Technician Routing Research Articles

The joint maintenance operation selection and technician routing problem

Numerous studies have explored the challenge of optimizing maintenance grouping, aiming to efficiently manage maintenance resources and reduce costs associated with various maintenance opportunities. Maintenance grouping is even more important when the systems to maintain are geographically distributed because it significantly reduces travel costs. In this research, we tackle a novel problem that integrates condition-based maintenance with the selection of maintenance operations and technician routing. The problem involves the selection of machines requiring maintenance for each time period, determining the nature of required operations (based on the uncertain degradation state of the machines), selecting suitable technicians based on their skills and availability, and planning their routes. We formulate this problem as a mixed-integer program and propose a heuristic approach for its solution. Our method constructs a solution by iteratively adding maintenance operations to technician routes. To assess the method’s performance, we conduct experiments that evaluate both running times and solution quality.

Technician routing and scheduling with employees’ learning through implicit cross-training strategy

With record high talent shortages and skill mismatches around the world, this paper investigates a variant of multi-period dynamic technician and routing problem that can be modeled as a Markov decision process. To deal with the double tradeoffs between the routing and service time costs, as well as the current and future costs, we propose an approximate dynamic programming (ADP)-based cost function approximation (CFA) algorithm — the implicit cross-training strategy (ICT). A two-phase routing and scheduling heuristic is developed to account for both employees’ learning and future information, and to facilitate an efficient implementation of CFA. Extensive computational results show that ICT can provide a better solution in the current decision with a global view in comparison with the myopic strategy. In depth analysis demonstrates that ICT trains the workforce with more balanced skillsets and workloads, which ensures the flexibility of the workforce and helps buffer against the future uncertainties with substantial routing cost savings. Additionally, ICT has much more advantages in large-scale problems with more diversified service requests and randomly distributed customers.

Technician routing and scheduling for the sharing economy

Efficient routing and scheduling plans for a modern workforce are challenging to develop for many firms offering services to customers at their home. In this paper, we focus on those firms that provide technical or maintenance related assistance using heterogeneously skilled technicians working in the sharing economy. We present a model that minimizes the costs of routing and scheduling these technicians, operating out of their own homes, serving a set of customers with demand for a variety of tasks revealed on a daily basis over a multi-period planning horizon. We construct this dynamic, stochastic problem as a Markov decision process and introduce a heuristic that takes advantage of problem characteristics to provide solutions efficiently. This heuristic simplifies the model by reducing the goal function and incorporating an approximation for routing cost, effectively and efficiently solving real world sized problems. We use the heuristic to provide insight into managerial decisions associated with managing a team of technicians, showing that the benefit of cross-training technicians has limitations and that in most circumstances it is more efficient to use technicians with focused areas of expertise.

Decision support for the technician routing and scheduling problem

AbstractThe 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.

Enhanced iterated local search for the technician routing and scheduling problem

Most public facilities in the European countries, including France, Germany, and the United Kingdom, were built during the reconstruction projects between 1950 and 1980. Owing to the deteriorating state of such vital infrastructure, its maintenance 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 minimize the overall costs. This includes planning technical interventions, workload balancing, productivity improvement, etc. In this paper, we focus on the routing of technicians and the scheduling of their tasks. We address for this purpose a variant of the workforce scheduling problem called the Technician Routing and Scheduling Problem. This problem has applications in different fields, such as maintenance of transportation infrastructure (rail and road networks), telecommunications, and sewage facilities. To solve the problem, we propose an enhanced iterated local search approach. The enhancement of the iterated local search firstly includes an intensification procedure that incorporates a set of local search operators and removal-repair heuristics adapted for the studied problem. 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 almost three times lower than that of the literature. Furthermore, eILS improved the best-known solution for 31 instances among a total of 56 while maintaining reasonable computational times.

Tactical planning for dynamic technician routing and scheduling problems

We consider the tactical planning aspect of a dynamic technician routing and scheduling problem with a time horizon of several days. In this problem study, some tasks are known beforehand, while others arrive dynamically. The tactical planning is to schedule the known tasks, such that we minimize the overall driving distance while ensuring short service times for the dynamic tasks. Without tactical planning, the known tasks would be spread throughout the whole area as they are scheduled based on a first-come-first-serve principle. In this study, we partition the area into disjoint slices covering the full plane and then assign the slices to individual work days. The partitioning of the area will be constructed with a balanced sweep algorithm using two approaches: The first is based on a parallel sweep-line following a given angle, while the second is based on a rotating sweep-line around a given origin. To guide the partitioning, we aim to minimize the degree of dynamism, which in our case is defined by the number of dynamic tasks, the distance from known tasks to dynamic tasks, and the tightness of the time windows. The tactical planning is evaluated using real-life and synthetic clustered data, where some of the tasks are known beforehand, and some are dynamic. We benchmark the various partitioning approaches against the default strategy of serving all known tasks on a first-come-first-serve basis. Computational results are reported showing around a 10% reduction in driving distance when using tactical planning. Furthermore, we show that up to 70% of the technicians can have a non-dynamic work day, without a significant increase in driving distance.

