The vaccines industry has not changed appreciably in decades regarding technology, and has struggled to remain viable, with large companies withdrawing from production. Meanwhile, there has been no ...let-up in outbreaks of viral disease, at a time when the biopharmaceuticals industry is discussing downsizing. The distributed manufacturing model aligns well with this, and the advent of synthetic biology promises much in terms of vaccine design. Biofoundries separate design from manufacturing, a hallmark of modern engineering. Once designed in a biofoundry, digital code can be transferred to a small-scale manufacturing facility close to the point of care, rather than physically transferring cold-chain-dependent vaccine. Thus, biofoundries and distributed manufacturing have the potential to open up a new era of biomanufacturing, one based on digital biology and information systems. This seems a better model for tackling future outbreaks and pandemics.
Biofoundries incorporate high levels of automation that implement complex workflows in order to increase the reliability and reproducibility of biotechnology.Biofoundries in different locations can communicate via digital data to complete design/build/test/learn operations.Distributed manufacturing attempts to bring small-scale manufacturing to many locations, directly in contradiction to the centralised mass production paradigm.Biofoundries are suited to the design of certain types of vaccines that do not require whole cells.The design and prototyping of vaccines in biofoundries with final manufacture in small-scale facilities offers the possibility to bring manufacturing close to the point of care.The marriage of biofoundries and the distributed manufacturing model offers solutions to the troubled vaccines manufacturing industry.
•This work studies the distributed reentrant permutation flow shop scheduling.•The inclusion of sequence-dependent setup times into the problem is analyzed.•The goal is to minimize the makespan, ...production cost, and tardiness.•We propose an improved multi-objective adaptive large neighborhood search.•The algorithm is equipped with solution acceptance and archive updating criteria.
The distributed reentrant permutation flow shop (DRPFS) is a combination of the reentrant flow shop problem and distributed scheduling. The DRPFS is a NP-hard problem that consists of two subproblems: (1) assigning a set of jobs to a set of available factories and (2) determining the operation sequence of jobs in each factory. This paper is the first study to consider the inclusion of sequence-dependent setup time in the DRPFS. The industrial applications of flow shop indicate that the machine setup time to process a job may depend on the previously processed jobs. Particularly, in DRPFS, the effect of sequence-dependent setup time is intensified due to its reentrant characteristic. An improved version of the multi-objective adaptive large neighborhood search (MOALNS) is proposed as a solution method for the sequence-dependent DRPFS with the aim to minimize the makespan, production cost, and tardiness. The proposed algorithm enhances the standard MOALNS by embedding an improved solution acceptance and non-dominated set updating criteria to assist the algorithm in finding the near-optimal Pareto front of the factory allocation and scheduling problems. To address the multiple objectives and the issue of non-uniform setup time, a new set of destroy and repair heuristics are developed. Further, the numerical experiments demonstrate the efficiency of IMOALNS in finding high-quality solutions in a relatively short time.
The demand for distributed manufacturing systems (DMS) in the manufacturing sector has notably gained vast popularity as a suitable choice to accomplish sustainability benefits. Manufacturing ...companies are bound to face critical barriers in their pursuit of sustainability goals. However, the extent to which the DMS attributes relate to sustainable performance and impact critical barriers to sustainability is considerably unknown. To help close this gap, this article proposes a methodology to determine the relative importance of sustainability barriers, the influence of DMS on these barriers, and the relationship between DMS attributes and sustainable performance. Drawing upon a rich data pool from the Chinese manufacturing industry, the best-worst method is used to investigate the relative importance of the sustainability barriers and determine how the DMS attributes influence these barriers and relate to sustainability. The study findings show that "organizational barriers" are the most severe barriers and indicate that "reduced carbon emissions" has the highest impact on "organizational" and "sociocultural barriers" whereas public approval" has the highest impact on "organizational barriers." The results infer that "reduction of carbon emission" is the DMS strategy strongly linked to improved sustainable performance. Hence, the results can offer in-depth insight to decision-makers, practitioners, and regulatory bodies on the criticality of the barriers and the influence of DMS attributes on the sustainability barriers, and thus, improve sustainable performance for increased global competitiveness. Moreover, our study offers a solid foundation for further studies on the link between DMS and sustainable performance.
Given the significant proportion of the outsourced parts, components, and the complex assembly structure of the automobiles, agriculture machinery and heavy industry equipment, distributed production ...and flexible assembly are much-needed production scheduling settings to optimise their global supply chains. This research extends the distributed assembly permutation flowshop scheduling problem to account for flexible assembly and sequence-independent setup times (DPFSP_FAST) in a supply chain-like setting. For this purpose, an original mixed-integer linear programming (MILP) formulation to the DPFSP_FAST problem is first investigated. Considering makespan as the optimisation criterion, constructive heuristic and customised metaheuristic algorithms are then proposed to solve this emerging scheduling extension. Through extensive computational experiments, it is shown that the proposed algorithms outperform the existing best-performing algorithms to solve the DPFSP_FAST problem, yielding the best-found solutions in nearly all of the benchmark instances. Narrowing the gap between theory and practice, this study helps integrate the production planning scheduling across the supply chain.
