Technician Scheduling Research Articles

Robust Optimization for Technician and Resource Management in Reconfigurable Assembly Lines

Research studies have rarely taken into consideration the challenge of resource selection and technician scheduling problems in the context of product evolution. Indeed, incorporating new product variants into the line’s future production generation has a significant impact on resource selection and technician scheduling. Additional resources may be necessary to produce new products, and each of these resources demands specialized skill for operation. Hence, it may be necessary to provide training for the technicians to enable them to effectively work with the new resources. This study aims to plan resource selection and technician scheduling for a reconfigurable assembly line. This line produces a given product family with a set of product variants. Moreover, a new product variant will be added after a certain period of time, named production generation. At each generation, the product family can be changed by adding or removing a product variant. We developed a Mixed-Integer Linear Programming (MILP) that minimizes the total resource selection and technician training costs for the worst-case scenario. The MILP is validated through solving a simple example and the results are given.

The Field Technician Scheduling Problem with Experience-Dependent Service Times

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.

Optimization models for patient and technician scheduling in hemodialysis centers.

Patient and technician scheduling problem in hemodialysis centers presents a unique setting in healthcare operations as (1) unlike other healthcare problems, dialysis appointments have a steady state and the treatment times are determined in advance of the appointments, and (2) once the appointments are set, technicians will have to be assigned to two types of jobs per appointment: putting on and taking off patients (connecting to and disconnecting from dialysis machines). In this study, we design a mixed-integer programming model to minimize technicians' operating costs (regular and overtime costs) at large-scale hemodialysis centers. As this formulation proves to be computationally challenging to solve, we propose a novel reformulation of the problem as a discrete-time assignment model and prove that the two formulations are equivalent under a specific condition. We then simulate instances based on the data from our collaborating hemodialysis center to evaluate the performance of our proposed formulations. We compare our results to the current scheduling policy at the center. In our numerical analysis, we reduced the technician operating costs by 17% on average (up to 49%) compared to the current practice. We further conduct a post-optimality analysis and develop a predictive model that can estimate the number of required technicians based on the center's attributes and patients' input variables. Our predictive model reveals that the optimal number of technicians is strongly related to the time flexibility of patients and their dialysis times. Our findings can help clinic managers at hemodialysis centers to accurately estimate the technician requirements.

Short-term aviation maintenance technician scheduling based on dynamic task disassembly mechanism

This paper focuses on the aviation maintenance technician scheduling (AMTS) problem and formulates AMTS and AMTS-DTDM model with a practical dynamic task disassembly mechanism (DTDM) and arrange maintenance technicians across shifts in short-term maintenance situations (less than 24–48 h). In DTDM, four technician scheduling modes are devised to flexibly disassemble the overtime work or reassigned it to other maintenance technicians according to work efficiency and progress, which could shorten the maintenance time and save the total cost. Moreover, a flexible time interval is designed to adjust the boundary of task disassembly. To further study the effectiveness of DTDM in reducing maintenance time and total costs, this paper divides both the AMTS and AMTS-DTDM models into three sub-models (i.e., two single-objective models and one multi-objective model), respectively. After that, we design solution methods as well as the encoding schemes compatible with both the AMTS model and the AMTS-DTDM model for different problem scales. Finally, to verify the effectiveness of DTDM, four groups of experiments are set up and particle swarm optimization (PSO) and multi-objective particle swarm optimization (MOPSO) are applied to compare AMTS and AMTS-DTDM sub-models. The experimental results show that the AMTS-DTDM model can effectively shorten the maintenance time and reduce the total costs for different scaled problems. Furthermore, the flexible time interval can facilitate more options for airlines to adjust the two above objectives to a small extent. The increase in flexible time may weaken the advantages of the AMTS-DTDM model while improving the rationality of the ultimate technician scheduling scheme.

Perancangan Model Penjadwalan Teknisi pada Perawatan BD Check Pesawat Airbus dengan Memperhitungkan Beban Kerja

<p><strong>PT GMF AeroAsia is engaged in the maintenance, repair, and overhaul (MRO) of aircraft. It is known that GMF is the largest domestic MRO company at 30% while 70% is owned by foreign MRO companies. Based on the company's vision to make the company a world-class MRO, it is necessary to develop a business scheme in terms of capability, quantity, and quality. However, the scheduling of technicians in carrying out aircraft maintenance is currently unable to meet the demand every hour due to the uncertain flight schedule of the aircraft every day so that the workload of each technician exceeds the normal workload. The calculation of the workload using the full-time equivalent method showed that the workload of technicians was overloaded so that an additional number of technicians was needed, as well as analyzing the causes and consequences of the excessive workload of technicians using the fishbone diagram method and modeling the formulation of the technician scheduling problem that works in maintenance before departure check Airbus aircraft in the form of integer linear programming with decision variables such as the number of technicians, the number of shifts and the number of working days, then the constraint function is the constraints to be achieved in modeling, and the objective function is to minimize the number of technicians employed. Furthermore, the model is implemented using the LINGO 19.0 software so that it is found that the model can produce an optimal technician schedule in scheduling.</strong></p><p><strong><em>Keywords</em></strong> - <em>Fishbone Diagram, Full Time Equivalent, Integer Linear Programming, Lingo Software, Matchematical Modeling, Scheduling, Workload</em><em></em></p>

