Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

Abstract

Increasing production variability while maintaining operation efficiency remains a critical issue in many manufacturing industries. While the adoption of mixed-model assembly lines enables the production of high product variety, it also makes the system more complex as variety increases. This paper proposes an information entropy-based methodology that quantifies and then minimizes the complexity through product sequencing. The theory feasibility is demonstrated in a series of simulations to showcase the impact of sequencing in controlling the system predictability and complexity. Hence, the framework not only serves as a tool to quantitatively assess the impact of complexity on total system performance but also provides means and insights into how complexity can be mitigated without affecting the overall manufacturing workload.

Product Interface Design for Complexity Management in Assembly Systems

“…Firstly, the interface design approach enables to manage complexity during the system-level design phase early in the development process. The DFA approach considering task difficulties [10], [32]- [34], on the other hand, can manage complexity at the detail design, and the OCC-based approach [19]- [22] focuses on the process design phase which can only control the layout or sequence of an assembly system. Other DFA approaches regarding granularity levels [36]- [38] or production time [40] deal with the system-level design phase, but decision levels of those research focus on a product architecture, modularization or a component design rather than an interface structure.…”

“…Wang et al [21] extended Wang and Hu's study [19] to general assembly lines with non-identical parallel stations by formulating a non-linear programming problem in order to identify an optimal system configuration. In recent studies on the OCC, Busogi et al [22] proposed a sequence-based optimization model for the reduction of complexity.…”

“…Interface design approach OCC-based approach [19]- [22] DFA approach (task difficulties) [10], [32]- [34] DFA approach (granularity levels) [36]- [38] DFA approach (production time) […”

Empirical comparison of assembly complexity assessment approaches in manufacturing

“…Modrak and Soltysova [42] introduced a novel operational complexity measure based on information entropy and used it to optimize production layout. Similarly, Busogi et al [43], in order to minimize the variety of product sequencing at an assembly station, measured manufacturing complexity using the probability of getting a variant demand.…”

Material Consumption Smoothing for Mixed-model Assembly Lines Using an Improved Target Tracking Method

“…However, unreasonable scheduling often leads to longer production cycle time and lower production line balance levels [1]. In order to solve the problem, several mathematical algorithms are utilized to quicken setup and smooth flow [2]: the genetic particle swarm algorithm [3] is put into practice to optimize the production line balance. An improved optimization method mixing genetic algorithm and simulated annealing algorithm is developed to improve the unbalanced load and decrease production cycle time [4], and a multi-objective mixed integer planning model has been established to minimize cycle time and maximize load balancing for the second type of equilibrium problem of mixed-model production lines [5].…”