Högskolan i Skövde

his.sePublications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 49) Show all publications
Rabet, R., Ganji, M. & Fathi, M. (2024). A simheuristic approach towards supply chain scheduling: Integrating production, maintenance and distribution. Applied Soft Computing, 153, Article ID 111264.
Open this publication in new window or tab >>A simheuristic approach towards supply chain scheduling: Integrating production, maintenance and distribution
2024 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 153, article id 111264Article in journal (Refereed) Published
Abstract [en]

This study attempts to integrate production, maintenance, and delivery operations among supply chain members. Despite numerous studies in the field of supply chain management, researchers have often overlooked crucial aspects, such as uncertainties in demand and production. For instance, the significant impact of maintenance activities on production flow has been underrepresented in supply chain management literature. This study investigates these gaps in the context of a fertilizer producer case study, which is characterized by seasonal demand and the functional silos syndrome due to old-fashioned management approaches. This study proposes a mathematical model and two multi-objective simheuristics for the Integrated Production, Maintenance, and Distribution Scheduling Problem (IPMDSP) considering demand variation for multiple products and product delivery time-windows using a heterogeneous fleet of vehicles. The IPMDSP is solved using the ϵ-constraint method and simheuristics linking the simulation model to customized and tuned versions of Particle Swarm Optimization (MOPSO) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimization objectives include minimizing maintenance duration, distribution costs, and customer dissatisfaction due to delivery tardiness. The results demonstrate the superiority of the simheuristic empowered by NSGA-II over the MOPSO in solving the IPMDSP. The comparison between the performance of deterministic and stochastic approaches in addressing the problem reveals that neglecting uncertainty caused by maintenance activities can lead to an increase in optimization objectives. Furthermore, the proposed simheuristics achieved significant improvements in minimizing objectives in solving the fertilizer producer case study. 

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Distribution, Heterogeneous vehicles routing problem, Integrated supply chain, Maintenance, Simheuristic, Fertilizers, Fleet operations, Genetic algorithms, Heuristic algorithms, Particle swarm optimization (PSO), Screening, Stochastic systems, Supply chain management, Heterogeneous vehicle routing problem, Heterogeneous vehicles, Integrated maintenance, Integrated production, Production distribution, Production Scheduling, Vehicle Routing Problems
National Category
Production Engineering, Human Work Science and Ergonomics Transport Systems and Logistics Other Civil Engineering
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23563 (URN)10.1016/j.asoc.2024.111264 (DOI)2-s2.0-85183117679 (Scopus ID)
Note

© 2024 Elsevier B.V.

Correspondence Address: R. Rabet; Department of Industrial Engineering, Islamic Azad University Science and Research Branch, Tehran, Iran; email: r.rabet@srbiau.ac.ir

Available from: 2024-02-01 Created: 2024-02-01 Last updated: 2024-02-26
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
Mahmoodi, E., Fathi, M., Tavana, M., Ghobakhloo, M. & Ng, A. H. C. (2024). Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing. Journal of manufacturing systems, 72, 287-307
Open this publication in new window or tab >>Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
Show others...
2024 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 72, p. 287-307Article in journal (Refereed) Published
Abstract [en]

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Resource allocation, High-mix low-volume, Multi-objective optimization, Data-driven simulation, Decision support system, Industry 4.0, Meta-learning
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23465 (URN)10.1016/j.jmsy.2023.11.019 (DOI)001140004800001 ()2-s2.0-85183766753 (Scopus ID)
Projects
ACCURATE 4.0PREFER
Funder
Knowledge FoundationVinnova
Note

CC BY 4.0 DEED

Corresponding author at: Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden. E-mail address: masood.fathi@his.se (M. Fathi).

This study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency via the ACCURATE 4.0 (grant agreement No. 20200181) and PREFER projects, respectively.

Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2024-02-15Bibliographically approved
Beheshtinia, M. A., Sedady, F., Fathi, M., Ghobakhloo, M. & Iranmanesh, M. (2023). A fuzzy three-dimensional house of quality to integrate and coordinate departments’ activities in organizations. IEEE Access
Open this publication in new window or tab >>A fuzzy three-dimensional house of quality to integrate and coordinate departments’ activities in organizations
Show others...
2023 (English)In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed) Published
Abstract [en]

