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, and ergonomics 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.

This paper provides a literature review and an analysis of the studies related to workforce reconfiguration strategies as a part of workforce planning for various production environments.The survey demonstrates that these strategies play a crucial role in the resilience and flexibility of manufacturing systems since they help industrial companies to quickly adapt to frequent changes in demand both in terms of volume and product mix. Five strategies are considered: the use of utility, temporary, walking, cross-trained workers, and bucket brigades. They are analyzed in the context of mixed and multi-model manual assembly lines, dedicated, cellular, flexible, and reconfigurable manufacturing systems. The review shows that most of the researches on these reconfiguration strategies focus on multi-or mixed-model assembly lines. At the same time, few studies consider workers team reconfiguration in flexible and reconfigurable manufacturing systems. Finally, this paper reveals several promising research directions in workforce reconfiguration planning, namely, the use of both machine and workforce reconfigurations, consideration of the ergonomic aspects, the combination of multiple workforce reconfiguration strategies, the study of workforce reconfiguration in human-robot collaborative systems, and the use of new technologies in human-machine industrial environments.

No abstract

Reconfigurable manufacturing systems (RMS) are designed to be able to be reconfigured to produce new items. Nevertheless, reconfigurations of a RMS may be time consuming and costly if they are not considered since early steps of new item design. This work describes a decision support system to automatically generate and test configurations for such RMSs based on a computeraided design (CAD) model of a new product. The proposed methodology consists of two main steps. First, a matrix of possible assembly plans (taking into account resource/tool compatibility, geometric constraints, …) is generated with a skillbased comparison between the new item and the production resources. Second, the assembly plan with minimum reconfiguration cost is found through mathematical optimization. The solution is analyzed by a simulation model in the end. Experiments performed on small use case validate the proposed methodology.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

No abstract

No abstract

Frequent changes in customer needs and large product variety are forcing manufacturing companies to move from mass production to mass customization. Customized production can be achieved by introducing reconfigurable production systems (RMS). The customized flexibility and several characteristics of RMSs provide many opportunities in terms of process and production planning. However, those characteristics greatly increase the complexity of the design and planning of production systems. This paper presents a decision support system relying on a skill-based approach to design a reconfigurable assembly line considering the planning of assembly processes and monitoring. The proposed decision aid system is modular in design and is composed of four modules. The main input data is a CAD model of a new product variant for the identification of the assembly and monitoring requirements. Besides, a current assembly system layout with its resource descriptions exists. In the first developed module, assembly-by-disassembly and a skill-based approach are used to generate different assembly plans. In the second module, feature recognition and skill-based approaches generate process monitoring alternatives. The third module uses a linear program (LP) that aims to minimize the total cost of workstation activation and reconfiguration, as well as cycle time, and to maximize the process quality of the assembly tasks. A user-based generative model design approach is applied to optimize the values of three objective functions. In the fourth and final module, a simulation of the optimized assembly plan allows either the validation of the assembly plan and process monitoring plan or initiates a new iteration due to their infeasibility. To further demonstrate how the proposed methodology works, some computational experiments are provided for two use cases.