A Cascading Auction Protocol as a Framework for Integrating Process Planning and Heterarchical Shop Floor Control

A new contract net-style auction protocol is proposed as a framework for integrating process planning and shop floor control in heterarchical manufacturing systems. Process planning is partitioned into on-line and off-line activities; off-line process planning decisions are represented in a graph format and used as input for on-line process planning activities performed by machine controllers. Triggered by the opening round of an auction, the final on-line stages of process planning are dovetailed with the resource allocation process in the shop floor control system. The auction process allows final process planning decisions to be based on timely information, relying on the distribution of static process planning information rather than the distribution of a model of dynamic shop floor status and allowing a controller to identify all the primary and secondary resources and operations that must be provided for the incremental processing of a part.     

A queueing model for a two-stage stochastic manufacturing system with overlapping operations

This paper presents analytical expressions for estimating average process batch flow times through a stochastic manufacturing system with overlapping operations. It is shown that the traditional queueing methodology cannot be directly applied to this setting, as the use of the overlapping operations principle causes the arrival process of sublots at the second stage to be a non-renewal process. An embedded queueing model is then proposed, which provides a tool to estimate the flow time reductions caused by the use of overlapping operations. Moreover, we provide expressions to estimate the production disruptions occurring at the second stage. The results of our research confirm the general intuition that the overlapping operations principle leads to less congestion, and hence a smoother flow of work through the system. On the other hand however, lot splitting inevitably requires more material handling on the shop floor. The expressions provided in this paper allow the quantification of the trade-off between these two effects, e.g., by gauging them within the scope of a cost model.     

Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy

This study presents the development of a multi-criteria control methodology for flexible manufacturing systems (FMSs). The control methodology is based on a two-tier decision making mechanism. The first tier is designed to select a dominant decision criterion and a relevant scheduling rule set using a rule-based algorithm. In the second tier, using a look-ahead multi-pass simulation, a scheduling rule that best advances the selected criterion is determined. The decision making mechanism was integrated with the shop floor control module that comprises a real-time simulation model at the top control level and RapidCIM methodology at the low equipment control level. A factorial experiment was designed to analyze and evaluate the two-tier decision making mechanism and the effects that the main design parameters have on the system’s performance. Next, the proposed control methodology was compared to a selected group of scheduling rules/policies using DEA. The results demonstrated the superiority of the suggested control methodology as well as its capacity to cope with a fast changing environment.     

Integrated Modeling Framework for Manufacturing Systems: A Unified Representation of the Physical Process and Information System

The task of process modeling in a manufacturing environment centers around controlling and improving the flow of materials. This flow comprises a complicated web of control and physical systems. Despite a variety of manufacturing system modeling approaches, more rigorous process modeling is required. This paper presents an integrated modeling framework for manufacturing systems (IMF-M). Conceptual modeling of physical materials flow supported by a graphical representation facilitates improvement of operations in manufacturing environments. A declarative and executable representation of control information systems helps to improve information management by managing a variety of information models with improved readability and reusability. A unified representation of the physical process and information system provides a common modeling milieu in which efforts can be coordinated among several groups working in the different domains of scheduling, shop floor and logistics control, and information system. Since the framework helps adapt to the changes of the physical process and information system affecting each other in a consistent manner, the modeling output enhances integration of the manufacturing system.     

Evaluating Virtual Cells and Multistage Flow Shops: An Analytical Approach

The implementation of cellular manufacturing can be carried out through the creation of manufacturing cells (i.e., groups of dissimilar machines dedicated to a set of part types that are placed in close proximity to one another) or virtual cells (i.e., the dedication of specific machines within the current departments to a prespecified set of part types). Typically, the former involves the reorganization of the shop floor and provides the operational benefit of reduced materials handling. On the other hand, the latter configuration is simpler to implement and easier to reconfigure in light of product demand changes, but it may not offer the same operational benefits. In this paper, we propose and validate analytical approximations for comparing the performance of virtual cells and multistage flow shops. Using these approximations and hypothetical data, we identify some key factors that influence the implementation of virtual cells in a multistage flow shop environment. We conclude with an application of our approximations to industrial data.     