This study aims to introduce a method to integrate and coordinate departments’ activities to enhance the service quality of organizations using Quality Function Deployment (QFD). To this purpose, the classical two-dimensional House Of Quality (HOQ) matrix is changed to a three-dimensional form (3D-HOQ). The 3D-HOQ is applied to the marketing and Human Resources (HR) departments of a bank to determine customers’ and employees’ demands, respectively. The 3D-HOQ is also employed to provide a unique list of technical requirements to satisfy the identified demands. Obtaining a unique list of technical requirements with the cooperation of both departments reduces the inconsistency between departments, saves cost and time by preventing reworks and parallel works, and increases the organization’s efficiency. Moreover, 3D-HOQ is combined with the SERVQUAL technique and fuzzy theory to determine the weight of obtained technical requirements. The study is conducted in four main steps, (1) identifying the customers’ and employees’ demands, (2) identifying the technical requirements for simultaneous satisfaction of both customers’ and employees’ demands, (3) determining the relationships between the technical requirements and the identified demands, and (4) prioritizing technical requirements. Applying the 3D-HOQ resulted in identifying 30 customers’ demands, 30 employees’ demands, and 50 technical requirements. The study results show that "using new banking technologies" has the highest weight among the customers’ demands, and "job security" has been found to have the highest weight among employees’ demands. Moreover, "Intra-organizational processes automation" has been identified as the technical requirement with the highest weight.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Customer service, Fuzzy theory, House of quality, Human resource, Quality management, SERVQUAL
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23120 (URN)10.1109/access.2023.3307358 (DOI)001058775000001 ()2-s2.0-85168755660 (Scopus ID)
Note

CC BY-NC-ND 4.0

Corresponding author: Masood Fathi (e-mail: masood.fathi@his.se).

Available from: 2023-08-23 Created: 2023-08-23 Last updated: 2023-10-10Bibliographically 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
Beheshtinia, M. A. & Fathi, M. (2023). Energy‐efficient and sustainable supply chain in the manufacturing industry. Energy Science & Engineering, 11(1), 357-382
Open this publication in new window or tab >>Energy‐efficient and sustainable supply chain in the manufacturing industry
2023 (English)In: Energy Science & Engineering, ISSN 2050-0505, Vol. 11, no 1, p. 357-382Article in journal (Refereed) Published
Abstract [en]

This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
Keywords
energy consumption, genetic algorithm, mathematical model, scheduling, supply chain, sustainability
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-22065 (URN)10.1002/ese3.1337 (DOI)000883782600001 ()2-s2.0-85143224043 (Scopus ID)
Note

CC BY 4.0

First published: 27 October 2022

Correspondence: Masood Fathi, Division of Intelligent Production Systems, University of Skövde, P.O. Box 408, SE‐541 28 Skövde, Sweden. Email: masood.fathi@his.se and fathi.masood@gmail.com

Available from: 2022-11-20 Created: 2022-11-20 Last updated: 2023-01-31Bibliographically approved
Beheshtinia, M. A., Bahrami, F., Fathi, M. & Asadi, S. (2023). Evaluating and prioritizing the healthcare waste disposal center locations using a hybrid multi-criteria decision-making method. Scientific Reports, 13(1), Article ID 15130.
Open this publication in new window or tab >>Evaluating and prioritizing the healthcare waste disposal center locations using a hybrid multi-criteria decision-making method
2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 15130Article in journal (Refereed) Published
Abstract [en]

Healthcare waste disposal center location (HCWDCL) impacts the environment and the health of living beings. Different and sometimes contradictory criteria in determining the appropriate site location for disposing of healthcare waste (HCW) complicate the decision-making process. This research presents a hybrid multi-criteria decision-making (MCDM) method, named PROMSIS, to determine the appropriate HCWDCL in a real case. The PROMSIS is the combination of two well-known MCDM methods, namely TOPSIS and PROMETHEE. Moreover, fuzzy theory is used to describe the uncertainties of the problem parameters. To provide a reliable decision on selecting the best HCWDCL, a comprehensive list of criteria is identified through a literature review and experts’ opinions obtained from the case study. In total, 40 criteria are identified and classified into five major criteria, namely economic, environmental, social, technical, and geological. The weight of the considered criteria is determined by the Analytical Hierarchy Process (AHP) method. Then, the score of the alternative HCWDCLs in each considered criterion is obtained. Finally, the candidate locations for disposing of HCWs are ranked by the proposed fuzzy PROMSIS method. The results show that the most important criteria in ranking the alternatives in the studied case are economic, environmental, and social, respectively. Moreover, the sub-criteria of operating cost, transportation cost, and pollution are identified as the most important sub-criteria, respectively.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Multi-criteria decision making, Disposal center location, Healthcare waste, Fuzzy theory
National Category
Environmental Management Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23217 (URN)10.1038/s41598-023-42455-w (DOI)001067753600011 ()2-s2.0-85171141630 (Scopus ID)
Funder
University of Skövde
Note

CC BY 4.0

email: masood.fathi@his.se

Open access funding provided by University of Skövde.

