Design of redistributed manufacturing networks: a model-based framework

Throughout the last century, manufacturing has been characterised by mass production conducted in central facilities, benefitting from economies-of-scale. These central facilities supply, and are supplied by sprawling, complex supply chains that are slow to adapt to demand changes and supply disruptions. As production strategies are gradually shifting from economies-of-scale to economies-of-scope to cater for increasingly complex heterogeneous demand and shorter product life cycles, new configurations are required to enable manufacturing systems to accommodate these demand changes efficiently. One area that has the potential to improve the responsiveness of manufacturing systems is redistributed manufacturing (RdM). RdM is a manufacturing paradigm where production is performed in a network of small, autonomous and geographically distributed facilities. Motivated by the potential opportunities that RdM could bring, this thesis develops a model-based decision-making framework for the design and operation of RdM networks. The framework is context-independent, addresses strategic, tactical and operational decision-making levels and accounts for the interdependence between these decisions in a stochastic environment. The framework is validated methodically through computational experiments on two case studies of different natures and objectives. Experts opinions were solicited throughout the design stage of this research, the implementation of the case studies and the analysis of the results. Results reveal that even when the objectives of the modelled systems are substantially different, the framework generates consistent outputs. The main takeout from the experiments’ results is that the RdM paradigm consistently produces significantly better service level performance, demonstrated by fewer occurrences of unmet demands and shorter lead times. However, although sufficiently close, the RdM paradigm is not as cost efficient as the traditional centralised manufacturing paradigm.Manufacturin

Reconfigurable manufacturing system scheduling: a deep reinforcement learning approach

Reconfigurable Manufacturing Systems (RMS) bring new possibilities toward meeting demand fluctuations while, at the same time, challenges scheduling efficiency. This paper presents a novel approach that, for the scheduling problem of RMS on multiple products, finds a dynamic control policy via a group of deep reinforcement learning agents. These teamed agents, embedded with a shared value decomposition network, aim on minimising the make-span of a constant updating order group by guiding a group of automated guided vehicles to move modules of machine, raw materials, and finished products inside the system

Design of emergency response manufacturing networks: a decision-making framework

In times of large-scale crises, seemingly streamlined supply chains could become prone to unforeseen disruptions, leading to interruption in the provision of vital supplies. This could lead to severe consequences if such interruptions include vital products, such as lifesaving medical supplies or healthcare workers’ protective gear. Shortages of vital supplies could occur due to unexpected sharp spike in demand, where manufacturers are unable to produce the necessary quantities required to meet the unusual demand. They could also be the result of a disruption in the product’s supply chain, originating in another country, or even continent, worse affected by the crisis. In either case, localized production, enabled by efforts and resources of local establishments and individuals, could provide a contingency means to produce such vital products to serve local needs, temporarily. Motivated by the growing availability of advanced manufacturing technologies, in particular additive manufacturing (AM), this paper aims to develop a decision-making framework for the design of AM enabled local manufacturing networks in times of crises. The framework consists of complementing interrelated optimization and simulation models that operate iteratively in an uncertain environment, until a local production network, producing the desired performance targets, emerges. Finally, a case study inspired by the shortages of medical supplies, and healthcare workers’ personal protective equipment (PPE), during the worldwide 2020 outbreak of the COVID-19 coronavirus is employed to demonstrate the applicability of the framewor

Redistributed manufacturing of spare parts: an agent-based modelling approach

Maintenance and repair activities from the perspective of OEMs are both considerable sources of revenue and expenses, particularly when part of a Product Service System (PSS). It is therefore necessary for an OEM that provides services bundled with products to ensure timely response without significant impact on cost. This paper proposes a make-to-order spare parts supply chain strategy through the adoption of Redistributed Manufacturing (RdM) where the supply chain is shortened and total cost is decreased. An agent-based model that portrays an OEM’s response to repair a failed equipment is developed to exhibit the potential time and cost savings gained by OEMs

A decision-making framework for the design of local production networks under largescale disruptions

