Medium term planning of biopharmaceutical manufacture with uncertain fermentation titers

The growing trend of employing multiproduct manufacturing facilities along with the randomness inherent in the biopharmaceutical manufacturing environment is creating significant scheduling and planning challenges for the biopharmaceutical industry. This work focuses on capturing the effect of uncertainty in fermentation titers when optimizing the planning of biopharmaceutical manufacturing campaigns. A mixed integer linear programming (MILP) model based on previous work is derived via chance constrained programming (CCP). The methodology is applied to two illustrative examples, and the results are compared with those from the deterministic model and a multiscenario model accompanied by an iterative construction algorithm. The computational results indicate that the proposed methodology offers significant improvements in solution quality over the compared approaches and presents an opportunity for biopharmaceutical manufacturers to make better medium term planning decisions, particularly under uncertain manufacturing conditions.     

Multiobjective long-term planning of biopharmaceutical manufacturing facilities

Biopharmaceutical companies with large portfolios of clinical and commercial products typically need to allocate production across several multiproduct facilities, including third party contract manufacturers. This poses several capacity planning challenges which are further complicated by the need to satisfy different stakeholders often with conflicting objectives. This work addresses the question of how a biopharmaceutical manufacturer can make better long-term capacity planning decisions given multiple strategic criteria such as cost, risk, customer service level, and capacity utilization targets. A long-term planning model that allows for multiple facilities and accounts for multiple objectives via goal programming is developed. An industrial case study based on a large scale biopharmaceutical manufacturer is used to illustrate the functionality of the model. A single objective model is used to identify how best to use existing capacity so as to maximize profits for different demand scenarios. Mitigating risk due to unforeseen circumstances by including a dual facility constraint is shown to be a reasonable strategy at base case demand levels but unacceptable if demands are 150% higher than expected. The capacity analysis identifies where existing capacity fails to meet demands given the constraints. A multiobjective model is used to demonstrate how key performance measures change given different decision making policies where different weights are assigned to cost, customer service level, and utilization targets. The analysis demonstrates that a high profit can still be achieved while meeting key targets more closely. The sensitivity of the optimal solution to different limits on the targets is illustrated.     

Capacity planning for batch and perfusion bioprocesses across multiple biopharmaceutical facilities

Production planning for biopharmaceutical portfolios becomes more complex when products switch between fed‐batch and continuous perfusion culture processes. This article describes the development of a discrete‐time mixed integer linear programming (MILP) model to optimize capacity plans for multiple biopharmaceutical products, with either batch or perfusion bioprocesses, across multiple facilities to meet quarterly demands. The model comprised specific features to account for products with fed‐batch or perfusion culture processes such as sequence‐dependent changeover times, continuous culture constraints, and decoupled upstream and downstream operations that permit independent scheduling of each. Strategic inventory levels were accounted for by applying cost penalties when they were not met. A rolling time horizon methodology was utilized in conjunction with the MILP model and was shown to obtain solutions with greater optimality in less computational time than the full‐scale model. The model was applied to an industrial case study to illustrate how the framework aids decisions regarding outsourcing capacity to third party manufacturers or building new facilities. The impact of variations on key parameters such as demand or titres on the optimal production plans and costs was captured. The analysis identified the critical ratio of in‐house to contract manufacturing organization (CMO) manufacturing costs that led the optimization results to favor building a future facility over using a CMO. The tool predicted that if titres were higher than expected then the optimal solution would allocate more production to in‐house facilities, where manufacturing costs were lower. Utilization graphs indicated when capacity expansion should be considered. © 2013 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 30:594–606, 2014     

Near optimal manufacturing flow controller design

Flow control of flexible manufacturing systems (FMSs) addresses an important real-time scheduling requirement of modern manufacturing facilities, which are prone to failures and other controllable or stochastic discrete events affecting production capacity, such as change of setup and maintenance scheduling. Flow controllers are useful both in the coordination of interconnected flexible manufacturing cells through distributed scheduling policies and in the hierarchical decomposition of the planning and scheduling problem of complex manufacturing systems. Optimal flow-control policies are hedging-point policies characterized by a generally intractable system of stochastic partial differential equations. This article proposes a near optimal controller whose design is computationally feasible for realistic-size systems. The design exploits a decomposition of the multiple-part-type problem to many analytically tractable one-part-type problems. The decomposition is achieved by replacing the polyhedra production capacity sets with inscribed hypercubes. Stationary marginal densities of state variables are computed iteratively for successive trial controller designs until the best inscribed hypercubes and the associated optimal hedging points are determined. Computational results are presented for an illustrative example of a failureprone FMS.     

