Changeability and flexibility of assembly line balancing as a multi-objective optimization problem
Introduction
The current environment of industrial production is characterized by a variety of megatrends, including an increasing customer demand for individual products [1]. A frequently used strategy to respond to this market demand is to increase the variety of products and variants [2]. As a result, the pressure for new and further product developments is intensifying, resulting in shorter product life cycles [3]. Moreover, this is reinforced by regulations, such as subsidies or emission thresholds for the automotive industry. The enlarged product portfolio and increased dynamic in customer demand lead to market conditions which can be considered challenging for industrial companies [4]. However, in order to adapt a company’s production program, often times corresponding changes in the production system are required. These changes may include production equipment, logistics or work organization and result in corresponding costs, which are also subject to the frequency of change. In order to predict fluctuations in demand different models are available [5,6]. Since the future is unknowable, a certain level of uncertainty remains [7]. An inherent characteristic of uncertainty is that it leads to risk [8], in this case the financial risk to over or under adapt a production system in order to meet the customer demand. To mitigate that risk, a production system can be designed in a way that options to easily change the production system are integrated. Since changes require effort in equipment, manpower, knowledge and time [9], changes need to be kept at a reasonable limit.
Automotive assembly typically is organized in the form of a variant flow assembly line [10], where changes represent a significant challenge. In this organizational form, assembly tasks are carried out at interlinked stations within a given cycle time [11]. The planning process for assigning assembly tasks to assembly stations is referred to as assembly line balancing [12]. The consideration of various product variants is mostly realized by the use of an average variant mix [12]. Changes in this variant mix can hardly be reacted to once the assembly line balancing has been determined [13].
The line balancing of an operating variant flow assembly line is to be designed under the premise of providing high efficiency and therefore economic advantages [14]. However, capacity reserves, such as additional assembly stations or extended cycle times for preventive addressing of demand fluctuations, reduce the economic efficiency of the assembly line [15]. This leads to the challenge of designing the assembly line balancing to be highly efficient, but also maintaining adequate capacity reserves in order to adapt the assembly line balancing to changed requirements such as fluctuations in the variant mix. The ability to adapt to changed requirements is described by various terms in the literature such as changeability [9] or reconfigurability [16], whereas the term flexibility focusses on maintaining the ability to production under fluctuating influences [17]. The novel approach addresses the presented line balancing challenge of variant flow assembly lines by introducing a model for integrated optimization of costs, changeability and flexibility regarding scenarios of the future variant mix. The characteristic of flexibility hereby describes the design of an assembly line balancing, which is able to uphold production within a certain level of requirement fluctuation without the need of adjustment. Furthermore, the changeability of a line balancing configuration relates to the organizational and physical reallocation of assembly tasks including assembly equipment. The remainder of the paper is organized as follows. In Section 2, a literature review is presented. Section 3 is devoted to the mixed integer linear program applied to obtain changeable and flexible line balancing configurations. Additionally the evaluation of flexibility and changeability is presented. In Section 4, the computational results are presented. Finally these results are discussed in Section 5.
Section snippets
Literature review
Assembly line balancing is of high practical relevance in industrial production. Since the mathematical formulation of the line balancing problem by Salveson [18] various research activities have been carried out in this field. The key task of assembly line balancing is to allocate specific assembly tasks and their required resources to a station of the assembly line, typically with the aim of minimizing cycle time or the number of assembly stations. This basic problem is referred to as the
Multi-objective optimization problem
In this section the description of the multi-objective optimization model for the design of a changeable and flexible assembly line balancing with regard to different variant mix scenarios is given. The objective functions are individually presented and subsequently combined in an objective function vector supplemented by problem specific constraints. The problem to be solved by optimization is the identification of a greenfield assembly line balancing for a given variant mix. This scenario
Use case
The assembly line balancing is to be planned for a compact class car with three different drive concepts based on real automotive industry data, collected in the research project “Energieeffiziente und flexibel industriell herstellbare Elektrofahrzeugantriebe (16EMO0064 K)”. The drive concepts contain an internal combustion engine (ICE), battery electric vehicle (BEV) and a bivalent natural gas engine (bNGV). The different drive concepts are realized by a conversion design and accordingly
Conclusion
The presented novel approach allows the design of an assembly line balancing considering future scenarios as well as the preference of a decision maker in the objective conflict of costs, flexibility and changeability. The approach can also be used to evaluate existing assembly line balancing configurations regarding these objectives. This requires a manual formulation of the data basis and its integration into the optimization model by defining the assembly line balancing for scenario .
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors would like to thank the Federal Ministry of Education and Research for the kind support and funding within the project “Energieeffiziente und flexibel industriell herstellbare Elektrofahrzeugantriebe (16EMO0064K)”.
Johannes Fisel holds a M.Sc. degree in Industrial Engineering and Management from KIT. Currently, he is a research associate at the wbk Institute of Production Science at KIT focusing on flexibility and changeability in production system planning.
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Johannes Fisel holds a M.Sc. degree in Industrial Engineering and Management from KIT. Currently, he is a research associate at the wbk Institute of Production Science at KIT focusing on flexibility and changeability in production system planning.
Yannick Exner received his B.Sc. in Industrial Engineering and Management from KIT and is currently engaged in his M.Sc. degree. He worked as a student research assistant at the wbk Institute of Production Science where he focused on adaptive production systems and agile factory planning.
Dr.-Ing. Nicole Stricker is chief engineer of the group “Production Systems” at the wbk Institute of Production Science at KIT. Focus topics of her research activities are robust production systems and machine learning.
Prof. Dr.-Ing. Gisela Lanza holds the Professorship “Production System and Quality Management” at KIT and is head of the wbk Institute of Production Science. Her division mainly deals with the areas of global production strategies, production system planning, and quality management in research, industrial application, and teaching.