Fed-batch bioreactor performance and cell line stability evaluation of the artificial chromosome expression technology expressing an IgG1 in Chinese hamster ovary cells

The artificial chromosome expression (ACE) technology system uses an engineered artificial chromosome containing multiple site-specific recombination acceptor sites for the rapid and efficient construction of stable cell lines. The construction of Chinese hamster ovary(CHO) cell lines expressing an IgG1 monoclonal antibody (MAb) using the ACE system has been previously described (Kennard et al., Biotechnol Bioeng. 2009;104:540-553). To further demonstrate the manufacturing feasibility of the ACE system, four CHO cell lines expressing the human IgG1 MAb 4A1 were evaluated in batch and fed-batch shake flasks and in a 2-L fed-batch bioreactor. The batch shake flasks achieved titers between 0.7 and 1.1 g/L, whereas the fed-batch shake flask process improved titers to 2.5–3.0 g/L. The lead 4A1 ACE cell line achieved titers of 4.0 g/L with an average specific productivity of 40 pg/(cell day) when cultured in a non optimized 2-L fed-batch bioreactor using a completely chemically defined process. Generational stability characterization of the lead 4A1-expressing cell line demonstrated that the cell line was stable for up to 75 days in culture. Product quality attributes of the 4A1 MAb produced by the ACE system during the stability evaluation period were unchanged and also comparable to existing expression technologies such as the CHO-dhfr system. The results of this evaluation demonstrate that a clonal, stable MAb-expressing CHO cell line can be produced using ACE technology that performs competitively using a chemically defined fed-batch bioreactor process with comparable product quality attributes to cell lines generated by existing technologies.     

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     

Object-Oriented Modeling for Flexible Manufacturing Systems

Object-oriented modeling provides a new way of thinking about flexible manufacturing systems, using models organized around real-world concepts. This paper describes how the object modeling technique can be used to develop integrated factory models that embrace factory process modeling as well as policy modeling. Such models can be used to assess how quickly a manufacturing organization can adjust its operations to meet changes in demands for products, consumer preferences, supplier quality, and lead times. These models also can be used as vehicles for studying the impact of introducing new product lines or new process technology without the disruption or expense of pilot projects or test setups.     

N‐linked glycosylation is an important parameter for optimal selection of cell lines producing biopharmaceutical human IgG

We studied the variations in N‐linked glycosylation of human IgG molecules derived from 105 different stable cell lines each expressing one of the six different antibodies. Antibody expression was based on glutamine synthetase selection technology in suspension growing CHO‐K1SV cells. The glycans detected on the Fc fragment were mainly of the core‐fucosylated complex type containing zero or one galactose and little to no sialic acid. The glycosylation was highly consistent for the same cell line when grown multiple times, indicating the robustness of the production and glycan analysis procedure. However, a twofold to threefold difference was observed in the level of galactosylation and/or non‐core‐fucosylation between the 105 different cell lines, suggesting clone‐to‐clone variation. These differences may change the Fc‐mediated effector functions by such antibodies. Large variation was also observed in the oligomannose‐5 glycan content, which, when present, may lead to undesired rapid clearance of the antibody in vivo. Statistically significant differences were noticed between the various glycan parameters for the six different antibodies, indicating that the variable domains and/or light chain isotype influence Fc glycosylation. The glycosylation altered when batch production in shaker was changed to fed‐batch production in bioreactor, but was consistent again when the process was scaled from 400 to 5,000 L. Taken together, the observed clone‐to‐clone glycosylation variation but batch‐to‐batch consistency provides a rationale for selection of optimal production cell lines for large‐scale manufacturing of biopharmaceutical human IgG. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.     

Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems

This paper presents a hierarchical approach to scheduling flexible manufacturing systems (FMSs) that pursues multiple performance objectives and considers the process flexibility of incorporating alternative process plans and resources for the required operations. The scheduling problem is solved at two levels: the shop level and the manufacturing system level. The shop level controller employs a combined priority index developed in this research to rank shop production orders in meeting multiple scheduling objectives. To overcome dimensional complexity and keep a low level of work-in-process inventory, the shop controller first selects up to three production orders with the highest ranking as candidates and generates all possible release sequences for them, with or without multitasking. These sequences are conveyed to the manufacturing system controller, who then performs detailed scheduling of the machines in the FMS using a fixed priority heuristic for routing parts of multiple types while considering alternative process plans and resources for the operations. The FMS controller provides feedback to the shop controller with a set of suggested detailed schedules and projected order completion times. On receiving these results, the shop controller further evaluates each candidate schedule using a multiple-objective function and selects the best schedule for execution. This allows multiple performance objectives of an FMS to be achieved by the integrated hierarchical scheduling approach.     