Trends in capacity utilization for therapeutic monoclonal antibody production

The administration of high doses of therapeutic antibodies requires large-scale, efficient, cost effective manufacturing processes. An understanding of how the industry is using its available production capacity is important for production planning, and facility expansion analysis. Inaccurate production planning for therapeutic antibodies can have serious financial ramifications. In the recent 5th Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and Production, 434 qualified respondents from 39 countries were asked to indicate, among other manufacturing issues, their current trends and future predictions with respect to the production capacity utilization of monoclonal antibodies in mammalian cell culture systems. While overall production of monoclonals has expanded dramatically since 2003, the average capacity utilization for mammalian cell culture systems, has decreased each year since 2003. Biomanufacturers aggressively attempt to avoid unanticipated high production demands that can create a capacity crunch. We summarize trends associated with capacity utilization and capacity constraints which indicate that biopharmaceutical manufacturers are doing a better job planning for capacity. The results have been a smoothing of capacity use shifts and an improved ability to forecast capacity and outsourcing needs. Despite these data, today, the instability and financial constraints caused by the current global economic crisis are likely to create unforeseen shifts in our capacity utilization and capacity expansion trends. These shifts will need to be measured in subsequent studies.     

Flexible versus dedicated technology: A capacity expansion model

Over the past two decades, flexible manufacturing systems have been adopted in a variety of industries. Shorter product lives have necessitated acquiring flexible facilities. Further, the economies of scope that can be derived from producing products with different seasonality have made flexible facilities attractive. At the same time, dedicated facilities (such as transfer lines) have not disappeared because they offer economies of scale beneficial in high volume manufacturing environments. Hence, when producing a wide variety of products with different seasonality and demand growth rates, firms need to consider both economies of scale and economies of scope in deciding whether to acquire capacity of flexible or dedicated technology or both. In this paper, we consider the problem of making capacity acquisition decisions, when there is a choice of flexible and dedicated technologies available in a deterministic multi-product manufacturing environment with demand assumed to be nondeclining over time. We present a novel formulation of the problem and show the equivalence of the formulation to the uncapacitated plant location formulation. This enables us to use a very efficient solution procedure (Erlenkotter 1978) to solve even large problems optimally. We then present the results of an extensive computational study performed to evaluate the impact of key problem parameters on the proportion of flexible and dedicated capacity acquired. An interesting result of the study is that the proportion of flexible capacity acquired decreases with increasing scale economies. We also find that investment in flexible technology is significant even when it is relatively more expensive than dedicated technology.     

Susceptibility of mouse minute virus to inactivation by heat in two cell culture media types

Viral contaminations of biopharmaceutical manufacturing cell culture facilities are a significant threat and one for which having a risk mitigation strategy is highly desirable. High temperature, short time (HTST) mammalian cell media treatment may potentially safeguard manufacturing facilities from such contaminations. HTST is thought to inactivate virions by denaturing proteins of the viral capsid, and there is evidence that HTST provides ample virucidal efficacy against nonenveloped or naked viruses such as mouse minute virus (MMV), a parvovirus. The aim of the studies presented herein was to further delineate the susceptibility of MMV, known to have contaminated mammalian cell manufacturing facilities, to heat by exposing virus‐spiked cell culture media to a broad range of temperatures and for various times of exposure. The results of these studies show that HTST is capable of inactivating MMV by three orders of magnitude or more. Thus, we believe that HTST is a useful technology for the purposes of providing a barrier to adventitious contamination of mammalian cell culture processes in the biopharmaceutical industry. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Agent-based supply-chain planning in the forest products industry

The new economic challenges and recent trends in globalization have made it very difficult for Canadian forest product companies to improve their financial position without the coordinated involvement of the entire company, including their supply chains (distributed facilities, company offices, industrial customers, and distributors). Such a new level of efficiency involves their distributed facilities and offices spread around the world, and their customers. One consequence of this new reality is that forest products companies are now facing the need to re-engineer their organizational processes and business practices with their partners. To do this they must adopt new technologies to support the coordination of their planning and control efforts in a customer-centered environment. This paper first proposes a generic software architecture for development of an experimentation environment to design and test distributed advanced planning and scheduling systems. This architecture enables combination of agent-based technology and operations research-based tools in order to first take advantage of the ability of agent technology to integrate distributed decision problems, and, second, to take advantage of the ability of operations research to develop and exploit specific normative decision models. Next, this paper describes how this architecture has been configured into an advanced planning and scheduling tool for the lumber industry. Finally, we present how an application of this advanced planning tool is currently being validated and tested in a real manufacturing setting.     

