Value-Based Manufacturing Optimisation in Serverless Clouds for Industry 4.0

There is increasing impetus towards Industry 4.0, a recently proposed roadmap for process automation across a broad spectrum of manufacturing industries. The proposed approach uses Evolutionary Computation to optimise real-world metrics. Features of the proposed approach are that it is generic (i.e. applicable across multiple problem domains) and decentralised, i.e. hosted remotely from the physical system upon which it operates. In particular, by virtue of being serverless, the project goal is that computation can be performed `just in time' in a scalable fashion. We describe a case study for value-based optimisation, applicable to a wide range of manufacturing processes. In particular, value is expressed in terms of Overall Equipment Effectiveness (OEE), grounded in monetary units. We propose a novel online stopping condition that takes into account the predicted utility of further computational effort. We apply this method to scheduling problems in the (max,+) algebra, and compare against a baseline stopping criterion with no prediction mechanism. Near optimal profit is obtained by the proposed approach, across multiple problem instances

Strategy using modularity tools to operationalize mass customization in manufacturing small and medium-sized enterprises

Abstract With the rise of technology, increasing competitiveness, market globalization and the fourth industrial revolution, companies are forced to rethink the way they do business to create or maintain a competitive advantage. Consumers, who are increasingly informed, demanding and concerned about sustainable development, are forcing companies to adapt to their needs to respond adequately to personalized demand. Small and medium-sized enterprises (SMEs) in the manufacturing sector must adjust to this new context. The move towards mass customization is one way of meeting customer requirements. However, no strategy for making this shift currently exists in the literature. The aim of this article is to present a strategy for operationalizing mass customization using modular tools. Action research is used to test the proposed strategy. The paper proposes 4 transformation axes to migrate towards mass customization: Modular product design, Modular process design, Technology use, Collaboration network. This article also highlights the need to tackle modular product design first to migrate to mass customization, by proposing a 3-stage strategy: modular product architecture, standardization of interfaces and definition of configuration rules. A case study is used to test the proposed strategy

Assessing the key enablers for Industry 4.0 adoption using MICMAC analysis: A case study

Purpose: The aim of this research is to assess the key enablers of Industry 4.0 (I4.0) in the context of the Indian automobile industry. It is done to apprehend their comparative effect on executing I4.0 concepts and technology in manufacturing industries, in a developing country context. The progression to I4.0 grants the opportunity for manufacturers to harness the benefits of this industry generation. Design/methodology/approach: The literature related to I4.0 has been reviewed for the identification of key enablers of I4.0. The enablers were further verified by academic professionals. Additionally, key executive insights had been revealed by using interpretive structural modelling (ISM) model for the vital enablers unique to the Indian scenario. The authors have also applied MICMAC analysis to group the enablers of I4.0. Findings: The analysis of this study’s data from respondents using ISM provided us with seven levels of enabler framework. This study adds to the existing literature on I4.0 enablers and findings highlight the specificities of the territories in India context. The results show that top management is the major enabler to I4.0 implementation. Infact, it occupies the 7th layer of the ISM framework. Subsequently, government policies enable substantial support to develop smart factories in India. Practical implications: The findings of this work provide implementers of I4.0 in the automobile industry in the form of a robust framework. This framework can be followed by the automobile sector in enhancing their competency in the competitive market and ultimately provide a positive outcome for the Indian economic development led by these businesses. Furthermore, this work will guide decision-makers in enabling strategic integration of I4.0, opening doors for the development of new business opportunities as well. Originality/value: The study proposes a framework for Indian automobile industries. The automobile sector was chosen for this study as it covers a large percentage of the market share of the manufacturing industry in India. The existing literature does not address the broader picture of I4.0 and most papers do not provide validation of the data collected. This study thus addresses this research gap

Performance-Predictable Resource Management of Container-based Genetic Algorithm Workloads in Cloud Infrastructure

Cloud computing, adopted by major providers like Amazon and Google, offers on-demand, pay-as-you-go services and resources through shared pools. Users submit workloads comprising multiple jobs, each containing tasks, including a specific genetic algorithm (GA) workload detailed in this thesis. This GA workload contains independent tasks from real-time multiprocessor allocation and Sudoku puzzle case studies, each with fixed deadlines and fitness requirements. Effective resource management is critical to enhance the Quality of Service (QoS) for cloud users. It involves resource allocation and adhering to QoS standards, guided by workload specifics. Container orchestration emerges as an essential deployment and management approach. This thesis focuses on managing multiple instances of genetic algorithms (GAs) in a cloud environment to achieve user-defined fitness levels within specified deadlines. It presents various approaches to allocate GAs to cloud nodes and control their execution iteratively. Initially, it introduces approaches such as fitness tracking (FT), fitness prediction (FP), fitness-prediction-based linear regression (FPLR), and fitness prediction based on weighted least squares (FPWLS) for managing the workload. To enhance resource efficiency, the thesis also addresses node interference, allowing multiple tasks to share resources while minimizing their impact on each other. It proposes a weighted-based node interference approach, considering fitness levels and response times during iterations to optimize task allocation. The performance of these approaches was experimentally evaluated by testing two GA applications and comparing them against state-of-the-art container-based orchestration approaches. Thus, different approaches were compared considering the number of successful tasks which can be defined by the number of tasks executed on time and achieved the fitness required. Comparison was also made between different approaches by taking iteration analysis into consideration. In situations where performance prediction was used, prediction errors like Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used to evaluate and compare the performance of the prediction approaches