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Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines

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In the paper we proposed and tested on a real industrial case, related to a company in the segment of Agricultural Equipment, an approach to design asynchronous assembly lines in compliance with ergonomic legislation. We considered the OCRA index as method for ergonomic risk assessment, as it is the preferred method indicated in international norms for detailed risk assessment related to handling of low loads at high frequency. A genetic algorithm approach able to integrate the ergonomic risks evaluation and balancing/sequencing is proposed. The approach allow designing line configurations taking into account many characteristics of the complex scenario of real industrial cases: mixed models assembly lines, stochastic task times, precedence constraints among tasks, equipment and line feeding duplication costs associated to parallel workstations. Thanks to the integration of a discrete event simulator, it is also possible to consider the effect of blocking and starvation phenomena on the effective cycle time and on worker’s ergonomic load. The respect of ergonomic norms is often view by companies as an onerous obligation, being often associated to the increase of required manpower. Results show that, using the proposed approach, extra costs due to the compliance with ergonomic legislation can be very limited. This should encourage companies to adopt design methodologies able at the same time to comply with ergonomic norms and to defend their profitability.
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... The balancing and sequencing problem of mixed model assembly lines focusses on assigning worker capacities at stations considering the production sequence of multiple types of products. The purpose is to minimise work overload at stations to improve production efficiency as well as ensure in-time delivery of orders [4][5][6][7]. However, the production sequencing usually depends on assembly times determined by balancing solutions, which increases the complexity of the balancing and sequencing problem of mixed model assembly lines [8,9]. ...
... Equation (3) ensures that the average processing time at station k with W k workers is no more than cycle time C. Equation (4) ensures that all workers are allocated to the stations. Equation (5) ensures that the maximum processing time cannot exceed station length. Equation (6) ensures production sequence to satisfy the demanded of an MPS. ...
... Range of values as well as global optimisation ability enabled by the iterative interaction mechanism. Figure 10 demonstrates iterative processes of different algorithms for MPS [1,5,5,5], of which the ordinate variable S represents objective function values in Equation (1). The heuristic rule solves the balancing problem and the sequencing problem separately based on predetermined policies, without solution improvement during its iterative process. ...
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A multi‐agent iterative optimisation method based on deep reinforcement learning is proposed for the balancing and sequencing problem in mixed model assembly lines. Based on the Markov decision process model for balancing and sequencing, a balancing agent using a deep deterministic policy gradient algorithm, a sequencing agent using an Actor–Critic algorithm, as well as an iterative interaction mechanism between these agents' output solutions are designed for realising the global optimisation of mixed model assembly lines. The exchange of solution information including assembly time and station workload in the iterative interaction realises the coordination of the worker assignment policy at the balancing stage and the production arrangement policy at the sequencing stage for the minimisation of work overload and idle time at stations. Through the comparative experiments with heuristic rules, genetic algorithms, and the original deep reinforcement learning algorithm, the effectiveness of the proposed method is demonstrated and discussed for small‐scale instances as well as large‐scale ones.
... We can also observe that during the last decade the researchers were interested in the design of unpaced lines (Hudson et al., 2015;Jeong and Jeon, 2021;Lopes et al, 2018Lopes et al, , 2019bLopes et al, , 2022Öner-Közenet al., 2017;Schlüter and Ostermeier, 2022;Tiacci and Mimmi, 2018;Urban and Chiang, 2016) and specific lines for printed circuit board (Koskinen et al, 2020;Mmumtaz et al., 2020;Toth et al., 2022). ...
... Indeed, in practice the reduction of ergonomic risks can be achieved either by line balancing or job rotation(Boenzi et al., 2015;Mossa et al., 2016), or both. The consideration of physical ergonomic risks was also included in line balancing problems in the following studies:Akyol and Baykasoğlu, 2019; Battini et al., 2017; Bautista et al., 2016; Bautista-Valhondo and Alfaro-Pozo, 2018; Bortolini et al., 2017; Cheshmehgaz et al., 2012; Digiesi et al., 2018; Kara et al., 2014;Mokhtarzadeh et al., 2021;Ozdemir et al., 2021;Sgarbossa et al., 2016;Stecke and Mokhtarzadeh, 2022;Tiacci and Mimmi, 2018; Zhan et al., 2020c. Another measure frequently used for evaluating physical strain of workers is fatigue also measured by energy expenditure(Abdous et al., 2022 ;Battini et al., 2016 ; Finco et al., 2020Xu and Hall, 2021. ...
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In the research on production economics, line balancing is an intensively studied combinatorial optimisation problem. In our previous comprehensive survey on line balancing problem published in 2013 in the International Journal of Production Economics, we compared input data modelling approaches, constraints and objective functions used in more 300 studies mostly published from 2007 to 2012. Ten years after, line balancing problems still attract high attention from both academia and practice. The aim of this review is to analyse actual problem formulations that are more and more frequently hybridized with other optimisation problems such as process planning, workforce planning and/or resource scheduling in order to create efficient solutions for customized production environments adapted for volatile markets. This presentation of the state of the art is based on a review of more than 500 articles published in refereed journals between 2012 and 2022.
... In the literature, works tried to introduce ergonomics issues mainly focussing on fully manual assembly lines to mitigate the risks and reduce MSDs. Most articles in the literature consider ergonomics with a risk assessment criteria, such as the occupational repetitive actions index (OCRA) (Otto and Scholl 2011;Baykasoglu et al. 2017;Tiacci and Mimmi 2018), with OCRA being a method of evaluating the musculoskeletal load of the upper limbs. Other works consider a customised general ergonomics assessment risk, such as environment, postural, and physical load (Choi 2009;Mutlu and Özgörmüş 2012;Bautista, Alfaro-Pozo, and Batalla-García 2016). ...
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Manufacturing systems are socio-technical systems, with explicit interactions between humans and technologies in shared workspaces. These shared workspaces could also be called hybrid collaborative manufacturing systems, which involve workers as well as technological equipment and combine the benefits of human workers and new Industry 4.0 technologies, such systems are particularly useful in a context requiring flexibility and adaptability. Furthermore, the new Industry 5.0 approach has the objective to shift toward more human-centric and resilient manufacturing systems. The key problems to solve in the design of collaborative manufacturing systems are the combinatorial assembly line balancing problem and the equipment selection problem. An efficient and sustainable line requires a cost-effective choice of equipment while improving the ergonomics and the safety of workers. Both decisions of balancing workload and the assignment of equipment impact the ergonomics of a collaborative system and present conflicting criteria. To this end, we propose a multi-objective approach, the objectives are the optimisation of the investment costs and the ergonomics with a fatigue and recovery criterion. We propose to linearise the fatigue and recovery to formulate a new Mixed Integer Linear Programming formulation. We developed an exact multi-objective solving algorithm based on the ϵ-constraint to obtain the trade-off between these objectives. We conducted numerical experiments with different instances from the literature with promising results for instances with up to 45 operations. Finally, we discuss insightful managerial conclusions and future research perspectives.
... The ''bowl phenomenon'' is observed from their results. Tiacci and Mimmi [16] utilized the OCRA index (an index for measuring occupational repetitive action) to assess the ergonomic risks of workers. They introduced the effect of blocking and starvation phenomena to model the workers' ergonomic risk. ...
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