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A sequential production line is defined as a set of sequential operations within a factory or distribution center whereby entities undergo one or more processes to produce a final product. Sequential production lines may gain efficiencies such as increased throughput or reduced work in progress by utilizing specific configurations while maintaining the chronological order of operations. One problem identified by the authors via a case study is that, some of the configurations, such as work cell or U-shaped production lines that have groups of buffers, often increase the space utilization. Therefore, many facilities do not take advantage of the configuration efficiencies that a work cell or U-shaped production line provide. To solve this problem, the authors introduce the concept of a buffer cluster. The production line implemented with one or more buffer clusters maintains the throughput of the line, identical to that with dedicated buffers, but with the clusters reduces the buffer storage space. The paper derives a time based parametric model that determines the sizing of the buffer cluster, provides a reduced time space for which to search for the buffer cluster sizing, and determines an optimal buffer clustering policy that can be applied to any N-server, N+1-buffer sequential line configuration with deterministic processing time. This solution reduces the buffer storage space utilized while ensuring no overflows or underflows occur in the buffer. Furthermore, the paper demonstrates how the buffer clustering policy serves as an input into a facility layout tool that provides the optimal production line layout.

A large scale discrete event model of a distribution center is presented where critical parameters identified were utilized to create a real-time scheduling control policy to improve throughput and redistribute work in process (WIP), reducing bottleneck process and overall WIP.In implementing the control policy, it was found that the data was queried multiple times at the same time stamp and continuously throughout the process during a time when the control policy specification would not be met. The amount of data collection, processing and storage can become expensive especially when implementing multiple control policies that are dependent on several critical parameters. There is a need for smart data collection, processing and storage policies for cost effectiveness and to manage existing power management and battery life issues that may occur with continuous data streaming with existing wireless devices and sensors. This paper presents both the real-time scheduling control policy and the data collection policy for a distribution center that must be considered simultaneously in order to realize the maximum benefits of a wireless real-time scheduling system.

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