Flexibility-Driven Planning Of Flow-Based Mixed-Model Assembly Structures

Trends such as mass customization, changing customer preferences and resulting output fluctuations increasingly challenge the production industry. Mixed-model assembly lines are affected by the rising product variety, which ultimately leads to ascending cycle time spreads and efficiency losses. Matrix assembly addresses these challenges by decoupling workstations and dissolving cycle time constraints while maintaining the flow. Both matrix and line assembly are flow-based assembly structures characterized by assembly objects moving along the stations. In assembly system planning, competing assembly structures are developed and the one best meeting the use case's requirements is selected for realization. During assessing requirements and selecting the superior assembly structure, the systematic consideration of flexibility is often not ensured within the planning approach. Therefore, a preferred assembly structure may not have the flexibility required for a use case. The systematic and data-driven assessment of required and provided flexibility in assembly system planning is necessary. This paper presents an assessment model that matches a use case's requirements with the flexibility of flow-based assembly structures based on production program and process data. On the one hand, requirements are defined by flexibility criteria that evaluate representative product mixes and process time heterogeneity. On the other hand, provided flexibility of flow-based assembly structures is assessed in a level-based classification. A method for comparing the requirements and the classification's levels to prioritize assembly structures for application in a case is developed. The flexibility requirements and assembly structure of an exemplary use case are determined and discussed under the planning project's insights to evaluate the developed model. This work contributes to the objective and data-driven selection of assembly structures by utilizing use case-specific data available during the phase of structural planning to meet flexibility requirements and ensure their consideration along the assembly planning process

Path Prediction For Efficient Order Release In Matrix-Structured Assembly Systems

Numerous research papers have already demonstrated the theoretical benefits of matrix-structured assembly systems. Nevertheless, such assembly systems have hardly been used in practice so far. The main reason for this, apart from the technical integration, is the complexity of controlling matrix-structured assembly systems. In theory, decentralized, agent-based control architectures have proven to be particularly suitable. However, order release has been largely neglected so far. Accordingly, the authors' previous work includes a conceptual approach for capacity-oriented order release in matrix-structured assembly systems. This previous approach calculates possible paths of an order and their capacity requirements considering both routing and sequence flexibility. Furthermore, by combining the possible paths of released orders with orders to be released and comparing them with the available capacity, the previously suggested approach can systematically carry out capacity-oriented release decisions. However, the NP-hard (NP: non-deterministic polynomial-time) problem arising from the consideration of all possible paths has a negative impact on the scalability and real-time capability of order release. Therefore, the present paper aims to extend the previously developed approach. By determining the most likely paths that a given order will take through the assembly system, the combination possibilities are limited in such a way that the total amount of calculations required to find a suitable order for release is reduced. Doing so, the NP-hardness of the previously developed approach can be circumvented. This work contributes to the practical realization and economic operation of matrix-structured assembly systems. The paper describes the logic of path prediction in detail and evaluates its impact on order release