A Process Chaining Approach toward Product Design for Environment

This article presents an approach toward product design for environment (DfE) at the level that integrates environmental hazard analysis with models of transformation processes. As a complementary analysis tool to life-cycle assessment (LCA), this method would support detailed design decisions through modeling of a "process chain" for a subset of the product's life cycle. The building blocks for this approach are a set of unit process models that can convert process and design parameters into estimates for energy utilization, production scrap, and ancillary waste flows. These values for quantity of environmental releases can be integrated using a multicriiteria environmental hazard evaluation methodology that can estimate the "qualrty" of environmental releases. Finally, the waste information can be used to support a design model that can link design parameters to material, process, and operational parameter selection. A case study illustrating printed circuit board (PCB) assembly is presented to show process chain implementation in manufacturing applications.     

Manufacturing capacity planning and the value of multi-stage stochastic programming under Markovian demand

Capacity planning is a crucial part of global manufacturing strategies in the automotive industry, especially in the presence of volatile markets with high demand uncertainty. Capacity adjustments in machining intensive areas, e.g. body shop, paint shop, or aggregate machining face lead times exceeding a year, making an elaborated decision support indispensable. In this regard, two-stage stochastic programming is a frequently used framework to support capacity and flexibility decisions under uncertainty. However, it does not anticipate future capacity adjustment opportunities in response to market demand developments. Motivated by empirical findings from the automotive industry, we develop a multi-stage stochastic dynamic programming approach where the evolution of demand is represented by a Markov demand model. An efficient multi-stage solution algorithm is proposed and the benefits compared to a rolling horizon application of a two-stage approach are illustrated for different generic manufacturing networks. Especially network structures with limited flexibility might significantly benefit from applying a multi-stage framework.     

Current good manufacturing practice in plant automation of biological production processes

The production of biologicals is subject to strict governmental regulations. These are drawn up in current good manufacturing practices (cGMP), a.o. by the U.S. Food and Drug Administration. To implement cGMP in a production facility, plant automation becomes an essential tool. For this purpose Manufacturing Execution Systems (MES) have been developed that control all operations inside a production facility. The introduction of these recipe-driven control systems that follow ISA S88 standards for batch processes has made it possible to implement cGMP regulations in the control strategy of biological production processes. Next to this, an MES offers additional features such as stock management, planning and routing tools, process-dependent control, implementation of software sensors and predictive models, application of historical data and on-line statistical techniques for trend analysis and detection of instrumentation failures. This paper focuses on the development of new production strategies in which cGMP guidelines are an essential part.     

The Structure of Manufacturing Systems: Insights on the Impact of Variability

A classic issue in manufacturing strategy is positioning, that is, the appropriate structure for the manufacturing system. At its simplest, the choice is between a job shop and a flow line; but if the system has to produce a variety of different products,then it also is necessary to decide on focus, the degree of specialization. While effective use of resources is essential, it also is necessary to consider the ability of the system to cope with variability and disturbances. Using a variety of queueing models of manufacturing systems, it is possibleto get some useful insights into positioning and focus.     

Sustainability in Supply Chain Management: Aggregate Planning from Sustainability Perspective

Supply chain management that considers the flow of raw materials, products and information has become a focal issue in modern manufacturing and service systems. Supply chain management requires effective use of assets and information that has far reaching implications beyond satisfaction of customer demand, flow of goods, services or capital. Aggregate planning, a fundamental decision model in supply chain management, refers to the determination of production, inventory, capacity and labor usage levels in the medium term. Traditionally standard mathematical programming formulation is used to devise the aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard aggregate planning formulation to account for additional environmental and social criteria to incorporate triple bottom line consideration of sustainability. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in aggregate planning. We analyze the revised models and interpret the results on a case study from real life that would be insightful for decision makers.     

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.     

Stable Earliest Starting Schedules for Cyclic Job Shops: A Linear System Approach

We consider a cyclic job shop where an identical mixture of parts of different types, called a minimal part set (MPS), is produced repetitively in the same processing order. The precedence relationships among events (start of operation) are represented by a directed graph that has a recurrent structure. Each operation starts as soon as all its preceding operations are complete (called earliest starting). There is a class of desirable schedules that has the minimum cycle time and an identical schedule pattern for every MPS. By using linear system theory on minimax algebra, we characterize the set of all possible such schedules. We develop an efficient algorithm to find one among such schedules that minimizes a performance measure related to work-in-progress inventory. We also discuss an application to a flexible manufacturing system.     

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.