Assembly Line Worker Assignment Research Articles

MULTI-OBJECTIVE GENETIC ALGORITHM FOR THE ASSEMBLY LINE WORKER ASSIGNMENT AND BALANCING PROBLEM: A CASE STUDY IN THE AUTOMOTIVE SUPPLY INDUSTRY

The primary challenge in assembly line design is the need for more appropriately allocating tasks and workers to workstations. This study addresses the problem of line balancing and worker assignments, considering the performance disparities among workers during the line balancing process. In the relevant literature, this problem is known as the Assembly Line Worker Assignment and Balancing (ALWAB) problem. This research examines a multi-objective ALWAB Type-2 problem, simultaneously evaluating cycle time and squared load assignment objectives. The study is conducted based on a real-life scenario in a sub-industry automotive industry that manufactures cable equipment. To solve this problem, a multi-objective genetic algorithm approach is proposed. Recognising that the selection of parameter values will influence the algorithm’s performance, parameter calibration has been performed. A full factorial experimental design and the irace method have been utilised for this purpose. The results are compared with those using parameter values utilised for similar problems in the literature. Furthermore, a sensitivity analysis has been carried out to examine the impact of various relative weight values of the objectives on the result. The results indicate that the experimental design generally yields superior results compared to other methods.

Model and metaheuristic for human–robot collaboration assembly line worker assignment and balancing problem

Given the Industry 5.0 trend towards human-centric and resilient industries, workers continue to be considered as a valuable and irreplaceable resource. Human–robot collaboration (HRC) is a promising production mode that combines the advantages of both human workers and robots, resulting in improved productivity and reduced ergonomic risks for workers. In this study, we present one of the first attempts to address the human–robot collaboration assembly line worker assignment and balancing problem (HRCALWABP), which allows workers and robots to perform tasks in parallel or in collaboration. Workers are heterogeneous, leading to variations in task execution times. We formulate a mixed-integer linear programming (MILP) model that minimizes the cycle time and propose a tight lower/upper bound. A rebalancing process is further considered in this study. Due to the complexity of HRCALWABP, we develop an improved adaptive neighborhood simulated annealing algorithm (IASA). An adaptive mechanism is applied to the IASA to dynamically adjust the selection probability of the designed neighborhood structures and operators, and a restart mechanism is developed for improving the search capacity. Extensive computational experiments are conducted to evaluate the performance of both the MILP and IASA, and the results confirm the superiority of the IASA compared to the MILP and other existing algorithms.

Exact and heuristic methods for a workload allocation problem with chain precedence constraints

Industrial manufacturing is often organized in assembly lines where a product is assembled on a sequence of stations, each of which executes some of the assembly tasks. A line is balanced if the maximum total execution time of any station is minimal. Commonly, the task execution order is constrained by precedences, and task execution times are independent of the station performing the task. Here, we consider a recent variation, called the “(Calzedonia) Workload Allocation Problem” (WAP), where the precedences form a chain, and the execution time of a task depends on the worker executing it. This problem was recently proposed by Battarra et al. (2020) and it is a special case of the Assembly Line Worker Assignment and Balancing Problem Miralles et al. (2007) where precedence relations are arbitrary. In this paper we consider the computational complexity of the problem and prove its NP-hardness. To solve the problem, we provide different lower bounds and exact and heuristic procedures. The performance of the proposed methods is tested on previously proposed instances and on new, larger instances with the same characteristics. The results show that the proposed methods can solve instances with up to about 4000 tasks and 29 workers, doubling the size of the instances that previously could be solved to optimality.

Modelling and prediction of worker task performance using a knowledge-based system application

It is a difficult task for an assembly line manager to select an appropriate worker from the available workers' list to assign to an assembly line workstation. Since each available worker has a unique set of skills and abilities, this research considers the worker differences in their work performance. The worker's work performance is considered based on their working speed or productivity. Task execution time (TET) is the measure used to distinguish the worker's work performance. The TET prediction is made by the application of a knowledge-based system framework. Workers' historical work-time data is used to model the knowledge objects. Workers are classified as skilled and semi-skilled respective methodologies are given for both categories of workers. Statistical-based learning algorithms are proposed for skilled workers based on the worker's age, gender, and work skill. Similarly, worker's learning patterns are used for semi-skilled workers. The predicted TET is used in solving the assembly line worker assignment problem. The second part of this work is to prioritise the aged worker during the worker selection without increment of worker count. The illustrative example helps understand the scope of the proposed methodology in an assembly line worker assignment problem. • A novel method is proposed to model and predict an assembly line worker’ work performance. • Statistical-based learning techniques are used for performance modelling, and Knowledge-based system (KBS) methodology is used for the performance prediction. • The predicted performance can be integrated with the assembly line worker assignment problem. • Lexicographic goal programming is added at the end of the worker assignment problem to prioritise the aged worker during the worker allocation. • The developed framework is helpful for an assembly line manager during worker allocation.

