Optimal planning and campaign scheduling of biopharmaceutical processes using a continuous-time formulation

This work addresses the optimal planning and campaign scheduling of biopharmaceutical manufacturing processes, considering multiple operational characteristics, such as the campaign schedule of batch and/or continuous process steps, multiple intermediate deliveries, sequence dependent changeovers operations, product storage restricted to shelf-life limitations, and the track-control of the production/campaign lots due to regulatory policies. A new mixed integer linear programing (MILP) model, based on a Resource Task Network (RTN) continuous time single-grid formulation, is developed to comprise the integration of all these features. The performance of the model features is discussed with the resolution of a set of industrial problems with different data sets and process layouts, demonstrating the wide application of the proposed formulation. It is also performed a comparison with a related literature model, showing the advantages of the continuous-time approach and the generality of our model for the optimal production management of biopharmaceutical processes

Production planning of biopharmaceutical manufacture.

Multiproduct manufacturing facilities running on a campaign basis are increasingly becoming the norm for biopharmaceuticals, owing to high risks of clinical failure, regulatory pressures and the increasing number of therapeutics in clinical evaluation. The need for such flexible plants and cost-effective manufacture pose significant challenges for planning and scheduling, which are compounded by long production lead times, intermediate product stability issues and the high cost - low volume nature of biopharmaceutical manufacture. Scheduling and planning decisions are often made in the presence of variable product titres, campaign durations, contamination rates and product demands. Hence this thesis applies mathematical programming techniques to the planning of biopharmaceutical manufacture in order to identify more optimal production plans under different manufacturing scenarios. A deterministic mixed integer linear programming (MILP) medium term planning model which explicitly accounts for upstream and downstream processing is presented. A multiscenario MILP model for the medium term planning of biopharmaceutical manufacture under uncertainty is presented and solved using an iterative solution procedure. An alternative stochastic formulation for the medium term planning of biomanufacture under uncertainty based on the principles of chance constrained programming is also presented. To help manage the risks of long term capacity planning in the biopharmaceutical industry, a goal programming extension is presented which accounts for multiple objectives including cost, risk and customer service level satisfaction. The model is applied to long term capacity analysis of a mix of contractors and owned biopharmaceutical manufacturing facilities. In the final sections of this thesis an example of a commercial application of this work is presented, followed by a discussion on related validation issues in the biopharmaceutical industry. The work in this thesis highlighted the benefits of applying mathematical programming techniques for production planning of biopharmaceutical manufacturing facilities, so as to enhance the biopharmaceutical industry's strategic and operational decision-making towards achieving more cost-effective manufacture

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation

Meta-heuristic & hyper-heuristic scheduling tools for biopharmaceutical production

The manufacturing of biopharmaceuticals requires substantial investments and necessitates long-term planning. Complicating the task of determining optimal production plans are large portfolios of products and facilities which limit the tractability of exact solution methods, and uncertainties & stochastic events which often render plans obsolete when reality deviates from the expectation. This thesis therefore describes decisional tools that are able to cope with these complexities. First, a capacity planning problem for a network of facilities and multiple products was tackled. Inspired by meta-heuristic approaches to job shop scheduling, a tailored construction heuristic that builds a production plan based on a sequence — optimised by a genetic algorithm—of product demands was proposed. Comparisons to a mathematical programming model demonstrated its competitiveness on certain scenarios and its applicability to a multi-objective problem. Next, a custom object-oriented model was introduced for a manufacturing scheduling system that utilised a failure-prone perfusion-based bioprocess. With this, process design decisions such as cell culture run time and process configuration, and single-product facility scheduling strategies were evaluated whilst incorporating simulations of stochastic failure events and uncertain demand. This model was then incorporated into a larger hyper-heuristic to determine optimal scheduling policies for a multi-product problem. Various policy representations are tested and a few policies are adapted from the literature to fit this specific problem. In addition, a novel policy utilising a look-ahead heuristic is proposed. The benefit of parameter tuning using evolutionary algorithms is demonstrated and shows that tuned policies perform much better than a policy that estimates parameters based on service level considerations. In addition, the disadvantages of relying on a fixed or rigid production sequence policy in the face of uncertainty is highlighted

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Stochastic Optimization of Bioreactor Control Policies Using a Markov Decision Process Model

