Analysing and levelling manufacturing complexity in mixed-model assembly lines

In recent years, the automotive industry has witnessed a rapid increase in model variety and customization. New models, which are mainly being introduced in response to consumers demand, feature long lists of choices in terms of variants (engine model, comfort level, colour palette, etc.) and options (entertainment system, start/stop functionality, etc.). This high variability increases the complexity of factory processes and workstations and thus impacts directly upon the complexity of the manufacturing system as a whole. The shift from mass production to mass customized production is a trend that looks likely to continue in the foreseeable future, driven by automotive manufacturers' struggle to maintain market share in their traditional markets and seize market share in new, fast-growing markets. To cope with this intensified customization, automotive assembly platforms are designed to be capable of assembling a large range of relatively different models. That is they become mixed-model assembly lines. This implies that a high variety of tasks are to be performed at each workstation. As a consequence, the manufacturing complexity at these workstations increases. Mixed-model assembly lines are flow-line production systems that typically encounter the assembly line balancing problem (ALBP), a combinatorial optimization problem involving the optimal partitioning of assembly work among the workstations with a particular objective in mind. Subsequently, solving mixed-model assembly line balancing problems (MMALBPs) is much more complex than single-model cases, as workload must be smoothed for all workstations and all models in order to avoid overload or idle time. Despite the recent focus on manufacturing complexity and the extensive study of the ALBP, little research has explored how complexity can be applied to optimize line efficiency. Manufacturing complexity has been a key concern of many researchers and manufacturers in recent years, however, practical procedures to level complexity have not yet been considered and investigated when balancing the assembly lines. Analysing, measuring and monitoring complexity while creating line balancing solutions is a new and unexplored topic, especially when using real industry scenarios. In this dissertation, we propose an approach that can be used to monitor manufacturing complexity at each workstation while balancing the mixed-model assembly lines. The research carried out relies on an investigation of real MMAL's aiming to develop a deep analysis of complexity. The goal is to understand what and how complexity is generated, in order to cope and reduce the high complexity and its impacts in the line. During several visits and workshops carried out in collaboration with manufactures, we could observe that work load distribution is directly related with models variety, as tasks' time might differ from model to model. We first explored the existing scientific literature on the mixed-model assembly line balancing problem and manufacturing complexity in Chapter 2. Then, manufacturing complexity is investigated using two approaches: (1) an empirical analysis approach based on data collected in the Field and (2) a quantitative analysis approach measuring the level of uncertainty by means of entropy

Balancing and sequencing mixed-model assembly lines to minimize work overload in real world workstations

In mixed-model line balancing, we usually have overload after having optimally balanced the line. To further reduce this overload, sequencing of the models may be required. This paper presents two Mixed Integer Linear Program (MILP) models, where one balances the overload and the second sequences models to minimize the overload. Two heuristics are developed to solve some realistic cases. The solutions representation, algorithms and results are systematically described. The efficiency of both models and heuristics are examined by applying them to real world data

Workload balancing and manufacturing complexity levelling in mixed-model assembly lines

To effectively react and meet the current ever growing demand for individualised motor vehicles, built to customer specific requirements, automotive industry has accelerated its transition towards mass-customisation. As a result, the number of new model introductions has drastically increased over the past three decades. To cope with this intensified customisation, the current automotive assembly platforms are designed to assemble a wide range of relatively different models, and are turned into mixed-model assembly lines (MMALs). This implies that the set of tasks to be performed on each workstation is no longer stable but varies highly with the model-mix. As a consequence, the manufacturing complexity increases at the workstations and throughout the whole assembly system. This paper proposes a method to monitor manufacturing complexity at each workstation while the MMAL is being balanced. An entropy-based quantitative measure of complexity, which incorporates the variability of each task duration, is developed. This measure is used to monitor the manufacturing complexity level at each workstation. An integrated mixed-line balancing and complexity monitoring heuristic is proposed, to determine workload balance solutions, in which manufacturing complexity is levelled throughout the workstations composing the line. This procedure is tested on a real data-set provided by an automotive manufacturer. The results are reported and thoroughly discussed

