Document Type : Original Article
Faculty of Management, Islamic Azad University Central Tehran Branch, Tehran, Iran.
Balancing the production system’s resources like budget, equipment, and workers is one of the most important concerns of production managers. Managers seek to find an optimal way to balance their resources in production systems. By evaluating U-shaped assembly line papers, this investigation adds the literature on U-shaped assembly lines to the simultaneous examination of the balance ergonomic risks of human workers and current costs in the system when government offers tax benefits for using disabled workers. The mentioned outlook was not considered in previous papers. This study proposes a two-objective model to evaluate the effects of considering both robots and human workers in a U-shaped assembly line. The first objective is to minimize the system costs, and the second is to minimize the ergonomic risks. Human workers are divided into normal and disabled. The disabled workers are hired to enable tax benefits from the government. The constraint programming model for small and medium-sized problems and the grasshopper optimization algorithm (GOA) for big problems are developed to dissolve the problem. Numerical results show that two objective functions can also level system costs and ergonomic risks. The sensitivity analysis section analyzes three effective parameters (Production cycle time, Fatigue rate of human workers, and government tax benefit). It is shown that production cycle time directly affects using a robot or human workers (due to their mean time of speed), fatigue rate determines the allocation of tasks, and tax benefit helps to determine whether using disabled workers or not according objective functions. Also, it should be noticed the efficiency of GOA is shown by a comparison of several examples. Therefore, it is used for big-scale test problems.
Abolfazli, N., Eshghali, M. and Ghomi, S.F., 2022, April. Pricing and Coordination Strategy for Green Supply Chain Under Two Production Modes. In 2022 Systems and Information Engineering Design Symposium (SIEDS) (pp. 13-18). IEEE.
Abualigah, L. and Diabat, A., 2020. A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Computing and Applications, 32(19), pp.15533-15556.
Aljarah, I., Al-Zoubi, A.M., Faris, H., Hassonah, M.A., Mirjalili, S. and Saadeh, H., 2018. Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Computation, 10(3), pp.478-495.
Asensio-Cuesta, S., Diego-Mas, J.A., Cremades-Oliver, L.V. and González-Cruz, M.C., 2012. A method to design job rotation schedules to prevent work-related musculoskeletal disorders in repetitive work. International Journal of Production Research, 50(24), pp.7467-7478.
Bautista, J., Alfaro-Pozo, R. and Batalla-García, C., 2016. Maximizing comfort in Assembly Lines with temporal, spatial and ergonomic attributes. International Journal of Computational Intelligence Systems, 9(4), pp.788-799.
Behnamian, J. and Rahami, Z., 2020. Multi-objective scheduling and assembly line balancing with resource constraint and cost uncertainty: A “box” set robust optimization. Journal of Industrial Engineering and Management Studies, 7(1), pp.220-232.
Brito, M.F., Ramos, A.L., Carneiro, P. and Gonçalves, M.A., 2019. Ergonomic analysis in lean manufacturing and industry 4.0—A systematic review. Lean Engineering for Global Development, pp.95-127.
Cantos Lopes, T., Sato Michels, A., Gustavo Stall Sikora, C., Magatão, L., 2019. ''Balancing and cyclical scheduling of asynchronous mixed-model assembly lines with parallel stations'', Journal of Manufacturing Systems, Vol. 50, pp. 193-200.
Carnahan, B.J., Norman, B.A. and Redfern, M.S., 2001. Incorporating physical demand criteria into assembly line balancing. Iie Transactions, 33(10), pp.875-887.
Caterino, M., Rinaldi, M. and Fera, M., 2022. Digital ergonomics: an evaluation framework for the ergonomic risk assessment of heterogeneous workers. International Journal of Computer Integrated Manufacturing, pp.1-21.
Chutima, P. and Khotsaenlee, A., 2022. Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers. Computers & Operations Research, 143, p.105775.