Service-oriented multi-skilled technician routing and scheduling problem for medical equipment maintenance with sudden breakdown

Motivated by the service-oriented intelligent maintenance activity under the development of the digital economy, we study the operation and maintenance strategies of the Maintenance Scheduling and Technician Routing Problem (MSTRP) for medical equipment implemented by multi-skilled technicians. We consider the maintenance services provided for both preventive maintenance (PM, e.g., periodic maintenance based on the contract schedule) and corrective maintenance (CM, e.g., repair action in the event of a sudden breakdown). As these activities are performed on equipment with various types of requirements at different geographically distributed systems, technicians with diverse qualifications need to build teams and visit the matching maintenance requests. Meanwhile, we consider the maintenance actions concerning the dynamic change in equipment failure rate and establish a flexible maintenance mechanism by balancing the total cost. This paper develops an efficient hybrid heuristic based on a genetic algorithm, variable neighborhood search, and other optimization strategies to solve this multi-period equipment maintenance scheduling and multi-skilled technician routing and scheduling problem. Based on the settings of the studied MSTRP, this paper designs different sizes of testing instances and multi-period explanatory cases before conducting extensive comparative experiments and sensitivity analysis to demonstrate the effectiveness of our proposed dynamic maintenance strategy and optimization method.

Enhanced Iterated Local Search for the Technician Routing and Scheduling Problem

Enhanced Iterated Local Search for the Technician Routing and Scheduling Problem

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.

A bi-objective approach based on fuzzy logic for multi-period technician routing and scheduling problems

This paper deals with a multi-period technician routing and scheduling problem. The problem carries out the tasks at various locations by being divided into teams considering differently skilled technicians. This paper provides a bi-objective mixed integer programming method to model the problem since the goals of the performed model are to optimize the travel cost while simultaneously minimizing the overtime and the waiting time. The paper introduces a fuzzy logic approach to solving the problem by offering a single Pareto solution to satisfy both objectives, rather than providing efficient solutions as in the case of classical multi-objective models. Finally, the paper presents the computational experiments and analyses to evaluate the efficiency of the suggested mathematical formulation and solution approach. The results demonstrate that the performed model accomplishes an acceptable satisfaction level.

Solving large-scale dynamic vehicle routing problems with stochastic requests

Dynamic vehicle routing problems (DVRPs) arise in several applications such as technician routing, meal delivery, and parcel shipping. We consider the DVRP with stochastic customer requests (DVRPSR), in which vehicles must be routed dynamically with the goal of maximizing the number of served requests. We model the DVRPSR as a multi-stage optimization problem, where the first-stage decision defines route plans for serving scheduled requests. Our main contributions are knapsack-based linear models to approximate accurately the expected reward-to-go, measured as the number of accepted requests, at any state of the stochastic system. These approximations are based on representing each vehicle as a knapsack with a capacity given by the remaining service time available along the vehicle’s route. We combine these approximations with optimal acceptance and assignment decision rules and derive efficient and high-performing online scheduling policies. We further leverage good predictions of the expected reward-to-go to design initial route plans that facilitate serving dynamic requests. Computational experiments on very large instances based on a real street network demonstrate the effectiveness of the proposed methods in prescribing high-quality offline route plans and online scheduling decisions.

Technician Collaboration and Routing Optimization in Global Maintenance Scheduling for Multi-Center Service Networks

With the popularization of service-oriented manufacturing, the current operation & maintenance (O&M) has shifted from traditional in-house maintenance to proactive outsourcing maintenance. It is paramount for an original equipment manufacturer (OEM) to provide timely and cost-effective maintenance schemes to geographically distributed customer enterprises. Interestingly, transportation theories could be combined to facilitate multi-location O&M management. In this paper, a transportation-oriented cross-regional opportunistic maintenance (TCOM) policy is developed for solving O&M optimizations and planning real-time schemes for the multi-center service network. The optimization model of this TCOM policy addresses several inter-related decisions: (1) most suitable maintenance times for each leased machine, (2) cost-effective arrangements of technician teams to perform maintenance tasks, and (3) optimal service routes for required teams. We not only integrate maintenance grouping and technician routing, but also investigate the new issues arising from the collaborative sharing of technician teams belonging to different maintenance centers. Numerical examples show that this TCOM policy can achieve significant cost-saving in cross-regional maintenance grouping and multi-location routing optimization for OEMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical problem of computing an optimal maintenance policy for the multi-center service network of geographically distributed production systems to achieve a timely and cost-effective O&M management. We consider individual machine degradations, complex maintenance opportunities and network logistics optimization to establish a global maintenance scheme for the multi-center service network. The existing approaches are to schedule the preventive maintenance (PM) for a single machine or multi-unit leased system, which is unilateral and cannot be applied to multi-location production systems. Although the network opportunistic maintenance policy has been proposed in the literature for maintenance grouping and technician routing problem, it still does not consider the limited workforce resource and the collaborative sharing of technician teams from different maintenance centers. To fill this literature gap, in this paper, we develop the TCOM policy to determine the optimal maintenance timetable for leased machines, as well as the cost-effective service route of technician teams belonging to multiple maintenance centers, so as to conduct timely and cost-effective PM on multi-location production systems. Compared with the conventional maintenance policy, our adaptive policy greatly reduces the total outsourcing maintenance cost in long-term O&M services.