Assembly line balancing and worker assignment considering workers’ expertise and perceived physical effort

In manual assembly systems, workers’ differences in terms of skills, level of expertise and perceived physical effort largely affect the assembly line balancing and system performance. Traditional long-term strategic decisions may not respond to workforce changes and needs, resulting in frequent requests for line rebalancing. In this study, we propose a methodological framework and an easy-to-use Assembly Line Worker Assignment and Rebalancing Problem with different options: workers’ assignment considering their performance variability, integration of worker dependent physical exertion constraints and possibility to use trainers to assist inexperienced workers. A bi-objective linear programming model is proposed aiming to minimise the cycle time and the number of reassigned tasks to respect the initial design while integrating new workers with different characteristics. The -constraint approach is used to build Pareto frontiers for this bi-objective problem. This approach is applied to three real cases. The obtained results show that the developed model can be successfully used in manufacturing companies to help the production managers to deal with workforce turnover and skills heterogeneity.

A Multi-Manned Assembly Line Worker Assignment and Balancing Problem With Positional Constraints

One of main operational issues in assembly lines is to assign tasks and workers to workstations so that the maximum productivity can be achieved. Many previous studies have addressed this problem, but most of them have assumed one worker in each workstation or multiple workers with the same skill level in a workstation. In this study, we consider the worker assignment and balancing problem for a real automotive parts assembly line where multiple workers simultaneously perform different tasks in a workstation and the processing time of each task varies by the worker. We also introduce two new positional constraints to secure each worker's working space, required from a company. The objective is to minimize the cycle time. We propose a filtered beam search (FBS) algorithm that adjusts beam and filter widths depending on the gap between an upper bound and a lower bound to quickly obtain the efficient assignment for large-sized instances. Several dominance rules for eliminating redundant nodes and lower bounds for selecting promising nodes are developed for the FBS algorithm. The FBS algorithm is compared to a branch and bound algorithm and a simulated annealing algorithm.

Developments in the Literature and New Study Recommendations Within the Scope of the Assembly Line Worker Assignment and Balancing Problem (ALWABP)

In this study, by defining the Assembly Line Worker Assignment and Balancing Problem (ALWABP), the point that has been reached to this day is researched on ALWABP. The studies in question are evaluated comprehensively in terms of their objective functions, established decision models, additional constraints that are being considered, the solution method that is applied, the size of the problem, the data that were used in the research, the type of assembly line that was used in the research, and the product model variety. For this purpose, a total of 41 articles, which are presented on the Web of Science (WoS), Scopus, and Google Scholar databases, are reviewed from the time that this problem first appeared in 2007, up to 2022. Keywords that are used while researching the mentioned databases are “Assembly Line Worker Assignment and Balancing Problem (ALWABP)”, “Assembly Line Balancing Problem (ALBP)”, and “Worker Assignment Problem (WAP)”. Based on the articles examined in the study, the developments in the literature concerning the subject of ALWABP have been discussed, and the required study themes in this area have been determined. Since the research performed is a study that has not taken any place in the literature to this day, it is considered that it can shed light on the research that might be needed in the future in the ALWABP area.

Developments in the Literature and New Study Recommendations Within the Scope of the Assembly Line Worker Assignment and Balancing Problem (ALWABP)

In this study, by defining the Assembly Line Worker Assignment and Balancing Problem (ALWABP), the point that has been reached to this day is researched on ALWABP. The studies in question are evaluated comprehensively in terms of their objective functions, established decision models, additional constraints that are being considered, the solution method that is applied, the size of the problem, the data that were used in the research, the type of assembly line that was used in the research, and the product model variety. For this purpose, a total of 41 articles, which are presented on the Web of Science (WoS), Scopus, and Google Scholar databases, are reviewed from the time that this problem first appeared in 2007, up to 2022. Keywords that are used while researching the mentioned databases are “Assembly Line Worker Assignment and Balancing Problem (ALWABP)”, “Assembly Line Balancing Problem (ALBP)”, and “Worker Assignment Problem (WAP)”. Based on the articles examined in the study, the developments in the literature concerning the subject of ALWABP have been discussed, and the required study themes in this area have been determined. Since the research performed is a study that has not taken any place in the literature to this day, it is considered that it can shed light on the research that might be needed in the future in the ALWABP area.

