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Balancing collaborative human–robot assembly lines to optimise cycle time and ergonomic risk
International Journal of Production Research
Abstract
Human–robot collaboration can enhance productivity of production lines and reduce human ergonomic risk. The numbers and types of robots and stations in which robots are allocated need to be determined. Operations should be scheduled carefully when a human and robot work on a part in a station to obtain a feasible operation allocation with the highest efficiency and lowest ergonomic risk. A mixed-integer linear programming model, constraint programming model, and Benders decomposition algorithm were developed to analyse advantages of collaborative robots in assembly lines. An energy expenditure method was used to evaluate ergonomic risk. By scheduling and balancing collaborative human–robot assembly lines, operational advantages and scheduling constraints from human–robot collaboration were studied when immobile and mobile robots are used. Regression lines were developed that can help managers determine how many and what types of robots are best for a line and what the impact of robot mobility on robot and line performance can be. The best configuration for equipping a line with collaborative robots is when (number of robots)/(number of stations) is near .7 and about 37% of robots are mobile. Robots can be efficiently used in lines with both a small and large number of passive resources and in simple and mixed-model lines.
... In this context, the ALBP considering HRC has received much attention in recent years. Since this research direction is still in the emerging stage, diverse problem definitions and nomenclature have been proposed by different studies, including Assembly Line Balancing Problem Considering Human-Robot Collaboration (ALBP-HRC) [13,14]; Semi-Automatic Assembly Line Balancing Problems (SAABLPs) [17,40]; Collaborative Robot Assembly Line Balancing Problem (CALB) [15,41,42]. All these reported works consider HRC task scheduling, and the different names correspond to different model assumptions (number of resources, types of cobots) and task allocation mechanisms (how to define collaborative assembly). ...
... In addition, there are studies that have considered two collaborative assembly modes simultaneously. The combination of simultaneous and supportive collaboration modes, explored in research by Chen, Sekiyama [48], Weckenborg, Kieckhäfer [13], Stecke and Mokhtarzadeh [41], ...
Developing manufacturing systems that consider workforce heterogeneity and inclusiveness is a significant emerging trend in human-centred production. In this context, collaborative robots (cobots), designed specifically to assist human operations, provide unparalleled support to assist a diverse range of semi-skilled workers. However, there remains a deficiency in effective production scheduling methods that can help industrial practitioners manage the increased complexity of heterogeneous human-robot collaborations. This study introduces the Assembly Line Worker Integration and Balancing Problem with Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC) model: an innovative method to optimise task allocation among a heterogeneous workforce, including semi-skilled workers and cobots, thereby improving productivity and cost efficiency. A key aspect of this approach is a multiskilled human-robot collaboration (mHRC) task allocation mechanism capable of selecting from five assembly/collaboration modes based on specific task requirements and skill availability, thus maximizing resource-skill complementarity. Moreover, an adaptive multiobjective cooperative co-evolutionary algorithm (a-MOCC) is introduced, featuring an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). We demonstrate the superior performance of the proposed method compared to three established metaheuristic algorithms in benchmark tests, underscoring its effectiveness in addressing the complexity of task allocation in heterogeneous environments. This work underscores the significant potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.
... The assessment of energy expenditure was initially introduced by [26], who presented an approach to assess the metabolic rate for manual labour and walking movements, which encompassed various human aspects, such as age, body weight, gender, height, load weight, and more. Evaluating energy expenditure is critical for assessing ergonomic risks [27,28] since it includes metrics such as the duration, level, and repetitiveness of physical tasks that indicate the stress caused by physical jobs [29]. ...
The shift from Industry 4.0 to Industry 5.0 represents a significant change in how technologies are approached in workplace design. Industry 4.0 was characterized by the automation of the production process, with a focus on maximizing output and efficiency. However, as Industry 5.0 becomes more relevant, the focus is moving toward the importance of putting people at the center of the production process. This means designing workspaces that prioritize human comfort and productivity and finding ways to integrate technology that supports and enhances human abilities. One of the key technologies that is helping to facilitate this transition is collaborative robots or cobots. By working alongside humans, cobots can help improve production efficiency while allowing for greater human involvement in the production process. However, to fully leverage the potential of cobots, it is essential to design workspaces that are optimized for human comfort and productivity. This requires taking into account the needs and preferences of both human and robotic resources and finding ways to allocate tasks in a way that maximizes efficiency while also taking into account human well-being. One promising approach to achieving this goal is the implementation of a dynamic multi-objective task allocation system, as presented in this work. This method uses physiological and performance data to evaluate the well-being of human operators and dynamically re-allocate tasks to ensure that operators are not overworked or fatigued. This is a significant step towards creating truly human-centered production environments that prioritize the well-being and productivity of human workers.