Production planning of a flexible manufacturing system in a material requirements planning environment

Early flexible manufacturing system (FMS) production planning models exhibited a variety of planning objectives; typically, these objectives were independent of the overall production environment. More recently, some researchers have proposed hierarchical production planning and scheduling models for FMS. In this article, we examine production planning of FMS in a material requirements planning (MRP) environment. We propose a hierarchical structure that integrates FMS production planning into a closed-loop MRP system. This structure gives rise to the FMS/MRP rough-cut capacity planning (FMRCP) problem, the FMS/MRP grouping and loading (FMGL) problem, and the FMS/MRP detailed scheduling problem. We examine the FMRCP and FMGL problems in detail and present mathematical programming models for each of these problems. In particular, the FMRCP problem is modeled as a generalized assignment problem (GAP), and a GAP-based heuristic procedure is defined for the problem. We define a two-phase heuristic for the FMGL problem and present computational experience with both heuristics. The FMRCP heuristic is shown to solve problems that exhibit a dependent-demand relation within the FMS and with FMS capacity utilization as high as 99 percent. The FMGL heuristic requires very little CPU time and obtains solutions to the test problems that are on average within 1.5 percent of a theoretical lower bound. This FMS/MRP production planning framework, together with the resulting models, constitutes an important step in the integration of FMS technology with MRP production planning. The hierarchical planning mechanism directly provides for system-level MRP planning priorities to induce appropriate production planning and control objectives on the FMS while simultaneously allowing for necessary feedback from the FMS. Moreover, by demonstrating the tractability of the FMRCP and FMGL problems, this research establishes the necessary groundwork upon which to explore systemwide issues pertaining to the coordination of the hierarchical structure.     

Trends in capacity utilization for therapeutic monoclonal antibody production

The administration of high doses of therapeutic antibodies requires large-scale, efficient, cost effective manufacturing processes. An understanding of how the industry is using its available production capacity is important for production planning, and facility expansion analysis. Inaccurate production planning for therapeutic antibodies can have serious financial ramifications. In the recent 5th Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and Production, 434 qualified respondents from 39 countries were asked to indicate, among other manufacturing issues, their current trends and future predictions with respect to the production capacity utilization of monoclonal antibodies in mammalian cell culture systems. While overall production of monoclonals has expanded dramatically since 2003, the average capacity utilization for mammalian cell culture systems, has decreased each year since 2003. Biomanufacturers aggressively attempt to avoid unanticipated high production demands that can create a capacity crunch. We summarize trends associated with capacity utilization and capacity constraints which indicate that biopharmaceutical manufacturers are doing a better job planning for capacity. The results have been a smoothing of capacity use shifts and an improved ability to forecast capacity and outsourcing needs. Despite these data, today, the instability and financial constraints caused by the current global economic crisis are likely to create unforeseen shifts in our capacity utilization and capacity expansion trends. These shifts will need to be measured in subsequent studies.Key words: capacity, production, monoclonal antibody, survey, biopharmaceutical, manufacturing, constraints, facilityBuilding new capacity and improving existing systems to meet the demand for new monoclonal antibody (mAb) therapeutics, whether through in-house manufacturing or out-sourced contract manufacturing, has long-term cost implications for biotechnology firms. Bringing new capacity on line requires accurate market knowledge, lead-time, large capital expenditures and careful planning, and understanding trends in capacity utilization for the manufacture of mAbs can be critical to the planning process.For the first three quarters of the twentieth century, the traditional and most efficient way of producing antibodies was to immunize a large vertebrate, bleed the animal and, from the serum, collect the polyclonal protein. Because of their hardy binding specificity, the value of immunoglobulins, especially as a potential therapeutic tool, was evident from the time of their discovery because scientists envisioned them as probes and transporters of therapeutic tools. Limitations of using polyclonals were also evident from early on. Therapeutic antibodies needed to be delivered in very high concentration, and polyclonals, a heterogeneous group of molecules, directed against many different epitopes of an antigenic source, could only be extracted from serum in tiny quantities.A solution to at least some of the problems seemed to appear in 1984 when Kohler and Milstein¹ described a novel method for producing antibodies from an immortalized cell line capable of continually producing a virtually unlimited amount of a single antibody, directed at a single epitope. The medical and scientific communities realized that this hybridoma technology could produce sufficient quantities of antibodies for therapy, and in 1986, the first mAb for human use (Orthoclone OKT3—Ortho Pharmaceuticals) was approved for the prevention of kidney transplant rejection.As hybridoma technology evolved, it was clear that there were obstacles to overcome. The first mAbs were murine, but therapeutic candidates needed to be less immunogenic in order to avoid transplantation incompatibility. So chimeric antibodies and humanized mAbs (part murine, part human), and fully human mab production technologies were developed. The OKT3 approval was followed by a wave of mostly anti-cancer mAbs through the 1990''s, and since then, these proteins have become a dominant component of the biopharmaceutical market, representing approximately 20% of all biologic products, with combined revenues of over $20 billion in 2006.²Administration of high doses of therapeutic antibodies requires large-scale, efficient, cost effective manufacturing processes. Over the past few years, improvements have been made in cell line generation, expression vectors, transfection technology and large-scale cell culture production, allowing biotech firms to successfully move candidates through the pipeline. Today''s technologies are enabling five times the concentration of antibody produced by technologies just 5 years ago.³ Biotechnology drugs, including mAbs, now make up more than one-quarter of the FDA filings for approval, and over 40% of preclinical trials are now large molecule candidates, and as a result, planning for biologics manufacturing will continue to require strategic approaches to avoid potentially disruptive production bottlenecks. The long lead-time required to successfully launch a mAb requires pre-planning for capacity. This planning demands a new level of partnership between manufacturers and suppliers to develop novel technologies that will keep pace with industry''s need for capacity.     