A New Ergonomic Assembly Line Worker Assignment and Balancing Model Proposal Considering the Heterogenous Worker Characteristics

A New Ergonomic Assembly Line Worker Assignment and Balancing Model Proposal Considering the Heterogenous Worker Characteristics

A digital-twin based worker's work allocation framework for a collaborative assembly system

Assigning the right task to the right worker remains a challenge to an assembly line manager. This work is proposed to use digital-twin technologies for Assembly line worker assignment and balancing problems (ALWABP) for a collaborative assembly system. Applications of the Internet of Things, Data analytics and Learning algorithms are proposed to estimate the worker's work skill and task performance. Assembly line Digital Twin and collaborative robot (CoBot) work simulation tools are proposed to estimate the CoBot's assembly task performing ability and execution time. ALWABP framework is proposed to optimize the work allocation between the worker and the CoBot. Assembly line digital twin is furthermore proposed to simulate the ALWABP output during the decision-making process of the assembly line manager by considering the Human-in-the-loop decision-making system.

A New Ergonomic Assembly Line Worker Assignment and Balancing Model Proposal Considering the Heterogenous Worker Characteristics

A New Ergonomic Assembly Line Worker Assignment and Balancing Model Proposal Considering the Heterogenous Worker Characteristics

Risk-averse assembly line worker assignment and balancing problem with limited temporary workers and moving workers

Assembly line worker assignment and balancing problem (ALWABP) is an important research topic originated from sheltered work centres for disabled, in which workforce is assumed to be heterogeneous due to their disabilities. Since the employment of disabled workers may sustain higher absenteeism rates due to their health, especially under COVID-19, employing temporary workers to fill labour shortage is a crucial issue. In addition, in practice, the movement of workers between stations on assemble lines can increase the flexibility of worker assignment. In this study, we investigate a new risk-averse ALWABP with uncertain disabled worker availability, limited temporary workers and moving workers. The objective is to minimise the risk-averse weighted sum of the cycle time and the number of employed temporary workers. For the problem, a risk-averse two-stage stochastic programming model is formulated. The first stage assigns specific tasks (called fixed tasks) to stations, while the second stage assigns workers and remaining tasks (called flexible tasks) to stations. A genetic algorithm combining K-means clustering approach and variable neighbourhood search (GAKV) is designed. Experiment results show the superiority of the GAKV in terms of solution quality and computation time compared with sample average approximation (SAA). In addition, managerial insights are drawn.

Conserving workforce while temporarily rebalancing assembly lines under demand disruption

ABSTRACT In stable circumstances, assembly lines have workers with different capabilities assigned to stations. They perform a set of specialised tasks multiple times daily. Under a situation of high demand disruption (e.g. the COVID-19 pandemic), the overproduction rate would lead inventory levels to soar. An approach to cope with these demand drops and ongoing workforce costs is to dismiss employees and rebalance the line. Nevertheless, this implies social and economic costs related to rehiring and training. Alternatively, agreements can be made to reduce workload with a proportional wage deduction. These decisions are particularly challenging in heterogeneous workforces. We propose a Mixed-Integer Linear Programming (MILP) model to address the Assembly Line Worker Assignment and Rebalancing Problem (ALWARP). Our model aims at preserving jobs while minimising labour costs. We consider scenarios with falling demands and impose regularity metrics on workload reductions. Computational tests on benchmark datasets show that our strategy can distribute social costs among workers, with only slightly higher cumulative labour hours, while avoiding inconveniences associated with future renovations. A real-world case study of a truck cabin assembly line is investigated: the model can easily incorporate many realistic features and decide which workers should have their workload reduced and at which rate.

Modeling and solving assembly line worker assignment and balancing problem with sequence-dependent setup times

Assembly lines appear with various differentiations in order to better include the disabled in the labor market and to increase production efficiency. In this way, the optimal workforce assignment problem that emerges heterogeneously is called assembly line worker assignment and balancing problem (ALWABP). This paper addresses the ALWABP where the simple version is enriched by considering sequence-dependent setup times between tasks. A mixed integer linear programming model is presented, and a simulated annealing algorithm is developed such as an NP-hard problem. In order to test the proposed solutions, 640 benchmark problems in the literature were combined and used. The solutions obtained through using the proposed algorithm are compared with the mixed integer programming model on the small-size test problems. Experimental results show that the proposed algorithm is more effective and robust for a large set of benchmark problems.

