Tg Tugce Martagan

A simulation model of port operations during crisis conditions

We consider the supply chain for containerized items that arrive at a port in the U.S. whose final destination is also in the U.S. Ports are important entities in global supply chains. As such, when a port cannot operate because of a crisis, such as a natural or man-made disaster, it is critical that freight flow is not disrupted. We develop a simulation model that can be used to make effective re-routing decisions so that the time for freight to reach its final destination is not significantly increased in a crisis. The simulation model will evaluate and report the performance of the supply chain under different re-routing strategies. The output can be analyzed to find the best re-routing strategy that minimizes congestion and delays during crisis conditions. The model can also be used by various decision makers such as port managers, ocean carriers, or transportation companies for strategic decision making.

Optimal condition-based harvesting policies for biomanufacturing operations with failure risks

ABSTRACT The manufacture of biological products from live systems such as bacteria, mammalian, or insect cells is called biomanufacturing. The use of live cells introduces several operational challenges including batch-to-batch variability, parallel growth of both desired antibodies and unwanted toxic byproducts in the same batch, and random shocks leading to multiple competing failure processes. In this article, we develop a stochastic model that integrates the cell-level dynamics of biological processes with operational dynamics to identify optimal harvesting policies that balance the risks of batch failures and yield/quality tradeoffs in fermentation operations. We develop an infinite horizon, discrete-time Markov decision model to derive the structural properties of the optimal harvesting policies. We use IgG1 antibody production as an example to demonstrate the optimal harvesting policy and compare its performance against harvesting policies used in practice. We leverage insights from the optimal policy to propose smart stationary policies that are easier to implement in practice.

Stochastic simulation under input uncertainty for contract-manufacturer selection in pharmaceutical industry

We consider a pharmaceutical company that sources a biological product from a set of unreliable contract manufacturers. The likelihood of a manufacturer to successfully deliver the product is estimated via logistic regression as a function of the product attributes. The assignment of a product to the right contract manufacturers is of critical importance for the pharmaceutical company, and simulation-based optimization is used to identify the optimal sourcing decision. However, the input uncertainty due to the uncertain parameters of the logistic regression model often leads to poor sourcing decisions. We quantify the decrease in the expected profit due to input uncertainty as a function of the size of the historical data set, the level of dispersion in the historical data of a product attribute, and the number of products. We also introduce a sampling-based algorithm that reduces the expected decrease in the expected profit.

Game theoretic analysis of an inventory problem with substitution, stochastic demand, and uncertain supply

A game theoretic approach is used to analyse an inventory problem with two products, stochastic demand, and uncertain supply. The supply chain analysed includes two competing retailers selling two substitutable products and those retailer’ suppliers. Retailers face stochastic demand and replenish the inventory from the suppliers. However, both suppliers provide an indeterminate fraction of the quantity requested, due to randomness in capacity and quality. Some customers with unmet demand will substitute that product with one sold by the other retailer. We assume that the retailers are rational players with conflicting objectives. We model the retailers’ single period expected payoffs and identify the ordering decisions using Nash strategy. We prove the existence and uniqueness of the Nash solution, and provide results for numerical examples. We analyse the combined impact of product substitution and supply uncertainty on the retailers. Results suggest that supply uncertainties do not always hurt retailers’ expected payoffs.

Simulation-based production planning for engineer-to-order systems with random yield

We consider an engineer-to-order production system with unknown yield. We model the yield as a random variable which represents the percentage output obtained from one unit of production quantity. We develop a beta-regression model in which the mean value of the yield depends on the unique attributes of the engineer-to-order product. Assuming that the beta-regression parameters are unknown by the decision maker, we investigate the problem of identifying the optimal production quantity. Adopting a Bayesian approach for modeling the uncertainty in the beta-regression parameters, we use simulation to approximate the posterior distributions of these parameters. We further quantify the increase in the expected cost due to the so-called input uncertainty as a function of the size of the historical data set, the product attributes, and economic parameters. We also introduce a sampling-based algorithm that reduces the average increase in the expected cost due to input uncertainty.

Control and design of the fillet batching process in a poultry processing plant

In the poultry processing industry demand and supply are still growing in volume and diversity, which requires more processing capacity, flexibility and smarter control. This paper focuses on the fillet batching process. To minimize the giveaway of fixed-weight fillet batching the right choices on layout, buffer sizes, batch sizes and batch allocation policies are of great importance. We develop a simulation model to support such decisions on design and control. The model is used (i) to determine buffer and grader sizes, (ii) to optimize batch allocation in a dedicated layout, (iii) to compare a dedicated to a flexible layout and (iv) to assess the impact of smart allocation policies. In particular we find that significant reductions in giveaway can be achieved by employing so-called index policies in a flexible layout.

Performance Guarantees and Optimal Purification Decisions for Engineered Proteins

We investigate protein purification operations conducted by biomanufacturers and pharmaceutical companies as part of their research and development efforts. Purification of these proteins involves unique challenges such as balancing the yield and purity trade-offs, dealing with uncertainty in the starting material, and estimating the impact of several interlinked decisions. We develop a Markov decision model and partition the state space into decision zones that provide managerial insights to optimize purification operations. We develop practical guidelines to quantify financial risks, and we characterize the optimal operating decisions based on specific production requirements. The optimization framework has been implemented at Aldevron, a contract biomanufacturer specializing in proteins, and has resulted in 25% reduction in the total lead times and 20% reduction in the costs of protein purification operations on average.