Efficient lipid delivery to hybridoma culture by use of cyclodextrin in a novel granulated dry-form medium technology

Lipids are critical nutrients for high density eukaryotic cell cultivation, but inclusion of lipid components into dry-form media has been technically challenging. Lipid supplements are usually supplied for separate addition after powder reconstitution and filtration, which increases manipulation and risk in a biopharmaceutical manufacturing facility. Advanced Granulation Technology (AGT) is a novel dry-form media format designed for large-scale biopharmaceutical manufacture. All components of a complex formulation are homogeneously incorporated into a single granulated medium that simply requires water addition to yield a complete reconstituted 1X medium. We investigated whether cyclodextrin technology could be combined with the AGT process to deliver usable lipid in a dry medium format. The test lipids were cholesterol and several fatty acids supplied either as an aseptic supplement to liquid media or as part of a complete AGT formulation. The test system utilized a cholesterol auxotroph, NS0, and a protein-free basal formulation (CD Hybridoma Medium), with or without supplemental lipid. Post-filtration recovery of cholesterol from AGT-processed materials compared favorably with media supplemented with liquid lipid concentrates. Cell growth and viability and expression of recombinant protein were equivalent in all test media.     

A pattern-directed approach to flexible manufacturing: A framework for intelligent scheduling,learning, and control

The planning, scheduling, and control of manufacturing systems can all be viewed as problem-solving activities. In flexible manufacturing systems (FMSs), the computer program carrying out these problem-solving activities must additionally be able to handle the shorter lead time, the flexibility of job routing, the multiprocessing environment, the dynamic changing states, and the versatility of machines. This article presents an artificial intelligence (AI) method to perform manufacturing problem solving. Since the method is driven by manufacturing scenarios represented by symbolic patterns, it is referred to as pattern-directed. The method is based on three AI techniques. The first is the pattern-directed inference technique to capture the dynamic nature of FMSs. The second is the nonlinear planning technique to construct schedules and assign resources. The third is the inductive learning method to generate the pattern-directed heuristics. This article focuses on solving the FMS scheduling problem. In addition, this article reports the computation results to evaluate the utility of various heuristic functions, to identify important design parameters, and to analyze the resulting computational performance in using the pattern-directed approach for manufacturing problem-solving tasks such as scheduling.     

