Search results
1 – 10 of 117
This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.
Abstract
Purpose
This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.
Design/methodology/approach
In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.
Findings
The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.
Originality/value
In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.
Details
Keywords
Patrik Jonsson, Johan Öhlin, Hafez Shurrab, Johan Bystedt, Azam Sheikh Muhammad and Vilhelm Verendel
This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?
Abstract
Purpose
This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?
Design/methodology/approach
A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.
Findings
The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.
Practical implications
The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.
Originality/value
The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?
Details
Keywords
The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process…
Abstract
Purpose
The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.
Design/methodology/approach
The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.
Findings
Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.
Originality/value
This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.
Details
Keywords
Armando Calabrese, Antonio D'Uffizi, Nathan Levialdi Ghiron, Luca Berloco, Elaheh Pourabbas and Nathan Proudlove
The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.
Abstract
Purpose
The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.
Design/methodology/approach
The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.
Findings
The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.
Originality/value
The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.
Details
Keywords
Assembly line is a common production form and has been effectively used in many industries, but the imprecise processing time of each process makes production line balancing and…
Abstract
Purpose
Assembly line is a common production form and has been effectively used in many industries, but the imprecise processing time of each process makes production line balancing and capacity forecasting the most troublesome problems for production managers. In this paper, uncertain man-hours are represented as interval grey numbers, and the optimization problem of production line balance in the case of interval grey man-hours is studied to better evaluate the production line capacity.
Design/methodology/approach
First, this paper constructs the basic model of assembly line balance optimization for the single-product scenario, and on this basis constructs an assembly line balance optimization model under the multi-product scenario with the objective function of maximizing the weighted greyscale production line balance rate, second, this paper designs a simulated annealing algorithm to solve problem. A neighborhood search strategy is proposed, based on assembly line balance optimization, an assembly line capacity evaluation method with interval grey man-hour characteristics is designed.
Findings
This paper provides a production line balance optimization scheme with uncertain processing time for multi-product scenarios and designs a capacity evaluation method to provide managers with scientific management strategies so that decision-makers can scientifically solve the problems that the company's design production line is quite different from the actual production situation.
Originality/value
There are few literary studies on combining interval grey number with assembly line balance optimization. Therefore, this paper makes an important contribution in this regard.
Details
Keywords
Anna Trubetskaya, Alan Ryan, Daryl John Powell and Connor Moore
Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk…
Abstract
Purpose
Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.
Design/methodology/approach
Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.
Findings
Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.
Practical implications
The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.
Originality/value
To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.
Details
Keywords
The existing literature has been mainly focused on local problems but without an overall framework for studying the top-level planning of intelligent construction from a…
Abstract
Purpose
The existing literature has been mainly focused on local problems but without an overall framework for studying the top-level planning of intelligent construction from a systematic perspective. The purpose of this paper is to fill this gap.
Design/methodology/approach
This research adopts a deductive research approach.
Findings
This research proposes a reference architecture and related business scenario framework for intelligent construction based on the existing theory and industrial practice.
Originality/value
The main contribution of this research is to provide a useful reference to the Chinese government and industry for formulating digital transformation strategies, as well as suggests meaningful future research directions in the construction industry.
Details
Keywords
Anil Kumar Inkulu and M.V.A. Raju Bahubalendruni
In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study…
Abstract
Purpose
In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.
Design/methodology/approach
A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.
Findings
The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.
Originality/value
This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.
Details
Keywords
Laura Lucantoni, Sara Antomarioni, Filippo Emanuele Ciarapica and Maurizio Bevilacqua
The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely…
Abstract
Purpose
The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.
Design/methodology/approach
Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.
Findings
The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.
Originality/value
The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.
Details
Keywords
Chuanmin Mi, Xiaoyi Gou, Yating Ren, Bo Zeng, Jamshed Khalid and Yuhuan Ma
Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system…
Abstract
Purpose
Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.
Design/methodology/approach
First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.
Findings
Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.
Originality/value
Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.
Highlights
The highlights of the paper are as follows:
A new seasonal grey buffer operator is constructed.
The impact of shock perturbations on seasonal data trends is effectively mitigated.
A novel seasonal grey forecasting approach with multi-method fusion is proposed.
Seasonal electricity consumption is successfully predicted by the novel approach.
The way to adjust China's power system flexibility in the future is analyzed.
A new seasonal grey buffer operator is constructed.
The impact of shock perturbations on seasonal data trends is effectively mitigated.
A novel seasonal grey forecasting approach with multi-method fusion is proposed.
Seasonal electricity consumption is successfully predicted by the novel approach.
The way to adjust China's power system flexibility in the future is analyzed.
Details