Inneke Van Nieuwenhuyse

Buffer sizing in multi-product multi-reactor batch processes: Impact of allocation and campaign sizing policies

This paper studies the impact of management policies, such as product allocation and campaign sizing, on the required size of the finished goods inventories in a multi-product multi-reactor batch process. Demand, setup and batch processing times for these products are assumed to be stochastic, and the inventory buffer for every product type needs to be such that target customer service levels are met. To perform this analysis, we develop a queueing model that allows us to explicitly estimate service levels as a function of the buffer size, and the allocation/campaign sizing policies. This model can be used to evaluate the service level given an existing buffer configuration, as well as to determine the buffer sizes required across products to meet a pre-specified service level. It also allows us to formulate a number of insights into how product allocation decisions and campaign planning policies affect buffer sizing decisions in symmetric production systems.

Analysis of gap times in a two-stage stochastic flowshop with overlapping operations

The impact of transfer batching (also referred to as lot splitting) on the performance of flowshops has received widespread attention in the literature. Most papers have emphasized the usefulness of lot splitting in cutting down average flow times, as it enables the overlapping of operations at different stages of the flowshop. However, while most analytical papers have studied deterministic flowshops, an important downside of lot splitting has been overlooked; i.e., the occurrence of idle time between the processing of consecutive sublots belonging to the same process batch (referred to as gap times). Gap times add no value to the product; they merely increase the process batch makespan at the different stages. In deterministic systems, these gap times may be avoided by balancing the processing rates of the different machines in the shop; in stochastic settings, however, they may occur even when the system is perfectly balanced, due to the inherent variability in the setup and processing times. Studying a two-stage flowshop with a single product type, this paper provides insight into the behavior of the gap times, and develops an approximation for the process batch makespan at the second stage in terms of the system characteristics and the lot splitting policy.