This paper presents a multi-period location–production problem involving small recycling units that are embedded in standard containers and can, therefore, be relocated from site to site at short ...notice. The waste generated over time at different locations can be stored up to a specific limit, at which point recycling must occur. A mixed-integer program is implemented to plan the relocation of the mobile recycling units in such a way that the total cost resulting from waste transport and facility relocation is minimized. To solve large instances, three heuristics are developed that form the basis for a detailed computational study. The results reveal that mobile recycling units can significantly reduce the total costs compared to centralized recycling because (a) a larger number of mobile recycling units, (b) larger storage capacities, and (c) more short-term relocation possibilities increase the optimization scope and thus the possibility to reduce the total transport distances, and thereby, the costs. However, achieving this requires intelligent planning that considers the complex interdependencies between the influencing parameters and balances the existing trade-offs.
•An optimization framework for decentralized, mobile recycling is presented.•One exact and three heuristic solution methods are evaluated.•The mobility of the recycling units allows cost reductions.
Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products ...of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re-usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future.
Abstract
System-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production ...planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It is shown that the N-list-enhanced Constructive Heuristic algorithm performs significantly better than the current best-performing algorithm and three new metaheuristics in terms of both solution quality and computational time. It can, therefore, be considered a competitive benchmark for future studies on distributed production scheduling and computing.
In the post-pandemic era, more manufacturers have expedited the shift from centralized to distributed manufacturing to enhance supply chain resilience. Along with this, the distributed shop floor ...scheduling problem has attracted much attention from academia, one of which is the distributed flexible job-shop scheduling problem (DFJSP). Nonetheless, the majority of research on DFJSPs overlooks crucial real-world necessities, such as multi-objective decision making and preventive maintenance (PM). Thus, this article suggests a multi-objective DFJSP with PM (DFJSP/PM) as a new variant of the DFJSP. The aim is to achieve a trade-off between production and maintenance to minimize the makespan, maintenance cost, and energy consumption. To this end, we establish a mathematical model and then customize a learning-assisted bi-population evolutionary algorithm (LBPEA) to solve it. In LBPEA, a novel encoding mechanism is proposed to initialize the population randomly. Then, a neighborhood search heuristic is designed to enhance the population's quality. To balance the convergence and diversity of the population, a bi-population evolution idea is introduced during the environmental selection. Besides, a two-stage local search (LS) process is adaptively triggered to balance the allocation of computational resources between exploration and exploitation. At the first stage, a reinforcement learning mechanism is employed to intelligently select LS operators to adjust either the operations' sequence or assignment to different factories and machines, while the second stage is to adjust the number and placement of maintenance decisions. Experimental results show that LBPEA has excellent performance in terms of convergence and diversity when solving the proposed multi-objective DFJSP/PM.
Manufacturing has been the key factor limiting rollout of vaccination during the COVID‐19 pandemic, requiring rapid development and large‐scale implementation of novel manufacturing technologies. ...ChAdOx1 nCoV‐19 (AZD1222, Vaxzevria) is an efficacious vaccine against SARS‐CoV‐2, based upon an adenovirus vector. We describe the development of a process for the production of this vaccine and others based upon the same platform, including novel features to facilitate very large‐scale production. We discuss the process economics and the “distributed manufacturing” approach we have taken to provide the vaccine at globally‐relevant scale and with international security of supply. Together, these approaches have enabled the largest viral vector manufacturing campaign to date, providing a substantial proportion of global COVID‐19 vaccine supply at low cost.
Well over a billion doses of the ChAdOx1 nCoV‐19 vaccine developed by The University of Oxford and AstraZeneca have been produced and distributed at low cost. This represents a substantial proportion of global COVID‐19 vaccine supply to date, especially in low‐ and middle‐income countries. The authors report on the development of the manufacturing process for the vaccine, and the process economics and distributed manufacturing approach involving bulk vaccine (drug substance) production on five continents.
Blockchain-based Shared Additive Manufacturing Lupi, Francesco; Cimino, Mario G.C.A.; Berlec, Tomaž ...
Computers & industrial engineering,
September 2023, 2023-09-00, Volume:
183
Journal Article
Peer reviewed
Open access
•Resource pooling increases utilization in presence of unexpected circumstances.•Ontology of the problem domain for protocol developed in process language.•Smart contracts and blockchain technology ...enable advanced manufacturing.•Increased resilience of shared manufacturing system is proven by simulation.•Python code developed for simulation publicly available.
Today, globalized markets require more resilient and agile manufacturing systems, as well as customized and virtualized features. Classical self-standing manufacturing systems are evolving into collaborative networks such as Cloud Manufacturing (based on centralized knowledge and distributed resources) or Shared Manufacturing (based on fully decentralized knowledge and distributed resources) as a solution to ensure business continuity under normal as well as special circumstances. Additive Manufacturing (AM), one of the enablers of Industry 4.0 (I4.0), is a promising technology for innovative production models due to its inherent distributed capabilities, digital nature, and product customization ability. To increase the adaptivity of distributed resources using AM technology, this paper proposes a mechanism for sharing workload and resources under unexpected behaviours in the supply chain. Smart contracts and blockchain technology in this concept are used to provide decentralized, transparent, and trusted operation of such systems, which provide more resilience to disruptive factors. In this paper, the proposed Blockchain-based Shared Additive Manufacturing (BBSAM) protocol, ontology, and workflow for AM capacity pooling are discussed and analysed under special conditions such as anomalous demand. Discrete-time Python simulation on a real Italian AM market dataset, also provided, is available on GitHub.
•Open-source 3-D printers makes distributed manufacturing technically feasible.•Self-replicating rapid prototypers (RepRaps) can manufacture half of their own parts.•Life-cycle economic analysis of ...RepRap technology for US household provided.•Open-source 3-D printers recover material costs in less than 1year, >200% ROI.•Open-source designs growing exponentially predicts distributed manufacturing scaling.
The recent development of open-source 3-D printers makes scaling of distributed additive-based manufacturing of high-value objects technically feasible and offers the potential for widespread proliferation of mechatronics education and participation. These self-replicating rapid prototypers (RepRaps) can manufacture approximately half of their own parts from sequential fused deposition of polymer feedstocks. RepRaps have been demonstrated for conventional prototyping and engineering, customizing scientific equipment, and appropriate technology-related manufacturing for sustainable development. However, in order for this technology to proliferate like 2-D electronic printers have, it must be economically viable for a typical household. This study reports on the life-cycle economic analysis (LCEA) of RepRap technology for an average US household. A new low-cost RepRap is described and the costs of materials and time to construct it are quantified. The economic costs of a selection of 20 open-source printable designs (representing less than 0.02% of those available), are typical of products that a household might purchase, are quantified for print time, energy, and filament consumption and compared to low and high Internet market prices for similar products without shipping costs. The results show that even making the extremely conservative assumption that the household would only use the printer to make the selected 20 products a year the avoided purchase cost savings would range from about $300 to $2000/year. Assuming the 25h of necessary printing for the selected products is evenly distributed throughout the year these savings provide a simple payback time for the RepRap in 4months to 2years and provide an ROI between >200% and >40%. As both upgrades and the components that are most likely to wear out in the RepRap can be printed and thus the lifetime of the distributing manufacturing can be substantially increased the unavoidable conclusion from this study is that the RepRap is an economically attractive investment for the average US household already. It appears clear that as RepRaps improve in reliability, continue to decline in cost and both the number and assumed utility of open-source designs continues growing exponentially, open-source 3-D printers will become a mass-market mechatronic device.
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