非常有效--它通常会在几秒钟内为100位客人找到一个不错的解决方案。你可能需要为1000%2B客人煮杯咖啡。在实践中，为了应付当地习俗和家庭政治的变幻莫测，通常也需要进行一些拖放式的微调(鲍勃叔叔有点耳聋，我们最好把他放在最上面的桌子附近)。|873135|您可以更多地了解遗传算法这里。|873136|自动座位分配只是创造一个好的座位计划的一小部分。有关详细信息，请参阅PerfectTablePlan+巡演和提示页面。|873137|entityMap|0|LINK|mutability|MUTABLE|url|http://discuss.joelonsoftware.com/?biz|1|http://en.wikipedia.org/wiki/Np_hard|2|http://www.perfecttableplan.com/html/genetic_algorithm.html|3|http://www.perfecttableplan.com/html/manage_your_guest_list.html|4|http://www.perfecttableplan.com/html/tips.html^0|Y|9|0|0|Q|4|1|0|0|C|2|2|0|1F|2|3|1I|2|4|0^^$0|@$1|2|3|4|5|6|7|11|8|@]|9|@$A|12|B|13|1|14]]|C|$]]|$1|D|3|E|5|6|7|15|8|@]|9|@$A|16|B|17|1|18]]|C|$]]|$1|F|3|G|5|6|7|19|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|1A|8|@]|9|@$A|1B|B|1C|1|1D]]|C|$]]|$1|J|3|K|5|6|7|1E|8|@]|9|@$A|1F|B|1G|1|1H]|$A|1I|B|1J|1|1K]]|C|$]]|$1|L|3|-4|5|6|7|1L|8|@]|9|@]|C|$]]]|M|$N|$5|O|P|Q|C|$R|S]]|T|$5|O|P|Q|C|$R|U]]|V|$5|O|P|Q|C|$R|W]]|X|$5|O|P|Q|C|$R|Y]]|Z|$5|O|P|Q|C|$R|10]]]]

I am the developer of PerfectTablePlan. I post here as well as <a href="http://discuss.joelonsoftware.com/?biz" rel="noreferrer">Joel's Business of Software</a> . ;0)

Combinatorial problems, such as seat assignment, are quite nasty algorithmically. <a href="http://en.wikipedia.org/wiki/Np_hard" rel="noreferrer">NP-hard</a> in fact. The number of ways to seat 60 guests in 60 seats is 60! (60 factorial) and that is more than the number of atoms in the known universe.

PerfectTablePlan allows you to specify that A must sit next to B, but nowhere near C. It uses a genetic algorithm to automatically the assign seats. This works pretty well in practice - it will usually find a decent solution for 100 guests in a few seconds. You might need to make a coffee for 1000+ guests. In practice some drag and drop fine-tuning is also usually required to cope with the vagaries of local customs and family politics (Uncle Bob is a bit deaf, we had better put him nearer the top table).

You can find out a bit more about the genetic algorithm <a href="http://www.perfecttableplan.com/html/genetic_algorithm.html" rel="noreferrer">here</a>.

Ps/ The automatic seat assignment is only a small part of creating a good seating plan. See the PerfectTablePlan <a href="http://www.perfecttableplan.com/html/manage_your_guest_list.html" rel="noreferrer">tour</a> and the <a href="http://www.perfecttableplan.com/html/tips.html" rel="noreferrer">tips</a> page for more details.

blocks|key|1211567|text|尝试使用GLPK对此进行建模。整数线性规划能够将约束引入到具有多种可能结果的基于图的问题中.|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1211568|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Try modeling this using GLPK. Integer linear programming is amenable to introducing constraints into graph-based problems with multiple possible outcomes.

blocks|key|212617|text|我个人最喜欢的是不要安排座位:允许人们随心所欲地坐。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|212618|这可能会导致无心的小圈子，但这意味着你不必担心。|212619|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

My personal favorite is to not assign seating: allow folks to sit wherever they want.

