Högskolan i Skövde

his.sePublications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 17) Show all publications
Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2024). Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration. Computers & industrial engineering, 187, Article ID 109775.
Open this publication in new window or tab >>Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration
2024 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 187, article id 109775Article in journal (Refereed) Published
Abstract [en]

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Industry 4.0, assembly line balancing, scheduling, human-robot collaboration, line layout, mathematical model
National Category
Robotics Production Engineering, Human Work Science and Ergonomics
Research subject
VF-KDO; Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23413 (URN)10.1016/j.cie.2023.109775 (DOI)001135405700001 ()2-s2.0-85179002846 (Scopus ID)
Funder
VinnovaKnowledge Foundation
Note

CC BY 4.0 DEED

Corresponding author: Email: amir.nourmohammadi@his.se

This study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the VF-KDO, ACCURATE 4.0, and PREFER projects.

Available from: 2023-12-04 Created: 2023-12-04 Last updated: 2024-01-19Bibliographically approved
Barrera Diaz, C. A., Nourmohammadi, A., Smedberg, H., Aslam, T. & Ng, A. H. C. (2023). An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems. Mathematics, 11(6), Article ID 1527.
Open this publication in new window or tab >>An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems
Show others...
2023 (English)In: Mathematics, ISSN 2227-7390, Vol. 11, no 6, article id 1527Article in journal (Refereed) Published
Abstract [en]

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
reconfigurable manufacturing system, simulation, multi-objective optimization, knowledge discovery
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Virtual Production Development (VPD); VF-KDO
Identifiers
urn:nbn:se:his:diva-22329 (URN)10.3390/math11061527 (DOI)000960178700001 ()2-s2.0-85151391170 (Scopus ID)
Funder
Knowledge Foundation, 2018-0011
Note

CC BY 4.0

(This article belongs to the Special Issue Multi-Objective Optimization and Decision Support Systems)

Received: 15 February 2023 / Revised: 15 March 2023 / Accepted: 17 March 2023 / Published: 21 March 2023

The authors thank the Knowledge Foundation, Sweden (KKS) for funding this research through the KKS Profile Virtual Factories with Knowledge-Driven Optimization, VF-KDO, grant number 2018-0011.

Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2023-09-01Bibliographically approved
Slama, I., Arbaoui, T., Nourmohammadi, A. & Fathi, M. (2023). Assembly Line Balancing with Collaborative Robots Under Uncertainty of Human Processing Times. In: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT): . Paper presented at 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), July 03-06, 2023, Rome, Italy (pp. 2649-2653). IEEE
Open this publication in new window or tab >>Assembly Line Balancing with Collaborative Robots Under Uncertainty of Human Processing Times
2023 (English)In: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), IEEE, 2023, p. 2649-2653Conference paper, Published paper (Refereed)
Abstract [en]

This paper studies the assembly line balancing problem with collaborative robots in light of recent efforts to implement collaborative robots in industrial production systems under random processing time. A stochastic version with uncertain human processing time is considered for the first time. The issue is defined by the potential for simultaneous human and robot task execution at the same workpiece, either in parallel or in collaboration. We provide stochastic mixed-integer programming based on Monte Carlo sampling approach for the balancing and scheduling of collaborative robot assembly lines for this novel issue type. In order to minimise the line cost including fixed workstation operating costs and resource costs caused by exceeding cycle time, the model determines both the placement of collaborative robots at stations and the distribution of work among humans and robots.

Place, publisher, year, edition, pages
IEEE, 2023
Series
International Conference on Control, Decision and Information Technologies, ISSN 2576-3547, E-ISSN 2576-3555
Keywords
Production systems, Costs, Uncertainty, Job shop scheduling, Service robots, Collaboration, Stochastic processes
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23333 (URN)10.1109/CoDIT58514.2023.10284282 (DOI)2-s2.0-85177445826 (Scopus ID)979-8-3503-1141-9 (ISBN)979-8-3503-1140-2 (ISBN)979-8-3503-1139-6 (ISBN)
Conference
2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), July 03-06, 2023, Rome, Italy
Available from: 2023-10-28 Created: 2023-10-28 Last updated: 2023-11-30Bibliographically approved
Nourmohammadi, A., Ng, A. H. C., Fathi, M., Vollebregt, J. & Hanson, L. (2023). Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling. CIRP - Journal of Manufacturing Science and Technology, 47, 71-85
Open this publication in new window or tab >>Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling
Show others...
2023 (English)In: CIRP - Journal of Manufacturing Science and Technology, ISSN 1755-5817, E-ISSN 1878-0016, Vol. 47, p. 71-85Article in journal (Refereed) Published
Abstract [en]

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Mixed-model assembly line balancing, Digital human modeling, Musculoskeletal risks, Multi-objective optimization, Mathematical mode, lNSGA-II
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD); User Centred Product Design; VF-KDO
Identifiers
urn:nbn:se:his:diva-23240 (URN)10.1016/j.cirpj.2023.09.002 (DOI)001082058800001 ()2-s2.0-85171985258 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Corresponding author. E-mail address: amir.nourmohammadi@his.se (A. Nourmohammadi).

This study is supported by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the VF-KDO, ACCURATE 4.0, and PREFER Projects. The authors highly appreciate the valuable collaborations with the experts from the industrial partner of this research.

Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-11-06Bibliographically approved
Nourmohammadi, A., Fathi, M., Ng, A. H. C. & Mahmoodi, E. (2022). A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems. Paper presented at 55th CIRP Conference on Manufacturing Systems. Procedia CIRP, 107, 1444-1448
Open this publication in new window or tab >>A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems
2022 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 107, p. 1444-1448Article in journal (Refereed) Published
Abstract [en]

Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Human-robot collaboration, assembly line balancing, genetic algorithms
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21178 (URN)10.1016/j.procir.2022.05.172 (DOI)2-s2.0-85132304719 (Scopus ID)
Conference
55th CIRP Conference on Manufacturing Systems
Projects
VF-KDOACCURATE 4.0
Funder
Knowledge Foundation
Note

CC BY-NC-ND 4.0

Corresponding author: Amir Nourmohammadi

Edited by Emanuele Carpanzano, Claudio Boër, Anna Valente

This study is funded by the Knowledge Foundation (KKS), Sweden, through the VF-KDO and ACCURATE 4.0 projects at the University of Skövde, Sweden.

Available from: 2022-05-27 Created: 2022-05-27 Last updated: 2023-05-02Bibliographically approved
Barrera Diaz, C. A., Del Riego Navarro, A., Rico Perez, A. & Nourmohammadi, A. (2022). Availability Analysis of Reconfigurable Manufacturing System Using Simulation-Based Multi-Objective Optimization. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 369-379). Amsterdam; Berlin; Washington, DC: IOS Press
Open this publication in new window or tab >>Availability Analysis of Reconfigurable Manufacturing System Using Simulation-Based Multi-Objective Optimization
2022 (English)In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 369-379Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, manufacturing companies face an increasing number of challenges that can cause unpredictable market changes. These challenges are derived from a fiercely competitive market. These challenges create unforeseen variations and uncertainties, including new regional requirements or regulations, new technologies and materials, new market segments, increasing demand for new product features, etc. To cope with the challenges above, companies must reinvent themselves and design manufacturing systems that seek to produce quality products while responding to the changes faced. These capabilities are encompassed in Reconfigurable Manufacturing Systems (RMS), capable of dealing with uncertainties quickly and economically. The availability of RMS is a crucial factor in establishing the production capacity of a system that considers all events that could interrupt the planned production. The impact of the availability in RMS is influenced by the configuration of the systems, including the number of resources used. This paper presents a case study in which a simulation-based multi-objective optimization (SMO) method is used to find machines’ optimal task allocation and assignment to workstations under different scenarios of availability. It has been shown that considering the availability of the machines affects the optimal configuration, including the number of resources needed, such as machines and buffers. This study demonstrates the importance of the availability consideration during the design of RMS.

Place, publisher, year, edition, pages
Amsterdam; Berlin; Washington, DC: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
Reconfigurable Manufacturing System, Simulation, Multi-Objective Optimization, Availability
National Category
Production Engineering, Human Work Science and Ergonomics Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21098 (URN)10.3233/ATDE220156 (DOI)2-s2.0-85132823793 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022
Note

CC BY-NC 4.0

Corresponding Author: carlos.alberto.barrera.diaz@his.se

Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2023-02-22Bibliographically approved
Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2022). Balancing and scheduling assembly lines with human-robot collaboration tasks. Computers & Operations Research, 140, 1-18, Article ID 105674.
Open this publication in new window or tab >>Balancing and scheduling assembly lines with human-robot collaboration tasks
2022 (English)In: Computers & Operations Research, ISSN 0305-0548, E-ISSN 1873-765X, Vol. 140, p. 1-18, article id 105674Article in journal (Refereed) Published
Abstract [en]

In light of the Industry 5.0 trend towards human-centric and resilient industries, human-robot collaboration (HRC) assembly lines can be used to enhance productivity and workers’ well-being, provided that the optimal allocation of tasks and available resources can be determined. This study investigates the assembly line balancing problem (ALBP), considering HRC. This problem, abbreviated ALBP-HRC, arises in advanced manufacturing systems, where humans and collaborative robots share the same workplace and can simultaneously perform tasks in parallel or in collaboration. Driven by the need to solve the more complex assembly line-balancing problems found in the automotive industry, this study aims to address the ALBP-HRC with the cycle time and the number of operators (humans and robots) as the primary and secondary objective, respectively. In addition to the traditional ALBP constraints, the human and robot characteristics, in terms of task times, allowing multiple humans and robots at stations, and their joint/collaborative tasks are formulated into a new mixed-integer linear programming (MILP) model. A neighborhood-search simulated annealing (SA) is proposed with customized solution representation and neighborhood search operators designed to fit into the problem characteristics. Furthermore, the proposed SA features an adaptive neighborhood selection mechanism that enables the SA to utilize its exploration history to dynamically choose appropriate neighborhood operators as the search evolves. The proposed MILP and SA are implemented on real cases taken from the automotive industry where stations are designed for HRC. The computational results over different problems show that the adaptive SA produces promising solutions compared to the MILP and other swarm intelligence algorithms, namely genetic algorithm, particle swarm optimization, and artificial bee colony. The comparisons of human/robot versus HRC settings in the case study indicate significant improvement in the productivity of the assembly line when multiple humans and robots with collaborative tasks are permissible at stations.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
assembly line balancing, human-robot collaboration, multiple humans and robots, joint tasks, mathematical model, meta-heuristic
National Category
Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-20805 (URN)10.1016/j.cor.2021.105674 (DOI)000752776300009 ()2-s2.0-85122478755 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Available online 18 December 2021

Available from: 2021-12-20 Created: 2021-12-20 Last updated: 2023-02-22Bibliographically approved
Smedberg, H., Barrera Diaz, C. A., Nourmohammadi, A., Bandaru, S. & Ng, A. H. C. (2022). Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems. Mathematical and Computational Applications, 27(6), Article ID 106.
Open this publication in new window or tab >>Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems
Show others...
2022 (English)In: Mathematical and Computational Applications, ISSN 1300-686X, E-ISSN 2297-8747, Vol. 27, no 6, article id 106Article in journal (Refereed) Published
Abstract [en]

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today's manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
multi-objective optimization, knowledge discovery, reconfigurable manufacturing system, simulation
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-22194 (URN)10.3390/mca27060106 (DOI)000904384800001 ()
Funder
Knowledge Foundation, 2018-0011
Note

CC BY 4.0

Correspondence: henrik.smedberg@his.se

This work was funded by the Knowledge Foundation (KKS), Sweden, through the KKS Profile Virtual Factories with Knowledge-Driven Optimization, VF-KDO, Grant No. 2018-0011.

(This article belongs to the Special Issue Evolutionary Multi-objective Optimization: An Honorary Issue Dedicated to Professor Kalyanmoy Deb)

Available from: 2023-01-19 Created: 2023-01-19 Last updated: 2023-09-01Bibliographically approved
Nourmohammadi, A., Eskandari, H., Fathi, M. & Ng, A. H. C. (2021). Integrated locating in-house logistics areas and transport vehicles selection problem in assembly lines. International Journal of Production Research, 59(2), 598-616
Open this publication in new window or tab >>Integrated locating in-house logistics areas and transport vehicles selection problem in assembly lines
2021 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, no 2, p. 598-616Article in journal (Refereed) Published
Abstract [en]

Decentralised in-house logistics areas, known as supermarkets, are widely used in the manufacturing industry for parts feeding to assembly lines. In contrary to the literature and inspired by observation in a real case, this study relaxes the assumption of using identical transport vehicles when deciding on the supermarkets’ location by considering the availability of different vehicles. In this regard, this study deals with the integrated supermarket location and transport vehicles selection problems (SLTVSP). A mixed-integer programming (MIP) model of the problem is developed. Due to the complexity of the problem, a hybrid genetic algorithm (GA) with variable neighborhood search (GA-VNS) is also proposed to address large-sized problems. The performance of GA-VNS is compared against the MIP, the basic GA, and simulated annealing (SA) algorithm. The computational results from the real case and a set of generated test problems show that GA-VNS provides a very good approximation of the MIP solutions at a much shorter computational time while outperforming the other compared algorithms. The analysis of the results reveals that it is beneficial to apply different transport vehicles rather than identical vehicles for SLTVSP.

Place, publisher, year, edition, pages
Taylor & Francis, 2021
Keywords
In-house logistics, supermarket location, parts feeding, transport vehicles, mixed-integer programming, genetic algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-18018 (URN)10.1080/00207543.2019.1701207 (DOI)000503313800001 ()2-s2.0-85076905476 (Scopus ID)
Note

Published online: 17 Dec 2019

Available from: 2019-12-18 Created: 2019-12-18 Last updated: 2021-02-24Bibliographically approved
Amouzgar, K., Nourmohammadi, A. & Ng, A. H. C. (2021). Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm. International Journal of Production Research, 59(12), 3572-3590
Open this publication in new window or tab >>Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm
2021 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, no 12, p. 3572-3590Article in journal (Refereed) Published
Abstract [en]

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices. 

Place, publisher, year, edition, pages
Taylor & Francis Group, 2021
Keywords
Tool indexing, genetic algorithm, non-machining time, multi-objective optimisation, SPEA2, mathematical model
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-19535 (URN)10.1080/00207543.2021.1897174 (DOI)000628710300001 ()2-s2.0-85102698141 (Scopus ID)
Projects
VF-KDO
Funder
Knowledge Foundation, HSK2019/20
Note

CC BY-NC-ND 4.0

Published online: 13 Mar 2021

Available from: 2021-03-15 Created: 2021-03-15 Last updated: 2023-02-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6280-1848

Search in DiVA

Show all publications