A reliability-and-cost-based framework to optimize maintenance planning and diverse-skilled technician routing for geographically distributed systems

We present an agile maintenance framework where technician assignment and maintenance schedules are planned jointly to ensure timely operation and maintenance (O&M) services. Such an agile framework can quickly respond to organizational scheduling requirements while controlling service costs and not compromising machine reliability. For preventive maintenance (PM) and replacement tasks, a diverse-skilled technician organizing and routing model (D-STORM) is developed for geographically distributed systems with the following decision variables: (1) the scheduled maintenance start time, (2) the appropriate maintenance level (i.e. PM or replacement) for a network of machines, (3) the composition of technician teams subject to constraints controlling the number of technicians per team and their skill levels, (4) the optimal service route for each team. And the two objectives are: (1) maximizing the total reliability improvement and (2) minimizing the total service cost. Other than traditional maintenance policies, we propose a machine-team-technician assignment approach to provide a flexible and unified maintenance framework that can incorporate diverse-skilled technicians and multi-level maintenance operations. Numerical studies show significant advantages in terms of improved reliability and reduced costs, providing a set of alternative solutions for global original equipment manufacturers (OEMs). Moreover, Pareto solutions help OEMs to adopt the most appropriate maintenance scheme for their practices.

A simple solution to technician routing and scheduling problem using improved genetic algorithm

A simple solution to technician routing and scheduling problem using improved genetic algorithm

Optimization of the technician routing and scheduling problem for a telecommunication industry

Optimization of the technician routing and scheduling problem for a telecommunication industry

Matheuristics and Column Generation for a Basic Technician Routing Problem

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

Examination of Daily Task Constraint in Technician Routing and Scheduling Problem

This paper deals with a multi-term technician routing and programming problem with a different concept. The problem is that technicians with various skills form teams, and these teams perform the tasks that take in distinct locations. Technicians have daily duty capacities while performing tasks. The cases where overtime costs are allowed by combining different technician numbers are analyzed. Paper provides a single-purpose mixed integer programming method for modeling the problem because the model implemented aims to optimize the travel cost while simultaneously minimizing the amount of overwork. The main issue also specifies the division of technicians with different skills into teams, assignment of tasks with different skill requirements to teams, and routes for each team at the same time. The number of customers visited on a route within the daily customer limit. In addition, the model included exceeding the daily customer limit in the model and bound it to a constraint. The various combinations of the daily normal and over-work number of customers were evaluated and examined their effects on the objective function. Finally, the paper presents computational experiments and analyses to evaluate the efficiency of the proposed mathematical formulation and solution approach. The results show that daily task constraints are effective in employee selection and amount of use.

Service-oriented global optimization integrating maintenance grouping and technician routing for multi-location multi-unit production systems

With the product-service requirement of modern production enterprises, service-oriented manufacturing and its corresponding operations and maintenance have gained growing attention. Advances in sensor technology and wireless communication, promoting lessors to propose new strategies for intelligent maintenance decision-making of geographically distributed manufacturing enterprises. In this article, we present a comprehensive strategy for solving the maintenance grouping and technician routing problem of multi-location multi-unit production systems. Based on real-time machine degradation, we estimate the failure rate of leased machines and establish a time-varying maintenance cost function to quantify the trade-off between early maintenance and delayed maintenance. Unlike group maintenance of a single system, we integrate the travel time between systems and the maintenance capacity of technician teams into a mixed-integer optimization model to provide the dynamic preventive maintenance scheme. Finally, numerical examples are employed to illustrate the effectiveness of the proposed strategy and explore some managerial insights for the lessor’s daily management.

Branch-and-Price for a Multi-attribute Technician Routing and Scheduling Problem

Branch-and-Price for a Multi-attribute Technician Routing and Scheduling Problem

A new distributed optimization approach for home healthcare routing and scheduling problem

Home health care faces new challenges day by day and it has become increasingly legitimate in the face of an aging population. Home healthcare centers are exposed to cumulative demands and academics are paying attention to the routing and scheduling matter, which is offered in literature as a Technician Routing and Scheduling Problem (TRSP) where the aim is to minimize the total cost subject to the time windows constraints to serve the patients respecting their priorities. In this paper, we develop a new distributed algorithm to resolve the home health care routing and scheduling problem (HHRSP). The principal idea of this algorithm is to apply artificial intelligence techniques in a distributed optimization method. The integration of automatic learning and search methods are applied to optimize the assignment of appointments to home caregivers. It allows us to gain time, effort, especially cost, and while complying with the problem constraints. The comparison results prove the efficacy of the recommended approach, which can offer decision support for medical executives of home health care.