Problem instances for robust spotter scheduling problem

We randomly generated problem instances for the robust spotter scheduling problem (RSSP) which are used in the working paper "Tadumadze, G., Boysen, and N., Emde, S. (2019): Robust spotter scheduling in trailer yards. Working Paper TU Darmstadt". For detailed instance generation scheme see Section 4.1 of the aforementioned work. We generate RSSP instances for the following three different sized trailer terminals which differ in the number of dock doors \(|D|\) of the terminal and the parking positions\(|\Pi|\) in the parking lot: S: \(|D| = 20\); \(|\Pi| \in \{40,60,80,100\}\) M: \(|D| = 50\); \(|\Pi| \in \{100,150,200,250\}\) L: \(|D| = 200\); \(|\Pi| \in \{400,600,800,1000\}\) The dataset contains of 800 RSSP instances each of which is described in a text-file (labelled as “&lt;ID&gt;.txt” where ID is a running index of instance (from 0001 to 0800)). In particular, each file contains information about the number of transport requests (i.e., jobs) \(n\), the spotter fleet size \(m\) , processing time of transport request \(j\) (\(j=1,…,n\)) \(p_j\) , completion time of transport request \(j\) (\(j=1,…,n\)) \(C_j\) and the deadheading time it takes a spotter to move from the target position of job \(j\) to the start position of job \(j'\) (\(j,j'=1,…,n\)) \(\delta_{jj'}\) (i.e., sequence-dependent setup times). Further, each file consists of additional information which describes the direction of each transport request (i.e., whether it describes a transport request of a semitrailer from parking lot to terminal or vice versa) and the index of corresponding truck (i.e., semitrailer). Each text-file is structured as follows: count_jobs = &lt;integer number that describs the number of transport requests (i.e., \(n\))&gt;. Count_spotters = &lt;integer number that describs the number of spotters (i.e., \(m\))&gt;. processing_times = &lt;vector with \(n\) integer numbers: the \(j\)-th element of the vector describes the processing time of for transport request \(j\) (i.e., \(p_j\))&gt;. completion_times = &lt;vector with \(n\) integer numbers: the \(j\)-th element of the vector describes the completion time of \(j\)-th transport request (i.e., \(C_j\))&gt;. weights = &lt;vector with \(n\) integer numbers: the \(j\)-th element of the vector describes the relative importance of transport request \(j\) (i.e., \(w_j\))&gt;. driving_times = &lt;matrix with \(n*n\) integers: each entry in \(j\)-th column and \(j'\)-th row describes the sequence-dependent setup times between jobs \(j\) and \(j'\) (\(\delta_{jj'}\))&gt;. *********************job_truck_relation********************* to_terminal = &lt;vector with n boolean values: the \(j\)-th element of vector has a value “\(True\)”, if the \(j\)-th job stands for transport request of a semitrailer from parking lot to terminal; “\(False\)” -- otherwise&gt;. trucks_index = &lt;vector with n integer numbers: the \(j\)-th element of vector describes the index of the semitrailer of transport request \(j\)&gt;. The first 360 RSSP instances (i.e., 0001-0360) are generated for the computational performance test (Section 4.2) and the next 440 instances (i.e., 0361-0800) -- for the simulation study (Section 4.4).我们为鲁棒拖车调度问题（Robust Spotter Scheduling Problem, RSSP）随机生成了算例集，用于工作论文“Tadumadze, G., Boysen, N. 与 Emde, S. (2019): 拖车堆场的鲁棒牵引车调度。达姆施塔特工业大学工作论文”。详细的算例生成方案请参见上述论文的4.1章节。 我们针对三种不同规模的拖车码头生成RSSP算例，三者的区别在于码头的装卸门数量|D|以及停车场的泊位数量|Π|： - 小型（S）：|D|=20；|Π|∈{40,60,80,100} - 中型（M）：|D|=50；|Π|∈{100,150,200,250} - 大型（L）：|D|=200；|Π|∈{400,600,800,1000} 本数据集共包含800个RSSP算例，每个算例以文本文件形式存储，文件命名格式为"<ID>.txt"，其中ID为算例的连续索引（范围为0001至0800）。具体而言，每个文件包含以下信息：运输请求（transport request）的数量n、牵引车车队规模m、作业j（j=1,…,n）的处理时间p_j、作业j的完工时间C_j，以及牵引车从作业j的目标位置移动至作业j'的起始位置所需的空载行驶时间δ_{jj'}（即序列依赖准备时间）。此外，每个文件还包含额外信息，用于描述每项运输请求的方向（即该请求是半挂车从停车场运往码头，还是反之），以及对应半挂车的索引。 每个文本文件的结构如下： count_jobs = <描述运输请求数量的整数，即n>。 Count_spotters = <描述牵引车车队规模的整数，即m>。 processing_times = <包含n个整数的向量：向量的第j个元素对应作业j的处理时间，即p_j>。 completion_times = <包含n个整数的向量：向量的第j个元素对应作业j的完工时间，即C_j>。 weights = <包含n个整数的向量：向量的第j个元素对应作业j的相对重要性，即w_j>。 driving_times = <包含n×n个整数的矩阵：第j'行、第j列的元素表示作业j与作业j'之间的序列依赖准备时间，即δ_{jj'}>。 *********************作业-卡车关联关系********************* to_terminal = <包含n个布尔值的向量：若第j个作业表示半挂车从停车场运往码头的运输请求，则向量的第j个元素为"True"；否则为"False">。 trucks_index = <包含n个整数的向量：向量的第j个元素对应作业j所关联的半挂车的索引>。 本数据集中前360个算例（即0001至0360）用于计算性能测试（对应论文4.2章节），剩余440个算例（即0361至0800）则用于仿真研究（对应论文4.4章节）。