Enhanced Iterated local search for the technician routing and scheduling problem

Most public facilities in the European countries, including France, Germany, and the UK, were built during the reconstruction projects between 1950 and 1980. Owing to the deteriorating state of such vital infrastructure has become relatively expensive in the recent decades. A significant part of the maintenance operation costs is spent on the technical staff. Therefore, the optimal use of the available workforce is essential to optimize the operation costs. This includes planning technical interventions, workload balancing, productivity improvement, etc. In this paper, we focus on the routing of technicians and scheduling of their tasks. We address for this purpose a variant of the workforce scheduling problem called the technician routing and scheduling problem (TRSP). This problem has applications in different fields, such as transportation infrastructure (rail and road networks), telecommunications, and sewage facilities. To solve the TRSP, we propose an enhanced iterated local search (eILS) approach. The enhancement of the ILS firstly includes an intensification procedure that incorporates a set of local search operators and removal-repair heuristics crafted for the TRSP. Next, four different mechanisms are used in the perturbation phase. Finally, an elite set of solutions is used to extensively explore the neighborhood of local optima as well as to enhance diversification during search space exploration. To measure the performance of the proposed method, experiments were conducted based on benchmark instances from the literature, and the results obtained were compared with those of an existing method. Our method achieved very good results, since it reached the best overall gap, which is three times lower than that of the literature. Furthermore, eILS improved the best-known solution for $34$ instances among a total of $56$ while maintaining reasonable computational times.Comment: Submitted manuscript to Computers and Operations Research journal. 34 pages, 7 figures, 6 table

Three essays on workforce scheduling

This thesis presents three different problems belonging to different planning stages of the workforce scheduling process. The first article addresses a tour scheduling problem faced by a ground-handling agency at airports. In this problem, multiskilled agents are assigned to shifts and days-off within a planning horizon of one month to cover the airlines agent requirements. The second article considers a technician routing and scheduling problem form an external maintenance provider. This problem mainly involves obtaining weekly schedules such that the maintenance tasks requested by geographically distributed customers are fulfilled. The considered decisions consist of the assignment of technicians to teams, the assignment teams to tasks, and the dispatch of teams to service routes. The third article addresses a task scheduling problem for check-in counters personnel at airports. This problem involves the daily assignment of multiskilled agents to flights (check-ins and boardings) considering the change-over times between gates, such that the agent requirements from the airlines are satisfied

Resource constrained routing and scheduling: Review and research prospects

In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects

Exact and heuristic methods for optimization in distributed logistics

Increasing sustainability in the transportation and logistics sector is a key element in achieving energy transition goals set internationally (UN), continentally (EU), and nationally. This thesis discusses two challenges related to this energy transition.First, I study how offshore wind can become an attractive alternative to traditional energy producers. I investigate how the maintenance of offshore wind farms can be organized more efficiently. Think of smartly coordinating technicians, maintenance tasks, spare parts, and a fleet of vessels. Our new algorithms ensure that the relatively polluting visits to the wind farm can be reduced, which directly causes a reduction of CO2 emmision and an increased sustainable energy production.Second, I focus on a different sustainability challenge in the logistics sector: How to handle the enormous amounts of product returns from and to (web)shops. We study how to incorporate these product returns in regular operations, instead of treating them distinct from current operations. In this way, we can reuse already existing capital, leading significant cost decreases. This directly increases sustainability of the e-commerce sector.Although both challenges are structurally different from a practical point of view, from an applied mathematician’s perspective this is not true. Our smart plannings algorithms are broadly applicable, and can be used to resolve major questions on how to increase sustainability in the transportation and logistics sector

Deep Learning Approach to Technician Routing and Scheduling Problem

This paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results

The Curricular Practical Training Rotation Problem Formulation and the Assessment of Rotation Strategies

This study addresses the curricular practical training rotation problem, which is a type of staff assignment problem. Many educational institutions require theoretical knowledge to be complemented by practical training. Although the details of the implementation differ from institution to institution, it is necessary to prepare a rotation plan that determines how long the trainees will practice in which unit in which training period. Because of the complexity of the problem and humanistic reasons, the manual rotation plan can not reach the optimal level that satisfies all stakeholders and takes time. This study defines a general Curricular Practical Training Rotation Planning Problem specific to the curriculum-based trainee assignment process carried out in a university department and proposes an integer mathematical model for its solution. It is one of the important contributions of this study. It also provides a methodological approach to identify the most appropriate rotation strategy that will satisfy stakeholders. The methodological approach followed is a structure that can be adapted to different perspectives. The study has the potential to guide practitioners and researchers in the field and to lead a rich literature that will be formed with different side constraints and purposes to the problem

