Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this paper, we propose a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers. Upon observing the limitation of the IP, a Constraint Programming (CP) model is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models, including sensitivity analysis to measure the computational cost of enabling the different constraints. To the best of our knowledge, we are the first paper to incorporate the different realistic mixed model assembly line constraints and develop a CP model based on the scheduling module of the IBM ILOG Optimizations Studio. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems.

中文翻译：

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带有并行工作站，分区约束和人体工程学的混合模型线平衡

装配线是具有成本效益的生产系统，可大量生产相同的产品。由于客户需求，制造商使用混合模型装配线来生产不同的定制产品。为了保持效率，需要优化生产线的管理决策，例如工人数量和将装配任务分配给工作站，以提高生产量并降低成本。在每个工作站中，要完成的工作都取决于确切的产品配置，并且在所有产品中都不一致。在本文中，我们提出了一个混合模型行平衡整数程序（IP），该程序考虑了并行工作程序，分区，任务分配和人机工程学方面的限制，目的是最大程度地减少工作程序数。遵守IP的限制后，开发了约束编程（CP）模型来解决较大的装配线平衡问题。来自汽车OEM的数据用于评估MIP和CP模型的性能，包括敏感性分析以衡量启用不同约束的计算成本。据我们所知，我们是第一篇结合不同现实的混合模型装配线约束并基于IBM ILOG Optimizations Studio的调度模块开发CP模型的论文。使用OEM数据，我们发现CP模型在较大问题上胜过IP模型。我们是第一篇结合了不同的现实混合模型装配线约束并基于IBM ILOG Optimizations Studio的调度模块开发CP模型的论文。使用OEM数据，我们发现CP模型在较大问题上胜过IP模型。我们是第一篇结合了不同的现实混合模型装配线约束并基于IBM ILOG Optimizations Studio的调度模块开发CP模型的论文。使用OEM数据，我们发现CP模型在较大问题上胜过IP模型。