Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments

Additive Manufacturing in the Supply Chain

Additive manufacturing (AM) is replacing traditional manufacturing approaches – such as subtractive and molding – in some industries. The product and supply chain impacts of AM continue to extend its industrial reach, improve engineer-to-order manufacturing, and pave the way to mass customization. This study explores the supply chain changes that may arise from a full or partial transition to AM-based production. Supply chain factors and dimensions that are greatly impacted are initially identified. Management and operational issues pertinent to each factor are discussed next. The interrelationships between these factors are then investigated considering the disruptive impact of AM on supply chain management. Next, the supply chain change matrix is presented for identifying the areas in that supply chains are expected to be impacted. Finally, the current literature and the future of AM-based supply chains are discussed. This chapter is concluded by providing a summary of the findings and insights into AM-based supply chain transition

Tailored Iterated Greedy metaheuristic for a scheduling problem in metal 3D printing

This article contributes to the additive manufacturing-based production planning literature by developing a Mixed-Integer Linear Programming (MILP) formulation for the Identical Parallel 3D-Printing Machines Scheduling Problem considering batching, multiple build platforms of restricted sizes, and sequence-independent setup times. Besides, a customized metaheuristic, named the Tailored Iterated Greedy (TIG) Algorithm is developed to solve the new optimization problem. TIG’s performance is evaluated through extensive numerical analysis and using a new testbed. It is shown that the customized computational mechanisms improve the optimization performance; statistical analysis is supportive of the significance of the resulting improvements. The developed mathematical model and optimization algorithm can be considered the basis for future developments in the optimization literature of additive manufacturing

Integrating sustainability into production scheduling in hybrid flow-shop environments

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop foors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid fow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers’ fexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversifcation, and quality metrics, providing more versatile and better solutions for production and energy efciency

Line Balancing Problem with Multi-Manned Workstations and Resource Constraints: The Case of Electronics Waste Disassembly

The increasing public awareness of environmental protection and the scarcity of rare earth elements have made closed-loop supply chains a necessity in many sectors. In particular, recycling components and parts from end-of-life consumer electronics have drawn the attention of both academics and practitioners. Disassembly line balancing improves the resource efficiency of recycling operations. This study proposes a new mathematical formulation and hybrid metaheuristics for solving the Disassembly Line Balancing Problem (DLBP) considering multi-manned workstations and resource constraints. The transformed AND/OR graph is used for prioritizing disassembly tasks in the modeling process. The method is applied for optimizing a real-world case of laptop disassembly to showcase the usefulness of the approach. The performance of the developed metaheuristics is compared to minimize the number of workstations, operators, and machines involved in the disassembly operations. Further, the results are analyzed through sensitivity analysis. This study concludes by providing practical insights and suggestions for the future development of DLBPs

N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

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

Delving Into the Interdependencies in the Network of Economic Sustainability Innovations

Legislative pressures and public awareness are urging companies to foster sustainability innovations that improve business operations. Limited studies explored the underpinnings of the economic dimension of sustainability innovations; studying economic innovation criteria in the manufacturing sector of emerging economies can inform other industries while recession fears loom the financial prospects. This article develops a decision analysis and evaluation framework for investigating the interdependencies in the network of economic sustainability innovation criteria using fuzzy Total Interpretive Structural Modeling (TISM). It is found that the ‘‘availability of financial resources for promoting innovation’’ is the criterion with the most network relations; this is what the managers should focus on to better pursue sustainability innovations in the supply chains and facilitate the shift towards sustainable industrial development. The study is concluded by providing practical insights into the economic dimension of sustainability innovations for industrial managers and academics

Exploring the mutual influence among the social innovation factors amid the COVID-19 pandemic

From the triple bottom line, the social aspect has received relatively limited attention during the Corona Virus Disease (COVID-19) pandemic, particularly in the emerging economies. Social innovation factors help improve the sustainability performance of the companies. This study develops a social innovation decision framework and analyses the interrelationships among social innovation factors considering the COVID-19 situation. For this purpose, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) is extended by integrating the Z numbers and rough fuzzy set theory into its computational procedure. Z-numbers address the uncertainty of the decision and experts’ confidence in the evaluation and rough numbers are used for aggregating the experts’ opinions. On this basis, the mutual influence of social innovation factors and the influence weights of these factors are investigated. The results suggest that a quick response to market demand for sustainable products is the most influential factor in attaining social sustainability innovation during the pandemic. This article is concluded by providing insights for industrial experts and decision-makers to understand the underpinnings of social sustainability innovation during unforeseen situations

Exploring indicators of circular economy adoption framework through a hybrid decision support approach

Circular economy (CE) focuses on a circular approach to energy and material resources, which provides economic, environmental and social benefits for manufacturing organisations. CE adoption in emerging economies facilitates in substantial economic growth through appropriate utilisation of energy and material resources across manufacturing industries. This study identifies CE indicators in the context of an emerging economy. The study further develops a framework for the adoption of CE and tests it through a hybrid Best Worst Method and Decision-Making Trial and Evaluation Laboratory approach. The framework is validated through an Indian manufacturing case organisation. While Best Worst Method computes the CE related indicator weights, Decision-Making Trial and Evaluation Laboratory analyses the inter-relationship among indicators. Disparate CE related indicators, e.g. strategic, managerial, informational and technological, supply chain and organisational, influence the CE adoption in an emerging economy context. The findings reveal that the strategic and managerial indicators have the strongest influence on developing other indicators. The causal digraph and relationship diagram assist the practitioners in predicting the inter-relationship of indicators in CE adoption. The study outcomes will help the practitioners, policymakers and researchers to draw a framework for adoption of circular and green practices and usage of resources sustainably

Exploring the curricular and pedagogical decision criteria for research-based learning design in undergraduate studies

Learning design has a multifaceted nature requiring a range of course- and institutional considerations. Analyzing the decision criteria’s influence on research-based learning design helps understand the causes of the success/failure of the approach in achieving the teaching goals to improve the study programs. This study explores the interrelationship between the curricular and pedagogical criteria for research-based learning design decisions at the undergraduate level. For this purpose, the DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) method is used to systematically analyze the decisive criteria and their causal relationships. Feedback from education professionals and university professors from Scandinavian universities is used to validate the pedagogy decision framework and provides input into the DEMATEL method. The student's role in the course is identified as the central criterion, featuring the highest level of interactions in the network of curricular and pedagogical decision criteria. Results are supportive of the identified institutional and course-specific criteria as prerequisites for the study outcomes