Distributed manufacturing systems are seeing increased investment by manufacturers looking for a more responsive, robust, and sustainable means to meet product demand amid market fragility, ...mass-customisation, sustainability, supply chain uncertainty, national security, Net Zero and the Circular Economy. One such system is Additive Manufacturing farms where numerous machines are housed in warehouses across the globe. The realisation of these systems in industry is offering the opportunity to exploit research in agent-based manufacturing strategies for the brokering and coordination of jobs. Until now, much of the underpinning research has been numerical and validation via empirical study is required to provide confidence in deploying the strategies in an industrial setting. To provide this validation, this paper presents an Opensource Living Lab platform that can be used to evaluate agent-based manufacturing strategies.
Summary
Motivated by the rising costs of doing business overseas and the rise and implementation of digital technologies in production, new strategies are being explored to bring production and ...demand closer. While concepts like cloud computing, internet of things, and digital manufacturing increasingly gain relevance within the production activities of manufacturing companies, significant advances in three‐dimensional (3D) printing technologies offer the possibility for companies to accelerate product development and to consider new supply chain models. Under this production scheme, material supply chains are redefined and energy consumption hotspots are relocated throughout the life cycle of a product. This implies a diversification of energy mixes and raw material sources that poses a risk of shifting problems between life cycle phases and areas of protection. This study compares a conventional mass scale centralized manufacturing system against a 3D printing‐supported distributed manufacturing system on the basis of the production of one frame for eyeglasses using the life cycle assessment methodology. The study indicates clearly that the optimization potential is concentrated mainly in the energy consumption at the unit process level and exposes a close link to the printing material employed.
Industry 4.0 (I4.0) increases flexibility of production processes to fabricate products with a level of customization that resembles the era of crafts manufacturing. Despite global competition, ...personalization is a differentiation strategy applied by manufacturers to remain distinctive. By incorporating personalized products next to their core products, Small-, and Medium sized Enterprises (SMEs) can respond to the demand for personalized products where individualization adds value. Yet, personalization comes along with high variation due to small series, or even individually unique products. To cope with this variation, SMEs need cost-effective, intuitive solutions to benefit from I4.0 involving minimal efforts. This study presents a concept increasing the capacities of SMEs to capitalize on mass personalization via a collaborative network.
Abstract
To alleviate the greenhouse gas emissions by the chemical industry, electrification has been proposed as a solution where electricity from renewable sources is used to power processes. The ...adoption of renewable energy is complicated by its spatial and temporal variations. To address this challenge, we investigate the potential of distributed manufacturing for electrified chemical processes installed in a microgrid. We propose a multiscale mixed‐integer linear programming model for locating modular electrified plants, renewable‐based generating units, and power lines in a microgrid that includes monthly transportation and hourly scheduling decisions. We propose a K‐means clustering‐based aggregation disaggregation matheuristic to solve the model efficiently. The model and algorithm are tested using a case study with 20 candidate locations in Western Texas. Additionally, we define a new concept, “the Value of the Multi‐scale Model,” to demonstrate the additional economic benefits of our model compared with a single‐scale model.
Facing globalization trends and sustainable industrial development, energy-aware distributed manufacturing has become an emerging topic. Meanwhile, welding is a kind of indispensable processing in ...the development of manufacturing and its effective scheduling can improve production efficiency and reduce energy consumption. However, it is difficult to solve the energy-aware distributed welding shop scheduling problem (EADWSP) due to the characteristics of large scale and multiple objectives. Thus, this paper presents a mathematical model and a cooperative memetic algorithm (CMA) to addresses the EADWSP with minimization both makespan and total energy consumption. To improve the quality and diversity of initial population, a hybrid initialization is developed with a modified NEH based heuristic. Via taking full advantage of historical information, a cooperative search based on feedback is designed and a cooperative selection strategy is employed to balance the exploration and exploitation. In addition, multiple problem-specific operators are presented and a local intensification with Q-learning is designed to enhance exploitation capability. Numerical experiments are carried out and the results demonstrate the effectiveness of the above specific designs. The comparisons to the existing algorithms show superiority of the proposed CMA. Moreover, the application to a real-life case also verifies the effectiveness and practicability in solving the EADWSP.
•Energy-aware distributed welding shop scheduling with is studied.•Bi-objective mathematical model with economic and environmental indexes is presented.•Cooperative memetic algorithm with feedback mechanism is proposed.•Local intensification with Q-learning is developed.•More effective than existing algorithm in solving datasets and real-life case.
The forthcoming paradigm of Mass-Individualisation will combine the low-cost advantage of the Mass-Production paradigm with the buyer's individual need of a specific product. The buyers will initiate ...the product and its features that fit their explicit taste and individual needs, and the manufacturer will build it at low cost in a local factory, which enables a rapid communication of the factory with the buyer. The realisation of the mass-individualisation paradigm requires innovations in (a) product development, (b) manufacturing system and network design, (c) system operations, and (d) business strategies. New factories will have to be in proximity to the customers who participate in the design of their product, which will have an enormous economic impact on local economies. We elaborate on the research challenges and directions for the realisation of the emerging mass-individualisation paradigm.
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