Branch-and-Bound Algorithm for Solving it Technician Scheduling Problems with Minimum Total Delay Considering Task Release Time

Branch-and-Bound Algorithm for Solving it Technician Scheduling Problems with Minimum Total Delay Considering Task Release Time

Aviation maintenance technician scheduling with personnel satisfaction based on interactive multi‐swarm bacterial foraging optimization

Aviation maintenance technician scheduling with personnel satisfaction based on interactive multi‐swarm bacterial foraging optimization

RETRACTED: An Efficient Metaheuristic Approach for the Multi-Period Technician Routing and Scheduling Problem

This article has been retracted at the request of the Editor-in-Chief and of co authors W. R. Cherif-Khettaf and M. Mostefai, as the article is largely based on work presented previously at an international conference by the same authors.
 The conference paper is:
 A. Khattara, W. R. Cherif-Khettaf and M. Mostefai, "Variable neighborhood search procedures for the multi-period technician routing and scheduling problem", 4th International Conference on Control, Decision and Information Technologies (CoDIT), Spain, April 5-7, 2017
 and the corresponding author of this submission was unaware of the publication of this conference's proceedings at the time of submission and review/publication of this paper.

An approximate dynamic programming method for the multi-period technician scheduling problem with experience-based service times and stochastic customers

In this paper, we study how an organization can recognize that individuals learn when assigning employees to tasks. By doing so, an organization can meet current demands and position the capabilities of their workforce for the yet unknown demands in future days. Specifically, we study a variant of the technician and task scheduling problem in which the tasks to be performed in the current day are known, but there is uncertainty regarding the tasks to be performed in subsequent days. To solve this problem, we present an Approximate Dynamic Programming-based approach that incorporates into daily assignment decisions estimates of the long-term benefits associated with experience accumulation. We benchmark this approach against an approach that only considers the impact of experience accumulation on just the next day's productivity and show that the ADP approach outperforms this one-step lookahead approach. Finally, based on the results from an extensive computational study we derive insights into how an organization can schedule their employees in a manner that enables meeting both near and long-term demands.

Multi-period technician scheduling with experience-based service times and stochastic customers

This paper introduces the multi-period technician scheduling problem with experience-based service times and stochastic customers. In the problem, a manager must assign tasks of different types that are revealed at the start of each day to technicians who must complete the tasks that same day. As a technician gains experience with a type of task, the time that it takes to serve future tasks of that type is reduced (often referred to as experiential learning). As such, while the problem could be modeled as a single-period problem (i.e. focusing solely on the current day’s tasks), we instead choose to model it as a multi-period problem and thus capture that daily decisions should recognize the long-term effects of learning. Specifically, we model the problem as a Markov decision process and introduce an approximate dynamic programming-based solution approach. The model can be adapted to handle cases of worker attrition and new task types. The solution approach relies on an approximation of the cost-to-go that uses forecasts of the next day’s assignments for each technician and the resulting estimated time it will take to service those assignments given current period decisions. Using an extensive computational study, we demonstrate the value of our approach versus a myopic solution approach that views the problem as a single-period problem.

A biased random key genetic algorithm for the field technician scheduling problem

This paper addresses a problem that service companies often face: the field technician scheduling problem. The problem considers the assignment of a set of jobs or service tasks to a group of technicians. The tasks are in different locations within a city, with different time windows, priorities, and processing times. Technicians have different skills and working hours. The main objective is to maximize the sum of priority values associated with the tasks performed each day. Due to the complexity of this problem, constructive heuristics that explore specific characteristics of the problem are developed. A customized Biased Random Key Genetic Algorithm (BRKGA) is also proposed. Computational tests with 1040 instances are presented. The constructive heuristics outperformed a heuristic of the literature in 90% of the instances. In a comparative study with optimal solutions obtained for small-sized problems, the BRKGA reached 99% of the optimal values; for medium- and large-sized problems, the BRKGA provided solutions that are on average 3.6% below the upper bounds.

Effective Heuristic Procedures for a Field Technician Scheduling Problem

This paper addresses a field technician scheduling problem faced by many service providers in telecommunication industry. The problem is to assign a set of jobs, at different locations with time windows, to a group of field technicians with different job skills. Such a problem can be viewed as a generalization of the well-known vehicle routing problem with time windows since technician skills need to be matched with job types. We designed and tested several heuristic procedures for solving the problem, namely a greedy heuristic, a local search algorithm, and a greedy randomized adaptive search procedure (GRASP). Our computational results indicate that GRASP is the most effective among them but requires more CPU time. However, the unique structure of GRASP allows us to exploit parallelism to achieve linear speed-up with respect to the number of machines used.