Public Counter – Same day service

 

Public Counter – Same day service

THE FEE FOR LEGALISATION IS £27 PER DOCUMENT.

PLEASE NOTE THAT THE LEGALISATION OFFICE WILL BE CLOSED FROM 11.30 FRIDAY 7 SEPTEMBER 2007.

Our public counter is normally open 9.30am to 3.30pm Monday to Friday. No appointment is necessary. The fee for legalisation of each document is £27 payable in cash or by credit/debit card. You do not need to bring ID and anyone can present the documents on your behalf. During busy periods you may have a total waiting time of up to 2 hours.

The Legalisation Office is not a suitable environment for babies or young children. Please try to avoid bringing babies or young children to the Legalisation Office.

If you bring batches of 10 or more documents we may agree a time for you to collect them. This may be the following day. Please also note that customers with batches of 20 or more documents can avoid queuing by dropping of and collecting batches according to the following arrangements:

  • 20 – 100 DOCUMENTS Batches deposited at Counter 1 before 10am will be available for collection from 3pm the same day. Batches dropped off at Counter 1 before 3pm will be available for collection from 10am the next working day.
  • OVER 100 DOCUMENTS We shall try our best to ensure that batches deposited at Counter 1 before 10am or 3pm will be available at the same time the next working day, but this may not always be possible depending on the size of the batch and our other commitments.

Customers depositing and collecting documents according to the above arrangements will NOT be required to queue. Batches may still be deposited and collected at other times in the usual way ie. by queuing to deposit them and waiting to collect them after they have been legalised.

 
基于python实现的粒子群的VRP(车辆配送路径规划)问题建模求解+源码+项目文档+算法解析,适合毕业设计、课程设计、项目开发。项目源码已经过严格测试,可以放心参考并在此基础上延申使用,详情见md文档 算法设计的关键在于如何向表现较好的个体学习,标准粒子群算法引入惯性因子w、自我认知因子c1、社会认知因子c2分别作为自身、当代最优解和历史最优解的权重,指导粒子速度和位置的更新,这在求解函数极值问题时比较容易实现,而在VRP问题上,速度位置的更新则难以直接采用加权的方式进行,一个常见的方法是采用基于遗传算法交叉算子的混合型粒子群算法进行求解,这里采用顺序交叉算子,对惯性因子w、自我认知因子c1、社会认知因子c2则以w/(w+c1+c2),c1/(w+c1+c2),c2/(w+c1+c2)的概率接受粒子本身、当前最优解、全局最优解交叉的父代之一(即按概率选择其中一个作为父代,不加权)。 算法设计的关键在于如何向表现较好的个体学习,标准粒子群算法引入惯性因子w、自我认知因子c1、社会认知因子c2分别作为自身、当代最优解和历史最优解的权重,指导粒子速度和位置的更新,这在求解函数极值问题时比较容易实现,而在VRP问题上,速度位置的更新则难以直接采用加权的方式进行,一个常见的方法是采用基于遗传算法交叉算子的混合型粒子群算法进行求解,这里采用顺序交叉算子,对惯性因子w、自我认知因子c1、社会认知因子c2则以w/(w+c1+c2),c1/(w+c1+c2),c2/(w+c1+c2)的概率接受粒子本身、当前最优解、全局最优解交叉的父代之一(即按概率选择其中一个作为父代,不加权)。
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