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The technician routing and scheduling problem (TRSP) optimizes routes for technicians serving tasks subject to qualifications, time constraints, and routing costs. In the literature, the TRSP is solved either to provide actual technician work schedules or to perform what‐if analyses on different TRSP scenarios. A TRSP scenario consists of a given number of tasks, technicians, skills, working hours and so forth. We present a method which builds optimal TRSP scenarios with respect to technician fleet, their skills, their working hours and digitization of task equipment. The scenarios are built such that the combined TRSP costs (OPEX) and investment costs (CAPEX) are minimized. By using a holistic approach we can generate scenarios that would not have been found by studying the investments individually. The proposed method consists of a matheuristic based on column generation. To reduce computational time, the routing costs of a technician are estimated instead of solved to optimality. The proposed method is evaluated on data from the literature and on real‐life data from a telecommunication company. The evaluation shows that the proposed method successfully suggests attractive scenarios. The method especially excels in ensuring that more tasks are serviced, but also in reducing travel time with around 16% in the real‐life instance. We believe that the proposed method could constitute an important strategic tool for routing companies. In the conclusion, we propose future research directions to extend the applicability. [ABSTRACT FROM AUTHOR]

This research studies the Field Technician Scheduling Problem with Experience-Dependent Service Times (FTSP–EDST), involving three main features: matching maintenance tasks with available technicians, sequencing the tasks, and considering the experience-dependent service times. Given a limited number of technicians, the objective is to maximize the collected profit for servicing tasks. This study formulates the problem as a mixed-integer linear programming model and proposes a Modified Iterated Local Search (MILS) to solve the benchmark problem instances of various sizes. A set of FTSP–EDST instances is generated based on existing publicly accessible data, and MILS is utilized to solve these newly generated instances. Computational results confirm the effectiveness of MILS in solving FTSP–EDST. [ABSTRACT FROM AUTHOR]

In this study, a multi-period technician routing and scheduling problem is addressed. The problem involves performing tasks at various locations by dividing them into teams of differently skilled technicians. Additionally, a bi-objective mixed-integer programming method is provided to model the problem as the goals of the model are to optimize the travel cost while simultaneously minimizing the overtime and waiting time. Furthermore, a fuzzy logic approach is introduced to solve the problem by offering a single Pareto solution to satisfy both objectives rather than providing efficient solutions, as in classical multi-objective models. Finally, computational experiments and analyses are presented to evaluate the efficiency of the proposed mathematical formulation and solution approach. The results demonstrate that the proposed model performed satisfactorily. [ABSTRACT FROM AUTHOR]

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.

This research studies the Field Technician Scheduling Problem with Experience-Dependent Service Times (FTSP–EDST), involving three main features: matching maintenance tasks with available technicians, sequencing the tasks, and considering the experience-dependent service times. Given a limited number of technicians, the objective is to maximize the collected profit for servicing tasks. This study formulates the problem as a mixed-integer linear programming model and proposes a Modified Iterated Local Search (MILS) to solve the benchmark problem instances of various sizes. A set of FTSP–EDST instances is generated based on existing publicly accessible data, and MILS is utilized to solve these newly generated instances. Computational results confirm the effectiveness of MILS in solving FTSP–EDST.

This paper proposes an improved genetic algorithm (IGA) to provide feasible solutions to the technician routing and scheduling problem (TRSP). The TRSP covers the feasible team generation, the assignment of feasible teams to suitable tasks, the proficiency level of workers, routings considering the allowed days, and the skill desire of the task. The paper deals with a five-day multi-period planning horizon, and a task is performed in any one of 5 days. The IGA consists of crossover, mutation, and three neighborhood structures. Mutation and crossover largely try to avoid getting caught in the local solution trap. Three neighborhood structures improve genetic algorithm (GA) by searching for better solutions. Further, the performance of the proposed algorithm is experimentally compared with GA and improved particle swarm optimization (IPSO) algorithm by providing the TRSP solutions on the generated benchmark instances. The numerical results indicate that IGA offers fast and better solutions considering GA and IPSO algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the technician routing and scheduling problem (TRSP). The TRSP consists of the assignment of technicians into teams, the assignment of teams to tasks, the construction of routes, and the selection of the day on which a service is provided by considering the proficiency level of workers and the proficiency requirement of the task. The paper considers the planning horizon as a multi-period covering 5 days, which further increases the complexity of the problem. Then a task can be fulfilled in any one of 5 days. The IPSO algorithm includes a particle swarm optimization (PSO) algorithm and one neighborhood operator. One neighborhood operator is used to avoid the local solution trap since the global best solution found by PSO is falling into a local solution trap. Further, the proposed algorithm's performance is experimentally compared with the branch-and-cut algorithm for the solution of the TRSP, on the benchmark instances generated from the literature. The computational results show that IPSO provides better solutions considering the branch-and-cut algorithm within reasonable computing time. [ABSTRACT FROM AUTHOR]

The VeRoLog Solver Challenge 2018–2019 of the EURO working group vehicle routing and logistics (VeRoLog) considers a multiperiod vehicle and technician routing and scheduling problem. This paper proposes a combination of large neighborhood and local search heuristics and a decomposition approach to efficiently generate competitive solutions under restricted computational resources. The interplay of the heuristics, the decomposition, and the way the search space is explored are orchestrated by an adaptive layer that explicitly considers the instance to be solved, a time limit and the performance of the computing environment. In a computational study it is shown that the method is efficient and effective, especially under tight time restrictions. [ABSTRACT FROM AUTHOR]

The Technician Routing and Scheduling Problem (TRSP) consists in routing staff to serve requests for service, taking into account time windows, skills, tools, and spare parts. Typical applications include maintenance operations and staff routing in telecoms, public utilities, and in the health care industry. In this paper, we present a formal definition of the TRSP, discuss its relation with the Vehicle Routing Problem with Time Windows (VRPTW), and review related research. From a methodological perspective, we describe a matheuristic composed of a constructive heuristic, a parallel Adaptive Large Neighborhood Search, and a mathematical programming based post-optimization procedure that successfully tackles the TRSP. We validate the matheuristic on the Solomon VRPTW instances, where we achieve an average gap of $$0.23\,\%$$ , and matched 44 out of 55 optimal solutions. Finally, we illustrate how the matheuristic successfully solves a set of TRSP instances extended from the Solomon benchmark. [ABSTRACT FROM AUTHOR]