That might lead to [un]intentional cliquishness, but it means you're not having to worry about it.

blocks|key|212526|text|我想这不是一个很好的答案，但是你可以研究植绒行为|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|212527|如果你在每一步中消除了随机的紧张情绪，那么羊群最终就会在每个成员相对于其他成员找到最佳位置的地方安顿下来。|212528|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Flocking_(behavior)^0|K|4|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

I expect this isn't a great answer, but you could research <a href="http://en.wikipedia.org/wiki/Flocking_(behavior)" rel="nofollow noreferrer">flocking behavior</a>

If you take away the random jitters at each step, the flock eventually settles where each member has found its optimum position in relation to the rest of the group.

blocks|key|212567|text|不久前我用了一个程序可以很好的适应这个..。它是一个Java应用程序，您可以定义规则，它将创建满足规则的测试用例。文件扩展名为.als|type|unstyled|depth|inlineStyleRanges|entityRanges|data|212568|事实GateRules+{+all+g:Gate+\一个g.loc+//+g.loc//盖茨有一个位置|blockquote|212569|我会继续绞尽脑汁找节目的名字。|212570|编辑:现在我想到的是合金，它可能不是理想的--“固定配置中的座位”的概念有点难以建模。我用不同的方法:定义规则(机场门在一个位置，只有一架飞机在跑道上)，以及测试前后的功能条件(在我降落飞机之后，我还能在跑道上有多架飞机吗？)|offset|length|212571|entityMap|0|LINK|mutability|MUTABLE|url|http://alloy.mit.edu/alloy/^0|0|0|0|A|2|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$]]|$1|E|3|F|5|6|7|U|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|V|8|@]|9|@$I|W|J|X|1|Y]]|A|$]]|$1|K|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|R]]]]

I used a program a while ago that would fit this perfectly... it was a Java App, you could define rules, and it would create test cases that satisfied the rules. The file extension was .als

<blockquote>
<pre><code>fact GateRules {
 all g:Gate | one g.loc // Gates have 1 Location
</code></pre>
</blockquote>

I'll keep wracking my brain for the program name.

EDIT: It was <a href="http://alloy.mit.edu/alloy/" rel="nofollow noreferrer">Alloy</a>
Now that I think about it, it may not be ideal - the notion of "seats in a fixed configuration" would be a little difficult to model. I used it differently: defining rules (an airport gate is in one location, only one plane is on a runway), and testing pre and post conditions for functions (after I land a plane, can i even have more than one plane on a runway?).

blocks|key|212689|text|这听起来像是一个约束满意度问题。您可能应该检查逻辑编程系统，这些系统也配备了约束--求解器。它们通常类似于prolog，只是它们实际上是对它们的解决者可以解决的问题的声明。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|212690|希望有一个从您最喜欢的语言中获得数据的简单接口。|212691|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

This sounds like a constraint satisfaction problem. You should probably check out logic programming systems that are also equipped with constraints-solvers. They're usually like prolog, only they are actually declarative for problems that are soluble by their solvers. 

Hopefully there is one that has an easy interface from your favourite language, to get the data in and out.

I'm getting married soon and am busy with the seating plan, and am running into the usual issues of who sits where: X and Y must sit together, but A and B cannot stand each other etc.

The numbers I'm dealing with aren't huge (so the manual option will work just fine), but being of the geeky persuasion, I was wondering if there was any software available to do this for me?

Failing an exact match, what should I look for (the problem space, books, reference code) to tweak for my purposes?

Seating plan software recommendations (does such a beast even exist?)

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我很快就要结婚了，正忙着安排座位，我遇到了一些常见的问题:X和Y必须坐在一起，但是A和B不能彼此站立等等。我正在处理的数字并不多(所以手动选项会很好)，但作为一个古怪的劝说者，我想知道是否有任何软件可以为我做到这一点？如果没有完全匹配，我应该寻找什么(问题空间，书籍，参考代码)来调整我的目的？

问座位表软件推荐(这样的野兽真的存在吗？)
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