Chutima, P. and Suchanun, T., 2019. Productivity improvement with parallel adjacent U-shaped assembly lines. Advances in Production Engineering & Management, 14(1), pp.51-64.
Diego-Mas, J.A., 2020. Designing cyclic job rotations to reduce the exposure to ergonomics risk factors. International journal of environmental research and public health, 17(3), p.1073.
Dong, J., Zhang, L. and Xiao, T., 2018. A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints. Journal of Intelligent Manufacturing, 29(4), pp.737-751.
Finco, S., Battini, D., Delorme, X., Persona, A. and Sgarbossa, F., 2018. Heuristic methods to consider rest allowance into assembly balancing problem. IFAC-PapersOnLine, 51(11), pp.669-674.
Hematian, M., Seyyedesfahani, M., Mahdavi, I., Mahdavi Amiri, N. and Rezaeian, J., 2020. A multi-objective optimization model for multiple project scheduling and multi-skill human resource assignment problem based on learning and forgetting effect and activities' quality level. Journal of Industrial Engineering and Management Studies, 7(2), pp.98-118.
Hochdörffer, J., Hedler, M. and Lanza, G., 2018. Staff scheduling in job rotation environments considering ergonomic aspects and preservation of qualifications. Journal of manufacturing systems, 46, pp.103-114.
Isaloo, F. and Paydar, M.M., 2020. Optimizing a robust bi-objective supply chain network considering environmental aspects: a case study in plastic injection industry. International Journal of Management Science and Engineering Management, 15(1), pp.26-38.
Khorram, M., Eghtesadifard, M. and Niroomand, S., 2022. Hybrid meta-heuristic algorithms for U-shaped assembly line balancing problem with equipment and worker allocations. Soft Computing, 26(5), pp.2241-2258.
Kucukkoc, I., Li, Z., Karaoglan, A.D. and Zhang, D.Z., 2018. Balancing of mixed-model two-sided assembly lines with underground workstations: A mathematical model and ant colony optimization algorithm. International Journal of Production Economics, 205, pp.228-243.
Kucukkoc, I. and Zhang, D.Z., 2014. Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines. International Journal of Production Economics, 158, pp.314-333.
Kucukkoc, I. and Zhang, D.Z., 2015. Balancing of parallel U-shaped assembly lines. Computers & Operations Research, 64, pp.233-244.
Li, Z., Janardhanan, M.N. and Rahman, H.F., 2021. Enhanced beam search heuristic for U-shaped assembly line balancing problems. Engineering Optimization, 53(4), pp.594-608.
Malekkhouyan, S., Aghsami, A. and Rabbani, M., 2021. An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system. International Journal of Computer Integrated Manufacturing, 34(11), pp.1237-1262.
Mgbemena, C.E., Tiwari, A., Xu, Y., Prabhu, V. and Hutabarat, W., 2020. Ergonomic evaluation on the manufacturing shop floor: A review of hardware and software technologies. CIRP Journal of Manufacturing Science and Technology, 30, pp.68-78.
Miralles, C., García-Sabater, J.P., Andrés, C. and Cardós, M., 2008. Branch and bound procedures for solving the assembly line worker assignment and balancing problem: Application to sheltered work centres for disabled. Discrete Applied Mathematics, 156(3), pp.352-367.
Mirjalili, S.Z., Mirjalili, S., Saremi, S., Faris, H. and Aljarah, I., 2018. Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), pp.805-820.
Saremi, S., Mirjalili, S. and Lewis, A., 2017. Grasshopper optimisation algorithm: theory and application. Advances in engineering software, 105, pp.30-47.
Mokhtarzadeh, M., Rabbani, M. and Manavizadeh, N., 2021. A novel two-stage framework for reducing ergonomic risks of a mixed-model parallel U-shaped assembly-line. Applied Mathematical Modelling, 93, pp.597-617.
