The aim of this section is to provide an overview of the original data set obtained from WSRP-like problems. This section describes them as they were originally published.
4.2.1
VRPTW data set
Given the close relationship between the vehicle routing problem with time windows (VRPTW) and workforce scheduling and routing (WSRP) two data sets from the literature of VRPTW are included. The first data set is the one by Solomon which has been widely studied. The second data set is from a study of the multi-objective aspect of VRPTW (Castro-Gutierrez et al., 2011).
4.2.1.1 Solomon’s data set
Solomon’s data set consists of 56 instances. Each instance contains 100 visits. The instances are classified according to the duration of the planning horizon and the location of the visits. In total there are six groups of instances R100, R200, C100, C200, RC100 and RC200. The initial letter in the name of the groups refers to the type of distribution of visits used within the groups’ instances. Groups R100 and R200 have a random visits distribution within the given area. Groups C100 and C200 present identifiable clusters of activities within the instances. Groups RC100 and RC200 combine random visit distribution with the presence of some clusters of visits. Groups R100 and RC100 have a short planning horizon between 230 and 240 minutes. In contrast, groups RC200, R200, C100 and C200 present a planning horizon of more than 900 minutes. In every instance there are different configurations of time windows for visits, some visits have an exact time window, others a flexible one and in some cases the time window is the same size as the planning horizon, i.e. not explicitly indicated. Solomon included instances with short service time (10 minutes) in groups R100, R200, RC100 and RC200 and long service time (90 minutes) in groups C100 and C200. There is not a defined set of vehicles per instance, because part of the objective of the VRPTW is to minimise the number of vehicles used to cover all 100 visits. Distances and travelling times are the same in absolute value. The matrix defining such values is symmetrical, i.e. the distance from location A to B is the same as from B to A. Distances are also Euclidean, i.e. the length of the line which connect to points.
4.2.1.2 Multi-objective VRPTW data set
The data set originally comes from a distribution company based in Tenerife, Spain (Castro-Gutierrez et al., 2011). It is structured in a similar way as the Solomon data set. The key differences are that distances and times values are based on information obtained via Google maps in contrast to simply euclidean values. As a result, in this data set distances and times values are different and non-symmetric. The distribution company has five types of customers, each type has its own time window profile for its required visits. The five types are: 1) Customers that are available through all the planning horizon (0-480 min); 2) Customers who prefer morning arrivals (0-160 min), afternoon deliveries (160-320 min) and late times (320-480 min); 3) Customers with similar distribution morning, afternoon and late but with a shortened time windows (130 minutes) respectively for morning (0-130), afternoon (175-305) and late (350- 480); 4) Customers with even more restricted time window arrangement (100 minutes) for morning (0,100), afternoon (190,290) and late (380,480); and 5) The final group of customers consists of random selection among the previously defined time windows. Instances are grouped depending on the number of customers they contain, either 50, 150 and up to 250. In total combining three different sizes (number of activities) times five different time window profiles (1, 2, 3, 4 and 5) giving 15 instances in total within the data set.
4.2.2
Home health care data set
The origin of these instances relates to a couple of home health care real scenarios based on two Danish municipalities (Rasmussen et al., 2012). This is perhaps the most complete of the data sets in terms of WSRP’s characteristics being provided. It includes skills for employees. There are four different main skills that are distributed among the carers. In addition, real average times in seconds and distances in meters are given. This is the only data set that contains preferences of both employees and recipients. Moreover, activities have an associated priority level. The priority is used because it is recognised in the industry that not all the activities can be performed in a day. Priority level might be increased as days pass without performing the corresponding activity. Finally, some instances contain time-dependent activities constraints. In total there are 11 instances in this data set.
4.2.3
Security guards patrolling data set
The data set describes the work of a set of security guards performing patrolling rounds in several locations. It has activities through out a month. The information originally comes from a Belgian company (Misir et al., 2011). The data set is divided into six districts. Each district with a range of security guards and different number of visits (patrolling round locations). It records up to 16 different skills for the security guards which provide a good range of skill matching against activities. Security guards are available 24 hours and they must start and end their work at home.
4.2.4
Technicians scheduling instance
Originally from a British Telecom Laboratories problem the only instance in this cat- egory described the assignment of 118 technicians to perform 250 dispersedly located jobs. The instance is used in the work of G¨unther and Nissen (2012). The distance and time matrices can be obtained following a simple formula. The duration of the jobs varies from 10 up to 513 minutes. It is the only dataset that provides average activities’ duration that vary depending on the technicians’ expertise. Time windows are only of three types: morning, afternoon and no preference which cover a mix of the previous two. Technicians are contracted for eight hours with different starting and finishing working times. There are 11 servicing centres and each of the technicians must start and end their working day at the designated one. Qualifications are present in this instance. Some activities can only be performed by a single employee, other simpler activities can be carried out by up to 107, thus giving a good distribution of activity-employee matchings.