In this paper, a model-based decision-making framework for the design of localized networked production systems under largescale disruptions is developed. The framework consists of optimization and agent-based simulation models that run successively in an iterative manner, gradually improving the performance of the perceived system. The framework integrates uncertainty, provides decisions at different decision-making levels and embeds an algorithm that allows for communication between demand nodes and production sites once inventory shortages occur. The framework has been applied on a case study for the design of localized production and distribution networks, powered by additive manufacturing (AM), in South East England during the early stages of the COVID-19 pandemic outbreak. Results revealed that implementing the framework indeed results in performance improvements to AM-powered production networks, particularly with regards to inventory shortages and lead time

Applying the big bang-big crunch metaheuristic to large-sized operational problems

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature

The concept of carbon accounting in manufacturing systems and supply chains

Carbon accounting is primarily a process for measuring, reporting, and allocating greenhouse gas emissions from human activities, thus enabling informed decision-making to mitigate climate change and foster responsible resource management. There is a noticeable upsurge in the academia regarding carbon accounting, which engenders complexity due to the heterogeneity of practices that fall under the purview of carbon accounting. Such plurality has given rise to a situation where diverse interpretations of carbon accounting coexist, often bereft of uniformity in definition and application. Consequently, organisations need a standardised, comprehensive, and sequentially delineated carbon accounting framework amenable to seamless integration into end-to-end manufacturing systems. This research commences with the progressive evolution of the conceptual definition of carbon accounting. Then, it delves into the current state of carbon accounting in manufacturing systems and supply chains, revealing gaps and implementation issues warranting future scholarly exploration

Carbon accounting management in complex manufacturing supply chains: a structured framework approach

Improving the management of carbon emissions in the drive to Net-Zero can involve both complex measurements and the development of cleaner technologies, which is a demanding challenge for both the private and public sectors. Specifically, within complex and often sensitive supply chains such as aerospace manufacturing, accounting for carbon management requires quantification of the extended enterprise’s direct and indirect emissions as a system. Currently however, there is a lack of standardised methods for carbon accounting suitable for use in the measurement and auditing of carbon performance both in the production process as well as in the supply chain. This research presents a structured framework-based approach, that could facilitate accurate, consistent and simplified management of carbon scoping, measurement and reporting, across complex extended supply chains. The proposed five step approach sets a thematic orientation for future customisation of carbon accounting tools at every step of the framework

Multi-objective reconfigurable manufacturing system scheduling optimisation: a deep reinforcement learning approach

Rapid product design updates, unstable supply chains, and erratic demand phenomena are challenging current production modes. Reconfigurable manufacturing systems (RMS) aim to provide a cost-effective solution for responding to these challenges. However, given their complex adjustable nature, RMSs cannot fully unlock their potential by applying old-fashion fixed dispatching rules. Reinforcement learning (RL) algorithms offer a useful approach for finding optimal solutions in such complex systems. This paper presents a framework to train a scheduling agent based on a proximal policy optimisation (PPO) algorithm. The results of a numerical case study that implemented the framework on a simplified RMS model, suggest a good level of robustness and reveal areas of unpredictable behaviour that could be the focus of further research

Factors affecting intention to breastfeed among Syrian and Jordanian mothers: a comparative cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Breastfeeding is considered the ideal method of infant feeding for at least the first six months of life. This study aimed to compare breastfeeding intention between Syrian and Jordanian women and determine factors associated with breastfeeding intention among pregnant women in these two countries.</p> <p>Methods</p> <p>A cross-sectional design was used to collect data from1200 pregnant women aged 18 years and above (600 participants from each country). A self- administered questionnaire was used to collect data on socio-demographic characteristics and breastfeeding intention.</p> <p>Results</p> <p>Intention to breastfeed was reported by 77.2% of Syrian and 76.2% of Jordanian pregnant women. There was no significant difference in intention to breastfeed between Syrian women and Jordanian women. In both countries, women with a more positive attitude to breastfeeding, women with previous breastfeeding experience and women with supportive partners were more likely to intend to breastfeed. Syrian women with a monthly family income of more than US$200, younger than 25 and primiparous or having one child were more likely to report an intention to breastfeed their infants. Jordanian women with an education level of less than high school and not living with their family-in-law were more likely to intend to breastfeed.</p> <p>Conclusions</p> <p>In Syria and Jordan, a more positive attitude to breastfeeding, previous breastfeeding experience and presence of supportive husbands are associated with intention to breastfeed. These factors should be considered when planning programs designed to promote breastfeeding in these two countries.</p