Exact and hyper?heuristic solutions for the distribution?installation problem from the VeRoLog 2019 challenge

This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper?heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real?world size revealed the success of the hyper?heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper?heuristic framework

Propuesta de un algoritmo dinámico para el problema de mantenimiento y ruteo de vehículos con ventanas de tiempo

Context:  In the context of business organizations, every process in which the product is immersed has a cost and time associated with it. The area of maintenance planning and scheduling is no exception; however, it is an aspect in which few companies specialize, tending to be outsourced. In this sense, the application of combinatorial models is a tool with a high potential to improve the overall performance of the organization through the understanding of the integral maintenance process. Method: A two-phase (maintenance and routing) dynamic algorithm is proposed which considers a set of clients distributed in a maintenance network (distance), where each of the technicians start from the same central node (depot), which, in turn, is the endpoint of each assigned route. The objective is to minimize the total cost associated with the development of preventive and corrective maintenance of all machines to be evaluated. With this purpose, the formulation of the mathematical problem for each of the phases and its interrelation method is proposed. Then, performance measures are expressed to evaluate the achieved objectives. Results: The results satisfy a consistent alternative for the resolution of problems of the NP-Hard type, which generates a high level of complexity to the model. That is, it proposes a tool for solving problems of these characteristics in low computational response times and with appealing results. Conclusions: The combined maintenance and routing model using a dynamic algorithm addresses the maintenance and routing problem satisfactorily. The model shows good results with respect to the comparison optimization model in percentage gaps of performance measures lower than 5%. As for the computational time required, a reduction of up to 98% was achieved, which makes it an ideal alternative for highly complex scenarios. Finally, achieving a higher level of characterization, employing multi-objective decision criteria and a greater number of constraints to the problem, is proposed in future research. Acknowledgements: To the High-Performance Computing Center (CECAD - Centro de computación de Alto Desempeño) of Universidad Distrital Francisco José de Caldas for their support, as well as for providing us with a virtual machine to run the proposed mathematical model, which was an essential element in the results obtained.Contexto: En el contexto de las organizaciones empresariales, todo proceso en el que está inmerso el producto conlleva un costo y un tiempo asociados. El área de planeación y programación de mantenimiento no es la excepción; sin embargo, es un aspecto en el cual pocas compañías se especializan, tendiendo a la tercerización. En este sentido, la aplicación de modelos combinatorios es una herramienta con un alto potencial de mejorar el desempeño global de la organización a través del entendimiento del proceso integral de mantenimiento. Método: Se plantea un algoritmo dinámico de dos fases (mantenimiento y ruteo) que considera un conjunto de clientes distribuidos en una red de mantenimiento (distancia) en el que cada uno de los técnicos parte del mismo nodo central (depósito), que a su vez es el punto final de cada ruta asignada. El objetivo consiste en minimizar el costo total asociado al desarrollo del mantenimiento tanto preventivo como correctivo de todas las máquinas a evaluar. Con esta finalidad se plantea la formulación del problema matemático para cada una de las fases y su método de interrelación. Después se expresan medidas de desempeño para evaluar los objetivos alcanzados. Resultados: Los resultados satisfacen una alternativa consistente para la resolución de problemas del tipo NP-Hard que genera un alto nivel de complejidad al modelo. Es decir, plantea una herramienta para resolución de problemas de estas características en tiempos de respuesta computacional reducidos y con resultados atractivos. Conclusiones: El modelo de mantenimiento y ruteo combinado usando un algoritmo dinámico permite abordar el problema de mantenimiento y ruteo de manera satisfactoria. El modelo presenta buenos resultados frente al modelo de optimización de comparación en brechas porcentuales de medidas de desempeño inferiores al 5 %. Respecto al tiempo computacional requerido, se logró una reducción de hasta el 98 %, lo cual lo convierte en una alternativa ideal para escenarios de gran complejidad. Finalmente, se propone en futuras investigaciones alcanzar un mayor nivel de caracterización por medio de criterios de decisión multiobjetivo y un mayor número de restricciones al problema. Agradecimientos: Al Centro para Computación de Alto Desempeño (CECAD) de la Universidad Distrital Francisco José de Caldas en su gestión y apoyo para el préstamo de la máquina virtual en la ejecución del modelo matemático planteado, como elemento esencial en los resultados alcanzados