Available from: 2023-09-14 Created: 2023-09-14 Last updated: 2023-10-23Bibliographically approved
Beheshtinia, M. A., Sayadinia, S. & Fathi, M. (2023). Identifying and prioritizing marketing strategies for the building energy management systems using a hybrid fuzzy MCDM technique. Energy Science & Engineering, 11(11), 4324-4348
Open this publication in new window or tab >>Identifying and prioritizing marketing strategies for the building energy management systems using a hybrid fuzzy MCDM technique
2023 (English)In: Energy Science & Engineering, ISSN 2050-0505, Vol. 11, no 11, p. 4324-4348Article in journal (Refereed) Published
Abstract [en]

Preventing energy waste in residential and office buildings has emerged as a critical issue in both developed and developing countries over recent decades. The growing demand for oil and energy reserves has amplified the urgency of this concern. The deployment of building energy management systems (BEMSs) can lead to timely responses to changes in environmental conditions, the prevention of energy wastage, a reduction in CO2 emissions, and an increase in the longevity of building equipment. Despite the undeniable benefits of BEMSs, their market size remains small, creating challenges for providers in reaching potential customers. This research seeks to identify and prioritize the marketing strategies for BEMSs. A case study was conducted, employing the “Strengths, Weaknesses, Opportunities, and Threats” analysis as a tool for identifying marketing strategies related to BEMSs. This method resulted in the identification of 18 distinct marketing strategies. These strategies were subsequently prioritized using a novel fuzzy multicriteria decision-making technique, VIkor-topSIS, considering six specific criteria. The findings of the study suggested a hierarchical influence of six criteria on the BEMS market, arranged in the following order of significance: effectiveness, cost, attainability, complexity, timing, and popularity. Furthermore, the top three marketing strategies for BEMSs were found to be internet advertising strategies, discounts to consumers, and online sales. The analysis of the results has also offered valuable insights into the strengths and weaknesses of the studied BEMS provider, as well as the opportunities and threats present within the BEMS market.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
Keywords
building energy management system, energy saving, fuzzy theory, marketing strategy, multicriteria decision‐making
National Category
Environmental Management Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23294 (URN)10.1002/ese3.1584 (DOI)001078718600001 ()2-s2.0-85173539292 (Scopus ID)
Note

CC BY 4.0

Correspondence: Masood Fathi, Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden. Email:masood.fathi@his.se

Available from: 2023-10-06 Created: 2023-10-06 Last updated: 2023-12-13Bibliographically approved
Khamnei, H. J., Nikannia, S., Fathi, M. & Ghorbani, S. (2023). Modeling income distribution: An econophysics approach. Mathematical Biosciences and Engineering, 20(7), 13171-13181
Open this publication in new window or tab >>Modeling income distribution: An econophysics approach
2023 (English)In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 20, no 7, p. 13171-13181Article in journal (Refereed) Published
Abstract [en]

This study aims to develop appropriate models for income distribution in Iran using the econophysics approach for the 2006–2018 period. For this purpose, the three improved distributions of the Pareto, Lognormal, and Gibbs-Boltzmann distributions are analyzed with the data extracted from the target household income expansion plan of the statistical centers in Iran. The research results indicate that the income distribution in Iran does not follow the Pareto and Lognormal distributions in most of the study years but follows the generalized Gibbs-Boltzmann distribution function in all study years. According to the results, the generalized Gibbs-Boltzmann distribution also properly fits the actual data distribution and could clearly explain the income distribution in Iran. The generalized Gibbs-Boltzmann distribution also fits the actual income data better than both Pareto and Lognormal distributions

Place, publisher, year, edition, pages
AIMS Press, 2023
Keywords
econophysics, Gibbs-Boltzmann, lognormal, pareto, income distribution
National Category
Mathematics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-22673 (URN)10.3934/mbe.2023587 (DOI)001016300600003 ()2-s2.0-85162240998 (Scopus ID)
Note

CC BY 4.0

Special Issue: Data modeling using compound distributions: theory and applications

Correspondence: Email: h_jabbari@tabrizu.ac.ir

Available from: 2023-06-12 Created: 2023-06-12 Last updated: 2023-10-10Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5530-3517

Search in DiVA

Show all publications