Rule-based production planning in flexible manufacturing systems

Production planning in flexible manufacturing may require the solution of a large-scale discrete-event dynamic stochastic optimization problem, due to the complexity of the system to be optimized, and to the occurrence of discrete events (new orders and hard failures). The production planning problem is here approached for a multistage multipart-type manufacturing shop, where each work cell can share its processing time among the different types of parts. The solution of this problem is obtained by an open-loop-feedback control strategy, updated each time a new event occurs. At each event time, two coupled problems are solved: 1) a product-order scheduling problem, conditioned on estimated values of the production capacities of all component work cells; and 2) a production-capacity planning problem, conditioned on predefined sequences of the product orders to be processed. In particular, the article aims at defining a production planning procedure that integrates both analytical tools, derived from mathematical programming, and knowledge-based rules, coming from experience. The objective is to formulate a hybrid (knowledge-based/analytical) planning architecture, and to analyze its use for multicell multipart-type manufacturing systems.     

Clustering algorithms to optimize the tool handling system management in an FMS

Tool management is recognized as a critical issue in flexible manufacturing facilities management. This article addresses the issue of tool management in a flexible system installed in an avionics components factory. The system is composed of two machining centers equipped with local tool magazines of limited capacity. A tool handling system is in charge of tool movements between the tool room and the two machines. Each machine is able to perform any operation, provided that it is equipped with the suitable tool. In this kind of installation, tool allocation must be determined, and tool movements must be synchronized in order to minimize operating costs, or, equivalently, maximize the productivity of the system. We propose an approach to production planning based on a clustering algorithm, which takes into account the tool requirements of each part program in the production batch. We also propose two different heuristics for the scheduling problem. A case study was conducted on the facility mentioned above. Two conflicting objectives can be identified for this kind of production system: the reduction of tools to be shared among machines and the reduction of workload unbalance. The tests and comparison made demonstrate how the proposed procedure leads to superior results in terms of both objectives.     

Job scheduling and management of wearing tools with stochastic tool lifetimes

The problem of scheduling jobs using wearing tools is studied. Tool wearing is assumed to be stochastic and the jobs are processed in one machining centre provided with a limited capacity tool magazine. The aim is to minimize the expected average completion time of the jobs by choosing their processing order and tool management decisions wisely. All jobs are available at the beginning of the planning period. This kind of situation is met in production planning of CNC-machines. Previous studies concerning this problem have either assumed deterministic wearing for the tools or omitted the wearing completely. In our formulation of the problem, tool wearing is stochastic and the problem becomes very hard to solve analytically. A heuristic based on genetic algorithms is therefore given for the joint problem of job scheduling and tool management. The algorithm searches the most beneficial job sequence when the tool management decisions are made by a removal rule taking into account the future planned usage of the tools. The cost of each job sequence is evaluated by simulating the job processing. Empirical tests with heuristics indicate that by taking the stochastic information into account, one can reduce the average job processing time considerably.     

转基因植物表达药用蛋白的研究进展

基因工程技术的进步使得转基因植物广泛应用于工业、农业各个领域,尤其在医药制造领域。研究成果表明,转基因植物作为生物反应器在制备药用蛋白,如重组疫苗、重组动物抗体、细胞因子等方面较其他表达系统,如微生物及动物表达系统具有成本低、应用安全等优势,但在工业化技术方面仍存在障碍。     

An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system. The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence. A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

Current trends in biopharmaceuticals production in Escherichia coli

Biotechnology Letters - Since the manufacture of the first biotech product for a fledgling biopharmaceutical industry in 1982, Escherichia coli, has played an important role in the industrial...     

Integrated production and distribution planning for perishable food products

Certain types of food, such as catering foods, decay very rapidly. This paper investigates how the quality of such foods can be improved by shortening the time interval between production and delivery. To this end, we develop an approach that integrates short-term production and distribution planning in an iterative scheme. Further, an aggregation scheme is developed as the interface between the production scheduling and distribution problem. The production scheduling problem is solved through an MILP modeling approach which is based on a block planning formulation. Our implementation shows promising results, elaborated in a numerical investigation, which recollects the real settings of a catering company located in Denmark.     

Validation of biopharmaceutical purification processes for virus clearance evaluation

Any biopharmaceutical product that has involved the use of animal-derived material during the manufacturing process has the potential to be contaminated with animal viruses. To ensure safety of these products, extensive testing is performed on the starting materials, such as the cell banks, and on the raw materials used in manufacture. Additional testing is also performed at various stages of production and, in some cases, on the final product as well. Because of inherent limitations in direct testing methods, the capacity of the downstream purification process to remove/inactivate potential viral contaminants is also studied to give an extra degree of assurance that the final product will be free of infectious viruses.