A method to design job rotation schedules to prevent work-related musculoskeletal disorders in repetitive work

This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Production Research in 2012, available online: http://www.tandfonline.com/10.1080/00207543.2011.653452.Job rotation is an organisational strategy widely used in human-based production lines with the aim of preventing work-related musculoskeletal disorders (WMSDs). These work environments are characterised by the presence of a high repetition of movements, which is a major risk factor associated with WMSDs. This article presents a genetic algorithm to obtain rotation schedules aimed at preventing WMSDs in such environments. To do this, it combines the effectiveness of genetic algorithms optimisation with the ability to evaluate the presence of risk by repeated movements by following the OCRA ergonomic assessment method. The proposed algorithm can design solutions in which workers will switch jobs with high repeatability of movements with other less demanding jobs that support their recovery. In addition, these solutions are able to diversify the tasks performed by workers during the day, consider their disabilities and comply with restrictions arising from the work organisation.The authors wish to thank the Universitat Politecnica de Valencia which supported this research through its Program for the Support of Research and Development 2009 and its financing through the project PAID-06-09/2902.Asensio Cuesta, S.; Diego-Mas, JA.; Cremades Oliver, L.; González-Cruz, M. (2012). A method to design job rotation schedules to prevent work-related musculoskeletal disorders in repetitive work. International Journal of Production Research. 50(24):7467-7478. https://doi.org/10.1080/00207543.2011.653452S74677478502

Distributed Scheduling in Cellular Assembly for Mass Customization

Part 1: Smart FactoryInternational audienceIndustry 4.0 has many objectives; among them is increasing flexibility in manufacturing, as well as offering mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. In Mass Customization, manufacturers are challenged to produce customized products at the lowest possible cost with minimal lead-time. This increased customization increases complexity in production planning. The main challenge becomes planning production for lots of one and for high product variety and volatile market demand. Moreover, the customer requires real time update on his order status, and is less tolerant for delays. Nevertheless, in a make to order or assembly to order supply chain, many disturbances (supplier delay, machine brake-downs, transportation network disturbance, …) may highly increase the customer order delay. Hence, production planning in this context becomes more complex requiring real-time information exchange with all stages of the supply chain. This paper tries to answer this challenge by proposing a distributed production scheduling approach for mass customization in a cellular assembly layout

Robust optimization of a mathematical model to design a dynamic cell formation problem considering labor utilization

Cell formation (CF) problem is one of the most important decision problems in designing a cellular manufacturing system includes grouping machines into machine cells and parts into part families. Several factors should be considered in a cell formation problem. In this work, robust optimization of a mathematical model of a dynamic cell formation problem integrating CF, production planning and worker assignment is implemented with uncertain scenario-based data. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all possible future scenarios. In this research, miscellaneous cost parameters of the cell formation and demand fluctuations are subject to uncertainty and a mixed-integer nonlinear programming model is developed to formulate the related robust dynamic cell formation problem. The objective function seeks to minimize total costs including machine constant, machine procurement, machine relocation, machine operation, intercell and intra-cell movement, overtime, shifting labors between cells and inventory holding. Finally, a case study is carried out to display the robustness and effectiveness of the proposed model. The tradeoff between solution robustness and model robustness is also analyzed in the obtained results

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a ''data mining-based evolutionary fuzzy expert system'' (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into subpopulations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems