J. Arturo Castillo-Salazar

Bio: J. Arturo Castillo-Salazar is an academic researcher from University of Nottingham. The author has contributed to research in topics: Solver & Greedy algorithm. The author has an hindex of 5, co-authored 7 publications receiving 182 citations.

Workforce scheduling and routing problems: literature survey and computational study

Abstract: In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers’ locations and security guards performing rounds at different premises, etc. We refer to these scenarios as workforce scheduling and routing problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time at the locations where tasks need to be performed. The first part of this paper presents a survey which attempts to identify the common features of WSRP scenarios and the solution methods applied when tackling these problems. The second part of the paper presents a study on the computational difficulty of solving these type of problems. For this, five data sets are gathered from the literature and some adaptations are made in order to incorporate the key features that our survey identifies as commonly arising in WSRP scenarios. The computational study provides an insight into the structure of the adapted test instances, an insight into the effect that problem features have when solving the instances using mathematical programming, and some benchmark computation times using the Gurobi solver running on a standard personal computer.

A Survey on Workforce Scheduling and Routing Problems

Abstract: In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at dierent locations hence requir- ing some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers' locations, security guards performing rounds at dierent premises, etc. We re- fer to these scenarios as Workforce Scheduling and Routing Problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time to the locations where tasks need to be performed. This kind of problems have been tackled in the literature for a number of years. This paper presents a survey which attempts to identify the common attributes of WSRP scenarios and the so- lution methods applied when tackling these problems. Our longer term aim is to achieve an in-depth understanding of how to model and solve workforce scheduling and routing problems and this survey represents the rst step in this quest.

Mixed Integer Programming with Decomposition to Solve a Workforce Scheduling and Routing Problem

Abstract: We propose an approach based on mixed integer programming (MIP) with decomposition to solve a workforce scheduling and routing problem, in which a set of workers should be assigned to tasks that are distributed across different geographical locations. This problem arises from a number of home care planning scenarios in the UK, faced by our industrial partner. We present a mixed integer programming model that incorporates important real-world features of the problem such as defined geographical regions and flexibility in the workers? availability. Given the size of the real-world instances, we propose to decompose the problem based on geographical areas. We show that the quality of the overall solution is affected by the ordering in which the sub-problems are tackled. Hence, we investigate different ordering strategies to solve the sub-problems and show that such decomposition approach is a very promising technique to produce high-quality solutions in practical computational times using an exact optimization method.

A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints

Abstract: We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints (synchronisation, overlap, minimum difference, maximum difference and minimum-maximum difference) on workforce scheduling and routing problems. These types of constraints are important because they allow the modelling of situations in which activities relate to each other time-wise, e.g. synchronising two technicians to complete a job. These constraints often make the scheduling and routing of employees more difficult. GHI is tested on set of benchmark instances from different workforce scheduling and routing problems (WSRPs). We compare the results obtained by GHI against the results from a mathematical programming solver. The comparison seeks to determine which solution method achieves more best solutions across all instances. Two parameters of GHI are discussed, the sorting of employees and the sorting of visits. We conclude that using the solver is adequate for instances with less than 100 visits but for larger instances GHI obtains better results in less time.

Computational Study for Workforce Scheduling and Routing Problems

Abstract: In this paper we perform a computational study of 112 generated instances for the workforce scheduling and routing problem (WSRP). WSRP has applications in many service provider industries which require employees to visit customers to perform a diverse range of activities. We use a mathematical programming model from the literature of vehicle routing problem with time windows (VRPTW) to tackle WSRP due to their similarity. The computational study has three objectives: (1) To apply a VRPTW model to tackle medium size WSRP instances containing different distribution of visits, teaming and connected activities constraints. These constraints are an important because they allow the modelling of scenarios in which is necessary to link two or more activities over time. (2) To compare the difficulty between WSRP and VRPTW. And, (3) to investigate which time limit is adequate for the mathematical programming solver to find feasible solutions for our adapted instances. Based on our study results, we conclude that although the solver can achieve feasible solutions for some instances using the model, other solutions methods need to be considered due to the number of instances in which solutions could not be found. It is also found that our adaptations to Solomon's VRPTW data set to generate WSRP instances, increase their difficulty. Finally, when using a mathematical programming solver 2 hours time limit is recommended to get feasible solutions in medium size scenarios.

A branch-and-price algorithm for the home health care scheduling and routing problem with stochastic service times and skill requirements

Abstract: Home health care (HHC) is defined as providing medical and paramedical services for patients at their own domicile. In the HHC industry, it is crucial for health care organisations to assign caregivers to patients and devise reasonable visiting routes to save total operational cost and improve the service quality. However, some special constraints make the problem hard to solve. For example, patients’ service times are usually stochastic due to their varying health conditions; caregivers are organised in a hierarchical structure according to their skills to satisfy patients’ demands. In this paper, we address a HHC scheduling and routing problem with stochastic service times and skill requirements. A stochastic programming model with recourse is proposed to formulate the problem in which the expected penalty for late arrival at customers is considered. To solve the problem, it is equivalently transformed into a master problem and a pricing sub-problem. A column generation algorithm is developed to solve t...