Assembly line balancing is assigning tasks to workstations in a production line to achieve optimal productivity. In recent years, the importance of energy studies in assembly line balancing has gained significant attention. Most existing publications focused on energy consumption in robotic assembly line balancing. This paper focuses on assembly line balancing with energy consumption in semi-automatic operation. The algorithm serves to improve the exploration to achieve a high-quality solution in a non-convex combinatorial problem, such as assembly line balancing with energy consumption. A novel approach called the Substituted Tiki-Taka Algorithm is introduced by incorporating a substitution mechanism to enhance exploration, thus improving the combinatorial optimization process. To evaluate the effectiveness of the Substituted Tiki-Taka Algorithm, a computational experiment is conducted using assembly line balancing with energy consumption benchmark problems. Additionally, an industrial case study is undertaken to validate the proposed model and algorithm. The results demonstrate that the Substituted Tiki-Taka Algorithm outperforms other existing algorithms in terms of line efficiency and energy consumption reduction. The findings from the case study indicate that implementing the Substituted Tiki-Taka Algorithm significantly increases line efficiency while simultaneously reducing energy consumption. These results highlight the potential of the proposed algorithm to positively impact manufacturing operations by achieving a balance between productivity and energy efficiency in assembly line systems.
Moving from Industry 4.0 to Industry 5.0 marks a substantial shift in the way technology is incorporated into workplace design. In Industry 4.0, the emphasis was on automating the production process to maximize efficiency and output. However, with Industry 5.0, the focus has shifted towards placing people at the heart of the production process. This involves creating workspaces that prioritize human comfort and productivity and integrating technology that enhances human abilities. Collaborative robots, or cobots, are a critical technology in this transition as they work alongside humans to improve efficiency while enabling greater human involvement. However, to make the most of cobots, it is necessary to design workspaces that optimize human comfort and productivity, by considering the needs and preferences of both human and robotic resources. A promising strategy for achieving this goal is the implementation of a dynamic multi-objective task allocation system. The method proposed in this work uses physiological and performance data to assess the well-being of human operators and reallocates tasks dynamically to avoid overworking or fatiguing them. This represents a significant step towards creating production environments that prioritize the well-being and productivity of human workers.
In this work, we consider a mixed-model two-sided assembly line balancing problem for a given cycle time (MTALBP-I), in which the primary objective is to minimise the number of mated-stations (i.e. the length of the two-sided assembly line), while the number of stations, which describes the total number of operators, is also concerned. To solve this problem, we propose a combinatorial Benders decomposition-based exact algorithm, and develop a sequence-based enumerative search method to calculate effective combinatorial Benders cuts. To evaluate the performance of our proposed solution approach, we conduct extensive computational experiments on a set of benchmark instances, and the results demonstrate its efficiency of finding exact solutions even for large-sized instances.
The research on the robotic assembly line balancing problem (RALBP) was originated for the first time nearly three decades ago. This problem is under the umbrella of the assembly line balancing problem in which robots and automated equipment are employed to take on human workers’ roles to form a flexible assembly line. In this review paper, the development and generalisation throughout the time of the RALBP are addressed. To make the review easy to comprehend and effective, the RALBP is first classified based on the types of layouts and then further dividing up according to the 4 M (Man, Machine, Material and Method) concept. The main contributions of different articles are chronologically summarised in the form of a table. Besides, the research contribution precedence diagram is used to illustrate the sequential order and linkage relationship among researches. Finally, from the findings of the review, future research directions are pinpointed and discussed.
Human-Robot Collaboration (HRC) is a form of direct interaction between humans and robots. The objective of this type of interaction is to perform a task by combining the skills of both humans and robots. HRC is characterized by several aspects, related both to robots and humans. Many works have focused on the study of specific aspects related to HRC, e.g., safety, task organization. However, a major issue is to find a general framework to evaluate the collaboration between humans and robots considering all the aspects of the interaction. The goals of this paper are the following: (i) highlighting the different latent dimensions that characterize the HRC problem and (ii) constructing a conceptual framework to evaluate and compare different HRC configuration profiles. The description of the methodology is supported by some practical examples.
