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

\u3cp\u3eWe 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.\u3c/p\u3

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

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

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

Risk assessment in pharmaceutical supply chains under unknown input-model parameters

\u3cp\u3eWe consider a pharmaceutical supply chain where the manufacturer sources a customized product with unique attributes from a set of unreliable suppliers. We model the likelihood of a supplier to successfully deliver the product via Bayesian logistic regression and use simulation to obtain the posterior distribution of the unknown parameters of this model. We study the role of so-called input-model uncertainty in estimating the likelihood of the supply failure, which is the probability that none of the suppliers in a given supplier portfolio can successfully deliver the product. We investigate how the input-model uncertainty changes with respect to the characteristics of the historical data on the past realizations of the supplier performances and the product attributes.\u3c/p\u3

Managing trade-offs in protein manufacturing:how much to waste?

\u3cp\u3eWe consider the challenges and trade-offs involved in the manufacturing of engineered proteins. Manufacturing these proteins involves high risk of financial losses due to the purity and yield trade-offs, uncertainty in the process outcomes, and high operating costs. In this setting, the biomanufacturer must determine how much protein to manufacture in the upstream fermentation operations, and then how much of it to waste in each subsequent purification operation because of the purity–yield trade-offs. We develop a Markov decision model to optimize three layers of interdependent decisions in protein manufacturing: the optimal amount of protein to be produced in upstream operations, the optimal choice of chromatography technique to be used in downstream operations, and the optimal choice of pooling windows during chromatography. The proposed stochastic model dynamically optimizes these three layers of interdependent decisions to maximize the expected profit. The structural analysis derives functional relationships between the purity–yield trade-offs and operating costs, and characterizes the optimal operating policies. The optimal policy also suggests when the biomanufacturer is better off failing early and cutting losses. We use a state aggregation scheme to reduce the computational efforts, and quantify the savings obtained from the use of the optimization model in industry practice at Aldevron.\u3c/p\u3

Simulation-based performance evaluation of a manufacturing facility with vertical as/rs

Klein Mechanisch Werkplaats Eindhoven (KMWE) is a precision manufacturing company situated in the Netherlands and recently relocated to a new location known as the ‘Brainport Industries Campus’ (BIC). This move allowed KMWE to improve the performance of its manufacturing facility known as the ‘Tool\u3cbr/\u3eService Center’ (TSC) by investing in vertical automated storage and retrieval systems (AS/RSs). However, these decisions needed to be made under input uncertainties since the move to BIC and modernization of existing equipment would cause changes in operating parameters inside the facility, over which little information was known in advance. In this study, we show how hybrid simulation modelling was used to assess the impact of input uncertainties (such as operator productivity, vertical storage height) on the throughput performance of TSC. Ultimately, the outcomes of this research project were used by KMWE to make an investment decision on new equipment acquisition quantity

Optimal Production Decisions in Biopharmaceutical Fill-and-Finish Operations

Fill-and-finish is among the most commonly outsourced operations in biopharmaceutical manufacturing and involves several challenges. For example, fill-operations have a random production yield, as biopharmaceutical drugs might lose their quality or stability during these operations. In addition, biopharmaceuticals are fragile molecules that need specialized equipment with limited capacity, and the associated production quantities are often strictly regulated. The non-stationary nature of the biopharmaceutical demand and limitations in forecasts add another layer of challenge in production planning. Furthermore, most companies tend to ‘freeze’ their production decisions for a limited period of time, in which they do not react to changes in the manufacturing system. Using such freeze periods helps to improve stability in planning but comes at a price of reduced flexibility. To address these challenges, we develop a finite-horizon, discounted-cost Markov decision model, and optimize the production decisions in biopharmaceutical fill-and-finish operations. We characterize the structural properties of optimal cost and policies, and propose a new, zone-based decision-making approach for these operations. More specifically, we show that the state space can be partitioned into decision zones that provide guidelines for optimal production policies. We illustrate the use of the model with an industry case study

Performance guarantees and optimal purification decisions for engineered proteins

\u3cp\u3eWe 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.\u3c/p\u3

Aldevron accelerates growth using operations research in biomanufacturing

In the biomanufacturing industry, production and planning decisions are often challenging because of batch-to-batch variability and uncertainty in the production yield, quality, cost, and lead times. To improve biomanufacturing efficiency, a multidisciplinary team of researchers collaborated over five years to develop a portfolio of decision support tools. The tools developed provide a data-driven, operations research–based approach to reduce biomanufacturing costs and lead times. These decision support tools comprise multiple deterministic and stochastic optimization models to optimize production and planning decisions. To optimize production decisions related to fermentation and protein purification, optimization tools were developed to provide a decision support mechanism that links the underlying biological and chemical processes with business risks and financial trade-offs. To optimize planning decisions, interactive scheduling and capacity planning tools were developed to enable efficient use of expensive and limited resources. Although developed in collaboration with Aldevron, these tools address common industry challenges, and they have been shared with a wider industry community through working group sessions