Momeni, B., Aghsami, A. and Rabbani, M., 2019. Designing humanitarian relief supply chains by considering the reliability of route, repair groups and monitoring route. Advances in Industrial Engineering, 53(4), pp.93-126.
Moussavi, S.E., Mahdjoub, M. and Grunder, O., 2018. A multi-objective programming approach to develop an ergonomic job rotation in a manufacturing system. IFAC-PapersOnLine, 51(11), pp.850-855.
Nazri, M.N.S., Ani, M.N.C. and Azid, I.A., 2021. Productivity Improvement Through Improving the WorkStation of Manual Assembly in Production Systems. In Progress in Engineering Technology III (pp. 125-135). Springer, Cham.
Neag, P.N., Ivascu, L., Mocan, A. and Draghici, A., 2020. Ergonomic intervention combined with an occupational and organizational psychology and sociology perspectives in production systems. In MATEC Web of Conferences (Vol. 305, p. 00031). EDP Sciences.
Özcan, U., (2019). ''Balancing and scheduling tasks in parallel assembly lines with sequence-dependent setup times'', International Journal of Production Economics, Vol. 213, pp. 81-96.
Özcan, U., Gökçen, H. and Toklu, B., 2010. Balancing parallel two-sided assembly lines. International Journal of Production Research, 48(16), pp.4767-4784.
Pereira, J., Ritt, M. and Vásquez, Ó.C., 2018. A memetic algorithm for the cost-oriented robotic assembly line balancing problem. Computers & Operations Research, 99, pp.249-261.
Rabbani, M., Mousavi, Z. and Farrokhi-Asl, H., 2016. Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem. Journal of Industrial and Production Engineering, 33(7), pp.472-484.
Rathore, B., Pundir, A.K., Iqbal, R. and Gupta, R., 2022. Development of fuzzy based ergonomic-value stream mapping (E-VSM) tool: a case study in Indian glass artware industry. Production Planning & Control, pp.1-21.
Rezaei, A., Shahedi, T., Aghsami, A., Jolai, F. and Feili, H., 2021. Optimizing a bi-objective location-allocation-inventory problem in a dual-channel supply chain network with stochastic demands. RAIRO-Operations Research, 55(5), pp.3245-3279.
Samuels Group, 2021. The top 5 manufacturing challenges & potential solutions. url: https://www.samuelsgroup.net/blog/top-5-manufacturing-challenges-and-solutions
Sana, S.S., Ospina-Mateus, H., Arrieta, F.G. and Chedid, J.A., 2019. Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry. Journal of Ambient Intelligence and Humanized Computing, 10(5), pp.2063-2090.
Simonetto, M. and Sgarbossa, F., 2021, September. Straight and U-Shaped Assembly Lines in Industry 4.0 Era: Factors Influencing Their Implementation. In IFIP International Conference on Advances in Production Management Systems (pp. 414-422). Springer, Cham.
Urban, T.L. and Chiang, W.C., 2006. An optimal piecewise-linear program for the U-line balancing problem with stochastic task times. European Journal of Operational Research, 168(3), pp.771-782.
Zhang, B. and Xu, L., 2020. An improved flower pollination algorithm for solving a Type-II U-shaped assembly line balancing problem with energy consideration. Assembly Automation.
Zhang, Z., Tang, Q., Han, D. and Qian, X., 2021. An enhanced multi-objective JAYA algorithm for U-shaped assembly line balancing considering preventive maintenance scenarios. International Journal of Production Research, 59(20), pp.6146-6165.
Zhang, Y.J., Liu, L., Huang, N., Radwin, R. and Li, J., 2021. From manual operation to collaborative robot assembly: An integrated model of productivity and ergonomic performance. IEEE Robotics and Automation Letters, 6(2), pp.895-902.
Zeng, X., Hammid, A.T., Kumar, N.M., Subramaniam, U. and Almakhles, D.J., 2021. A grasshopper optimization algorithm for optimal short-term hydrothermal scheduling. Energy Reports, 7, pp.314-323.