A manufacturing system able to perform a high variety of tasks requires different types of resources. Fully automated systems using robots possess high speed, accuracy, tirelessness, and force, but they are expensive. On the other hand, human workers are intelligent, creative, flexible, and able to work with different tools in different situations. A combination of these resources forms a human-machine/robot (hybrid) system, where humans and robots perform a variety of tasks (manual, automated, and hybrid tasks) in a shared workspace. Contrarily to the existing surveys, this study is dedicated to operations management problems (focusing on the applications and features) for human and machine/robot collaborative systems in manufacturing. This research is divided into two types of interactions between human and automated components in manufacturing and assembly systems: dual resource constrained (DRC) and human-robot collaboration (HRC) optimization problems. Moreover, different characteristics of the workforce and machines/robots such as heterogeneity, homogeneity, ergonomics, and flexibility are introduced. Finally, this paper identifies the optimization challenges and problems for hybrid systems. The existing literature on HRC focuses mainly on the robotic point of view and not on the operations management and optimization aspects. Therefore, the future research directions include the design of models and methods to optimize HRC systems in terms of ergonomics, safety, and throughput. In addition, studying flexibility and reconfigurability in hybrid systems is one of the main research avenues for future research.
The quality of the balancing of mixed-model assembly lines is intimately related to the defined production sequence. The two problems are, however, incompatible in time, as balancing takes place when planning the line, while sequencing is an operational problem closely related to market demand fluctuations. In this paper, an exact procedure to solve the integrated balancing and sequencing problem with stochastic demand is presented. The searched balancing solution must be flexible enough to cope with different demand scenarios. A paced assembly line is considered and utility work is used as a recourse for station border violations. A Benders’ decomposition algorithm is developed along with valid inequalities and preprocessing as a solution procedure. Three datasets are proposed and used to test algorithm performance and the value of treating uncertainty in mixed-model assembly lines. The integration of the strategic balancing problem with the operational sequencing problem results in more robust assembly lines.
Effective and safe human-robot collaboration in assembly requires accurate prediction of human motion trajectory, given a sequence of past observations such that a robot can proactively provide assistance to improve operation efficiency while avoiding collision. This paper presents a deep learning-based method to parse visual observations of human actions in an assembly setting, and forecast the human operator's future motion trajectory for online robot action planning and execution. The method is built upon a recurrent neural network (RNN) that can learn the time-dependent mechanisms underlying the human motions. The effectiveness of the developed method is demonstrated for an engine assembly.
With human-robot collaborations, companies are struggling to decide how to schedule tasks in an effective way. We propose an approach to find an eligible division of tasks that forgoes expert knowledge and simulations. Based on a recreation of the application using predefined basic processes and knowledge of the process constraints, the human and robot capabilities are examined. In addition, a time assumption inspired by the Methods-Time Measurement is carried out, to determine the subprocess times. Subsequently, a genetic algorithm assigns the tasks to the human and robot. Expert evaluation and real implementation prove the effectiveness of the proposed approach.
The advancement of technology and the empowerment of the industry have made humans and robots more closely tied together, known as human-robot collaboration. A sector that specifically utilises this collaboration is the printed circuit boards industry. Therefore in this industry, proper allocation of tasks to humans and robots is crucial. This study investigates this type of allocation to minimise makespan. A Constraint Programming based (CP) approach is developed to solve the problem as the main novelty of this study. A single board problem, as the basic model, is developed by adding more assumptions including different groups of tasks, no-wait scheduling of tasks, multi-agent planning, and multiple boards sequencing. Then, different experimental instances are generated and solved to analyse the performance of CP and the sensitivity of idle time and makespan to key parameters of the problem. The superiority of the computational results of CP over mathematical programming is evident.
Notwithstanding its disruptive potential, which has been the object of considerable debate, Industry4.0 (I4.0) operationalisation still needs significant study. Specifically, scheduling is a key process that should be explored from this perspective. The purpose of this study is to shed light on the issues regarding scheduling that need to be considered in the new I4.0 framework. To achieve this, a two-stage cascade literature review is performed. The review begins with an analysis regarding the opportunities and challenges brought by I4.0 to the scheduling field, outputting a set of critical scheduling areas (CSA) in which development is essential. The second-stage literature review is performed to understand which steps have been taken so far by previous research in the scheduling field to address those challenges. Thus, a first contribution of this work is to provide insight on the influence and expected changes brought by I4.0 to scheduling, while showcasing relevant research. Another contribution is to identify the most promising future lines of research in this field, in which relevant challenges such as holistic scheduling, or increased flexibility requirements are highlighted. Concurrently, CSA such as decentralised decision-making, and human–robot collaboration display large gaps between current practice and the required technological level of development.
Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which includes both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.