Balancing assembly lines operating with heterogeneous workers under uncertainty in task processing times

PurposeThis paper considers the assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times. This problem is an extension of the (simple) SALBP-2 in which task times are worker-dependent and concurrently uncertain. Two criteria are simultaneously considered for minimization, namely, fuzzy cycle time and fuzzy smoothness index.Design/methodology/approachFirst, we show how fuzzy concepts can be used for managing uncertain task times. Then, we present a multiobjective genetic algorithm (MOGA) to solve the problem. MOGA is devoted to the search for Pareto-optimal solutions. For facilitating effective trade-off decision-making, two different MO approaches are implemented and tested within MOGA: a weighted-sum based approach and a Pareto-based approach.FindingsExperiments over a set of fuzzified test problems show the effect of these approaches on the performance of MOGA while verifying its efficiency in terms of both solution and time quality.Originality/valueTo the author’s knowledge, no previous published work in the literature has studied the biobjective assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times.

Bi-criteria Assembly Line Worker Assignment and Balancing Problem by Considering Labor Cost

Assignment of worker appears as a new problem in the literature for assembly lines (AL) where the workforce is heterogeneously located. The difference of this line balancing from the traditional line is that the assembly task times of the workers to be assigned to the stations differ from each other. Especially assembly lines such as worker assignment and balancing, are generally a labor-intensive type of production. Therefore, while balancing this kind of assembly lines, it should be done considering the labor costs. Thus, the proposed solutions will be able to produce more realistic results for real life environments. For this purpose, in this study, a mixed integer mathematical formulation is presented for ALWABP by considering the objective function of minimizing labor costs and cycle time. The efficiency of the proposed model and the improvements has been demonstrated by solving test instances in the literature.

Assembly line worker assignment and rebalancing problem: A mathematical model and an artificial bee colony algorithm

In real life, some situations such as demand fluctuations, changes in product structure, and workstation failures affect the existing balance of assembly lines, resulting in the need for these lines to rebalance. In the assembly line rebalancing problem (ALRBP), it is assumed that the processing time of a task does not depend on the worker performing it. Nevertheless, in practice, the time each worker requires to execute a task varies owing to several reasons, including the experience, skill, and disability of some individuals. In this study, the assembly line worker assignment and rebalancing problem (ALWARBP), which considers task times diversify in terms of workers, is introduced to fill this gap. The ALWARBP consists of the reassignment of tasks and workers to non-disrupted workstations after disruptions occur due to breakdowns or shutdowns of workstations to minimize variability in terms of cycle time and workstation assignments of tasks relative to the initial line balance. The aims of this paper are to i) describe the ALWARBP, ii) develop a mixed-integer linear programming (MILP) model, and iii) propose an artificial bee colony (ABC) algorithm to tackle the considered problem. The numerical experiments have been designed and conducted using 120 problem instances. The experimental results indicate that both methods managed to obtain optimal solutions in small-sized instances. In large-sized test instances, the proposed ABC algorithm showed higher performance in terms of solution value and computation time.

A dynamic job rotation scheduling conceptual framework by a human representing digital twin

This work is an extension part of the Assembly Line Worker Assignment Balancing Problem(ALWABP), aims to provide a dynamic solution for an assembly line fatigue worker job rotation by using machine learning based digital twin. This framework explains, fatigue worker identification and work rotation possibilities for a reconfigurable assembly line. The fatigue causing parameters are sensed from the workers and classified with a fatigue classifier then send the fatigue worker details to a worker job rotation search algorithm. The job rotation search algorithm provides a suggestion to the production supervisor for a best possible worker job rotation/reallocation solution dynamically.

The Fuzzy Assembly Line Worker Assignment and Balancing Problem

Consideration is given to the assembly line worker assignment and balancing problem (ALWABP) with uncertain task processing times and objective to simultaneously minimize the fuzzy cycle time and the fuzzy smoothness index of the line. After positioning the problem within the relevant research field, we present a genetic algorithm (GA) for its solution. Results over selected benchmarks test problems show the effectiveness of the proposed GA. The results obtained are further supported by a sensitivity analysis that shows the effect of the vagueness (uncertainty) of the task processing times on the uncertainty of the examined optimization criteria.

Robust optimization for U-shaped assembly line worker assignment and balancing problem with uncertain task times

Awareness of the importance of U-shaped assembly line balancing problems is all on the rise. In the U-shaped assembly line, balancing is affected by the uncertainty associated with the assembly task times. Therefore, it is crucial to develop an approach to respond to the uncertainty caused by the task times. When the great majority of existing literature related to uncertainty in the assembly line is considered, it is observed that the U-shaped assembly line balancing problem under uncertainty is scarcely investigated. That being the case, we aim to fill this research gap by proposing a robust counterpart formulation for the addressed problem. In this study, a robust optimization model is developed for the U-shaped assembly line worker assignment and balancing problem (UALWABP) to cope with the task time uncertainty characterized by a combined interval and polyhedral uncertainty set. A real case study is conducted through data from a company producing water meters.