Biopharmaceuticals are the fastest-growing segment of the pharmaceutical industry. Their manufacture is complicated by the uncertainty exhibited therein. Scholars have studied the planning and operation of such production systems under some uncertainties, but the simultaneous consideration of fermentation and resin yield uncertainty is lacking so-far. To study the optimal operation of biopharmaceutical production and purification systems under these uncertainties, a stochastic, dynamic approach is necessary. This thesis provides such a model by extending an existing discrete state-space, infinite horizon Markov decision process model of upstream fermentation. Tissue Plasminogen Activator fermentation and chromatography was implemented. This example was used to discuss the optimal policy for operating different fermentation setups. The average per-cycle operating profit of a serial setup was 1,272 $; the parallel setup produced negative average rewards. Managerial insights were derived from a comparison to a basic, titer maximizing policy and process sensitivities. In conclusion, the integrated stochastic optimization of biopharma production and purification control aids decision making. However, the model assumptions pose room for further studies. Keywords: Markov decision process; biopharmaceuticals production; fermentation uncertainty; chromatography resin; stochastic performance decay

Strategic Biopharmaceutical Production Planning for Batch and Perfusion Processes

Capacity planning for multiple biopharmaceutical therapeutics across a large network of manufacturing facilities, including contract manufacturers, is a complex task. Production planning is further complicated by portfolios of products requiring different modes of manufacture: batch and continuous. Capacity planning decisions each have their own costs and risks which must be carefully considered when determining manufacturing schedules. Hence, this work describes a framework which can assimilate various input data and provide intelligent capacity planning solutions. First of all, a mathematical model was created with the objective of minimising total cost. Various challenges surrounding the biomanufacturing of both perfusion and fed-batch products were solved. Sequence-dependent changeover times and full decoupling between upstream and downstream production suites were incorporated into the mixed integer linear program, which was used on an industrial case study to determine optimal manufacturing schedules and capital expenditure requirements. The effect of varying demands and fermentation titres was investigated via scenario analysis. To improve computational performance of the model, a rolling time horizon was introduced, and was shown to not only improve performance but also solution quality. The performance of the model was then improved via appropriate reformulations which consider the state task network (STN) topology of the problem domain. Two industrial case studies were used to demonstrate the merits of using the new formulation, and results showed that the STN improved performance in all test cases, and even performed better than the rolling time horizon approach from the previous model in one test case. Various strategic options regarding capacity expansion were analysed, in addition to an illustration of how the framework could be used to de-bottleneck existing capacity issues. Finally, a multi-objective component is added to the model, enabling the consideration of strategic multi-criteria decision making. The ε-constraint method was shown to be the superior multi-objective technique, and was used to demonstrate how uncertain input parameters could affect the different objectives and capacity plans in question

Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture

The previous research work in the literature for capacity planning and scheduling of biopharmaceutical manufacture focused mostly on the use of mixed integer linear programming (MILP). This paper presents fast genetic algorithm (GA) approaches for solving discrete-time MILP problems of capacity planning and scheduling in the biopharmaceutical industry. The proposed approach is validated on two case studies from the literature and compared with MILP models. In case study 1, a medium-term capacity planning problem of a single-site, multi-suite, multi-product biopharmaceutical manufacture is presented. The GA is shown to achieve the global optimum on average 3.6 times faster than a MILP model. In case study 2, a larger long-term planning problem of multi-site, multi-product bio-manufacture is solved. Using the rolling horizon strategy, the GA is demonstrated to achieve near-optimal solutions (1% away from the global optimum) as fast as a MILP model

A dynamic simulation framework for biopharmaceutical capacity management

In biopharmaceutical manufacturing there have been significant increases in drug complexity, risk of clinical failure, regulatory pressures and demand. Compounded with the rise in competition and pressures of maintaining high profit margins this means that manufacturers have to produce more efficient and lower capital intensive processes. More are opting to use simulation tools to perform such revisions and to experiment with various process alternatives, activities which would be time consuming and expensive to carry out within the real system. A review of existing models created for different biopharmaceutical activities using the Extend® (ImagineThat!, CA) platform led to the development of a standard framework to guide the design and construct of a more efficient model. The premise of the framework was that any ‘good’ model should meet five requirement specifications: 1) Intuitive to the user, 2) Short Run-Time, 3) Short Development Time, 4) Relevant and has Ease of Data Input/Output, and 5) Maximised Reusability and Sustainability. Three different case studies were used to test the framework, two biotechnology manufacturing and one fill/finish, with each adding a new layer of understanding and depth to the standard due to the challenges faced. These Included procedures and constraints related to complex resource allocation, multi-product scheduling and complex ‘lookahead’ logic for scheduling activities such as buffer makeup and difficulties surrounding data availability. Subsequently, in order to review the relevance of the models, various analyses were carried out including schedule optimisation, debottlenecking and Monte Carlo simulations, using various data representation tools to deterministically and stochastically answer the different questions within each case study scope. The work in this thesis demonstrated the benefits of using the developed standard as an aid to building decision-making tools for biopharmaceutical manufacturing capacity management, so as to increase the quality and efficiency of decision making to produce less capital intensive processes