Desambiguação do mapeamento automático de ontologias

O processo de emparelhamento (alinhamento de nível 0/1) automático é fundamental no processo de mapeamento (alinhamento de nível 2), mas de grande ambiguidade aquando da sua adaptação. Ora a aplicação do mapeamento na transformação de dados (migração de dados) é de grande exigência, pelo que as situações ambíguas devem ser detectadas e corrigidas. Sendo a desambiguação fundamental, ainda adquire mais relevância se o processo for sistemático e completo, pois permite e sua formalização e implementação, ao mesmo tempo que promove a sua aceitação. Este artigo descreve a análise e sistematização do processo de desambiguação do mapeamento automático de ontologias, e propõe a caracterização dos cenários segundo cinco dimensões. Como resultado, identificam-se as soluções por tipo e características dos cenários, e aponta-se o uso da metodologia a cenários mais complexos

Measuring the objective complexity of assembly workstations

The large number of product variants, produced to satisfy customers, increases significantly the complexity of manufacturing systems. As a consequence, new approaches to deal with production processes are required. Because of the impact of complexity on productivity, it is in the first place important to understand what complexity is and what are its main drivers. Thereto, this paper defines complexity in a production environment and it proposes an identifier for complex assembly workstations. Based on real data a model is suggested to characterize workstations complexity. The model is presented and its validity and accuracy are discussed

Balancing mixed-model assembly lines in real world complex workstations

In an attempt to react to the increasing customization, the same mixed-model assembly line is required to assemble a large variety of products. As a consequence, workstations become more complex due to the resulting variability of the assembly tasks. Thus, the need for new mixed-model assembly line balancing solutions, taking this complexity into account is raising. This paper discusses the issue of mixed-model line balancing of complex workstations using real data from some automotive assembly lines. Using an earlier developed complexity analysis model, the complexity of each workstation was evaluated. Then an optimization Mixed Integer Linear Program (MILP) model was developed and used to determine a line balance that optimizes overloads. The complexity results and the overload of each workstation are analysed to determine how these two models are related. The complexity model measures complexity of each workstation based on collected data and the MILP model determines an optimal balance. The complexity and overload results are then analysed and opposed to determine where improvements are required

Shorten new product development and introduction in bio(pharmaceutical) industries

Introduction Nowadays, the production of monoclonal antibody using animal cell culture (MAB) requires new improvements. The approach presented in this poster is based on simulation, scheduling optimization and de-bottlenecking of production processes. Mainly an abstraction of the P&I D (Process and instrumentation diagram) is done and the control software for a Biotech Plan is modeled. The modeling of the control software is based on ISA standards: ISA-88 and ISA-95. These standards are crucial for optimization and debottlenecking of processes. The simulation of a small scale production facilitates to recognize lowlights and highlights in the production , improving the results when it is applied to a full scale production

Measuring complexity in mixed-model assembly workstations

In an effort to maintain or increase their market share and at the same time prevent costs from escalating, manufacturing organisations are increasingly using their current manufacturing system to produce custom output. As a consequence, the large number of product variants increases significantly the complexity of manufacturing systems, both for the operators as for the support services. This is especially true in automotive industry, where customisation is increasing at a rapid pace. To counter the ensuing loss of productivity, a more fundamental approach to dealing with this complexity in manufacturing processes is required. In order to investigate the impact of complexity on production performance, one must first delineate the concept and then identify as unambiguously as possible highly complex workstations. This article defines complexity at the workstation level and proposes a complexity measure for mixed-model assembly workstations. Based on data from several leading automotive companies from Belgium and Sweden, some statistical models are proposed to characterise workstations complexity. The models are described and their validity and accuracy are discussed